esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 2 Materials and Methods
### MODIS-MAIAC Reflectance and Spectral Vegetation Indices  
Surface reflectance and spectral vegetation indices used here were obtained from MODIS MCD19A1-C6 product at 1 km spatial resolution [51], atmospherically corrected by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm [52], normalized for Sun-sensor geometry effects using the bidirectional reflectance distribution function (BRDF) and the Ross-Thick Li-Sparse (RTLS) model parameters provided by [PERSON] et al. [52], and aggregated into monthly composites (for detailed information on this implementation please see [PERSON] et al. [53]). Spectral vegetation indices were regridded to 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) from their original resolution through the Nearest Neighbor method. The SVIs we have employed in this study were EVI [20; 21] and NDVI [19]. All MODIS-MAIAC data were obtained for the period from February 2007 to December 2017.
## 3 Results and Discussion
### Asphalt Mixture Performance Analysis  
#### 3.2.1 High-Temperature Performance  
The Marshall and rutting tests mainly evaluate asphalt mixtures' high-temperature stability. The Marshall stability, rut depth, and dynamic stability test results are shown in Figures 13 and 14.  
From Figure 13, it can be seen that at the same temperature, the Marshall stability decreases gradually with an increase in asphalt content, indicating that epoxy asphalt's resin phase content decreases, and the strength of the mixture formed becomes lower. The Marshall stability of SSEA decreases with increasing temperature. Marshall stability decreases faster from 20 \({}^{\circ}\)C to 40 \({}^{\circ}\)C and slower from 40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. When the temperature rises to 60 \({}^{\circ}\)C, the Marshall stability of BEJ-1-BEJ-5 is 9.20 kN, 7.94 kN, 7.10 kN, 6.12 kN, and 4.96 kN, respectively. Currently, the Marshall stability of SSEA is lower than that of AC mixtures of 60 \({}^{\circ}\)C stability at 14-18 kN and higher than that of BJ200 stability at 4.76 kN. This shows that the high dose of asphalt in the bridge's seamless expansion joint mixture makes the mix temperature sensitivity higher. The high-temperature performance of SSEA is lower than that of traditional AC mixtures. At the same time, the thermosetting properties of SSEA make its high-temperature performance higher than that of BJ200, which indicates that the higher Marshall stability of SSEA can meet the use requirements.  
As shown in Figure 14, the SSEA mixture has good high-temperature stability, and they all meet the provisions of the JTG F40-2004 highway used in asphalt concrete with dynamic stability higher than 800 times/mm. Comparing the epoxy asphalt with different asphalt dosages, it can be found that with an increase in asphalt content, the rutting depth gradually increases, and the dynamic stability gradually decreases. This is due to the reduced epoxy system in the epoxy asphalt; the original curing of the formation of a strong three-dimensional mesh structure is slowly converted to a weak three-dimensional mesh structure, and the resistance to high-temperature deformation performance is reduced [49]. This phenomenon is in line with the findings of [PERSON] et al. [50], who also pointed out that a higher proportion of epoxy resin and lower bitumen content help to improve the high-temperature performance of asphalt mixtures. However, as the proportion of bitumen increases, the temperature sensitivity of the material increases, leading to a decrease in its resistance to deformation. The dynamic stability of SSEA-1, SSEA-2, and SSEA-3 is higher than that of BJ200, which indicates that the high-temperature stability of these three asphalt binders exceeds that of BJ200, reflecting the superior high-temperature stability of epoxy asphalt [32; 51]. The dynamic stability of SSEA-4 and SSEA-5 is lower than that of BJ200, which suggests that the high-temperature performance of these two asphalt binders needs further improvement.
# Is Running Technique Important to Mitigate Hamstring Injuries in Football Players?
## 4 Discussion  
The current study aimed to evaluate the significance of improving running technique as a preventive measure for reducing hamstring injuries among football players. Thus, to investigate the effects of a six-week running technique intervention program, a 3D model was employed to check the eventual linear and angular kinematic changes due to the implementation of the running technique exercises. Although the existing literature on the topic is scarce, data suggest an association between running technique and hamstring injuries in football [12; 18; 33]. The effectiveness of the running technique intervention became evident through an increase in stride length and flight time, as well as a decrease in ground contact time, enhancing running efficiency in sprint cycle for the experimental group. A trend towards a reduction in the step length difference between the left and right lower limbs was observed, decreasing from 0.04 to 0.03 m from pre- to post-intervention. Further analysis of segment and joint angles at touchdown provided additional insights into the impact of the intervention protocol on sprint mechanics. A reduction in hip flexion was observed, causing the lower limb to make contact with the ground at a higher angular velocity, as noted by [PERSON] et al. [49]. This increased velocity may lead to heightened excitation and eccentric contraction of the hamstring to decelerate the shank during knee extension, potentially contributing to a greater risk of hamstring injuries [18; 50; 51]. On the other hand, this observed reduction in hip flexion may also contribute to a decreased in the number of hamstring injuries due to the diminished braking impulse [16; 19]. By bringing the foot closer to the body's center of mass at touchdown, players reduce the ground contact time and require less propulsive impulse, or \"pawing back\", generated by the hamstrings to maintain the same horizontal velocity.
## 3 Major Agriculture Applications and Services
### Fertilizer  
Plant growth, development, and reproduction mainly depend on the necessary nutrients received from fertilizers or organic amendments. For the development of flower, fruit, and root, three significant nutrients are needed: for leaf growth, nitrogen is necessary; for root development, phosphorus and the expansion of stem and water passage in pant potassium is required [36]. Plant health can be affected by the imbalance application of these fertilizers to the plant. On the other hand, the excessive application of these nutrients and fertilizers to the plant not only affects plant health and causes economic losses but also affects the environment, including qualities of soil, water, and air. For example, only half of the total nitrogen is utilized for crop development, and another half of nitrogen is released into the environment. The imbalanced application of undesired nutrients to the crops causes a negative impact on the environment and climate. It also increases the nutrient level in the soil [46]. New technology such as the IoT is beneficial for the exact estimation of the rate of fertilizers in smart agriculture. These approaches also reduce the negative effect on environmental conditions.  
New technology is used to estimate the spatial-temporal application of nutrients for the fulfillment of fertilization, which is relatively less laborious and has maximum efficiency [47]. Generally, the normalized vegetation index (NDVI) is used to monitor vegetation health, which ultimately helps to predict the application of nutrients in the soil. The efficacy of nutrient application can be enhanced by such efficient methods, which ultimately reduces the other effect on the environment. Many recent enabling technologies such as geographic information systems (GIS) [48], variable rate technology [49], and global positioning systems (GPS) [50; 51] are used to estimate nutrients in the field [52; 53; 54]. For the betterment of crops, it was observed that fertilizers are the best source for improving crop productions, and they can be triggered by only the use of new tools and technology known as the IoT [55].
## 5 Proposed Scheme
### Formulation of Signal-to-Interference-Plus-Noise Ratio (SINR)
\[\delta_{i} =\frac{\left|h_{k,b}\right|^{2}}{\left|h_{k,b}\right|^{2}}.\frac{ \left|h_{k,i}\right|^{2}}{\left|h_{l,b}\right|^{2}} \tag{14}\] \[\gamma_{j}=\ \frac{P_{i}\cdot l_{i,B}^{-\alpha}\cdot\left|h_{i,B}\right|^{2}}{P _{k}\cdot l_{k,j}^{-\alpha}\cdot\left|h_{k,j}\right|^{2}+I_{i^{\prime}}+\ \sum_{ne\
eq A1}I_{j,ne}+\ P_{N}} \tag{6}\]0  
where \(\delta_{i}\) and \(\delta_{i^{\prime}}\) are the factors that determine the channel gain for two D2D pairs. When the value of \(\delta_{i}\) and \(\delta_{i^{\prime}}\) are high, the system can achieve highest throughput after integrating D2D communication into conventional cellular network.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 4 Methodology
### Deep Reinforcement Learning Based on Double DQN
\[y=Q(s_{t},a_{t};\theta_{t}) \tag{16}\]  
\[a^{*}=argmax_{a\in\mathbb{A}}Q(s_{t+1},a;\theta_{t}) \tag{17}\]  
Neural network parameters are iterated by the temporal difference method. Through forward propagation of the neural network, target network calculates the value of the agent's selection of action \(a^{*}\) under the state \(s_{t+1}\), and obtains the target and error of the temporal difference iteration method. \[\hat{y^{{}^{\prime}}}=R_{t}+\gamma Q(s_{t+1},a^{*};\theta^{\prime}_{t}) \tag{18}\]  
\[td_{error}=\hat{y^{{}^{\prime}}}-y \tag{19}\]  
The loss function is defined as Equation (20), and the neural network is backpropagated to obtain gradient \(\
abla_{\theta}Q(s_{t},a_{t};\theta_{t})\). \[L(\theta)=\frac{1}{2}\left[\hat{y^{{}^{\prime}}}-y\right]^{2} \tag{20}\]  
At this point, the parameters of policy network are updated by gradient descent, and the parameters of target network are updated by weighted average. \[\theta_{t+1}\leftarrow\theta_{t}-\alpha\cdot td_{error}\cdot\
abla_{\theta}Q (s_{t},a_{t};\theta_{t}) \tag{21}\]  
\[Q^{*}(s_{t},a_{t})=argmax_{\pi}Q^{\pi}(s_{t},a_{t}) \tag{14}\]0  
where \(\tau\in(0,1)\) is a hyperparameter. After the agent starts to learn, every time the state changes, the parameters of the neural network will be updated until the agent reaches the final state. Figure 7 shows the double DQN structure. The policy network parameter parameters are updated with the step size, and the target network parameters are updated every N step, copying the policy network parameters at that time and reducing the model complexity. Section 4.4 shows the detailed CNN structure used in this study. The structure consists of two convolutional layers, two pooling layers, two fully connected layers and, finally, a softmax function to output the result, following the model step update. The Double DQN algorithm alleviates the overestimation problem of DQN by changing the calculation method of the target value [36]. The underlying principle is that using another function to choose the action that cannot reach the level of the original function max, can effectively limit the growth of overestimation. Different networks perform different things and the effect may be improved.
###### Abstract  
Lithium-ion batteries, with their high energy density, long cycle life, and non-polluting advantages, are widely used in energy storage stations. Connecting lithium batteries in series to form a battery pack can achieve the required capacity and voltage. However, as the batteries are used for extended periods, some individual cells in the battery pack may experience abnormal failures, affecting the performance and safety of the battery pack. At the same time, as batteries operate in complex environments, the data collected by sensors are susceptible to random noise and drift interference, which can affect the accuracy of anomaly detection in individual battery cells. In order to solve this problem, this article proposes an anomaly detection method for battery cells based on Robust Principal Component Analysis (RPCA), taking the historical operation and maintenance data of a large-scale battery pack from an energy storage station as the research subject. Firstly, theRPCA is used to denoise the observed voltage data of the battery cells to an extreme degree, obtaining a baseline charging state curve for a cell consistency assessment. This also solves the problem of sensor outputs being affected by random noise. To further detect and identify abnormal battery cells, the RPCA is used to extract outlier components. Based on the Average Deviation-3\(\sigma\) principle and by utilizing Gaussian distribution probability characteristics, battery cells are conducted to screen, and the serial numbers of the anomaly cells are obtained. Finally, the effectiveness and accuracy of this anomaly detection method for battery cells are compared and verified through different statistical distributions.  
lithium-ion battery; robust principal component analysis; anomaly detection method; energy storage station +
Footnote †: journal: Applied Sciences  
1
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 3 Classification of IR Radiation Intensity Sequence Based on IRRNN
### Structure of RRNN
where, I, H, and O are the number of nodes of the input layer, the hidden layer, and the output layer respectively; \(\mathbf{a}_{l}\) and \(\mathbf{h}_{l}\) respectively represent the input and output of the hidden layer at time step t; and \(\mathbf{b}_{l}\) and \(\mathbf{y}_{l}\) represent the input and output of the output layer at time step t, respectively. \(\mathbf{W}^{\text{HI}}\), \(\mathbf{W}^{\text{HH}}\), and \(\mathbf{W}^{\text{OH}}\) are the weight matrix between the network layers respectively; \(\mathbf{B}^{\text{H}}\) and \(\mathbf{B}^{\text{O}}\) are the offset parameters of the hidden layer and the output layer; \(\mathbf{W}_{l}\) is the random weight matrix; and \(f_{h}(\cdot)\) and \(f_{o}(\cdot)\) are the activation function of the hidden layer and the output layer, respectively. Since the network structure is similar to the traditional RNNs algorithm, the RRNN training uses the gradient descent with momentum optimization method. We choose the random weighted matrix to process the historical output information. The basis and advantages are:  
(1) Make full use of the historical output information \(\mathbf{y}_{1}\), \(\mathbf{y}_{2}\),..., \(\mathbf{y}_{t-1}\) before time step t, and the random weighted history information \(\sum_{i=1}^{t-1}\mathbf{W}_{i}\mathbf{y}_{i}\) is approximately irrelevant to the input data \(\mathbf{x}_{t}\) at time t analysis. (2) The parameters in the randomly generated weight matrix \(\mathbf{W}\) can reduce the over-fitting effect, which is similar to the increase of random noise for the input data to improve the generalization of the classification network [28]. (3) The randomly generated weight matrix \(\mathbf{W}\) does not need to be obtained through learning, omitting the complicated steps of calculating the gradient of \(\mathbf{W}\) and back-passing. Compared with the traditional RNNs, the training difficulty is not increased, and the classification performance is improved.
## 2 Materials and Methods
### Relative Fluorescence Quantum Yield  
Fluorescence quantum yield (\(\Phi_{f}\)) measurements were conducted similar to the procedure described above for singlet oxygen measurements. ZnPcBCH\({}_{3}\) and ZnPc solutions were prepared in DMF within 0.09 to 0.1 absorbance units at 608 nm. The area of the fluorescence emission band and the recorded absorption spectra were used to calculate the fluorescence quantum yield using Equation (2). The conditions for the sample and standard were the same and this measurement was repeated twice on different days. ZnPc in DMF was used as a standard for the measurement (\(\Phi_{f}\) = 0.28) [21]:  
\[\Phi_{f}=\Phi_{f}(std)\frac{A_{obs(x)\cdot OD(std)}}{A_{obs}(std)\cdot OD(x)}, \tag{2}\]  
where \(A(x)\) and \(A(std)\) are areas under the fluorescence emission curves of the samples and the standard, respectively. \(OD(x)\) and \(OD(std)\) are the optical density of the sample and standard, respectively.
## 4 Simulation Results and Discussion
Therefore, to achieve more than 70% accuracy, we need to change the size of the training data set, as depicted in Figure 5.  
Similarly, the effect of the number of components per task on accuracy is shown in Figure 6. As the number of components per task increases, the possible number of offloading policies (which is \(2^{n}\)) increases. Therefore, the chances of selecting the offloading policy with minimum cost decreases and the performance of TOT and ROT decreases. While for the other techniques using deep learning-based approaches, the complexity between constant input and output data increases, the accuracy of CEDOT, EEDOT, and DOT decreases as the number of components per task increases. However, the performance of the proposed work is higher than all the other techniques and is almost equal to EEDOT with the advantage of low energy consumption and time delay for computational offloading and partitioning. Figure 4: Cost for different task sizes. Figure 5: Training dataset size for different number of components with more than 70% accuracy. Figure 7 shows the accuracy of the trained DNN along with varying size of training dataset. We can observe that the accuracy increases as the size of training dataset increases because large datasets means more data which provides more information to the DNN. The performance of CEDOT and EEDOT is better and almost equal, however, the proposed technique solves the computational offloading and partitioning jointly while EEDOT ignores the partitioning of the task. Figure 8 shows the accuracies of different techniques along with varying distance between MES and UE. CEDOT, EEDOT, and DOT have almost constant accuracy for different distances which means the accuracy of the DNN is not affected by the mobility of UEs. TOT has a decreasing behavior as the distance increases between MES and UE. It is because at lower distances the chances of offloading all components to MES are high since the transmission and reception cost is minimum. However, the accuracies of TOT and ROT are lower as compared to the other techniques because there is no learning mechanism used in TOT and ROT. Figure 6: DNN accuracy for different number of components per task. Figure 7: Comparison of the DNN accuracy for different sizes of training dataset.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 3 System Model
### Scenario  
Usually, the IIoT scenario mainly locates indoors. In the indoor space, the clutter is distributed. The clutter could be big machinery composed of regular metallic surfaces. For example, several mixed production areas with open spaces and storage/commissioning areas or small to medium metallic machinery and objects with irregular structures. For example, assembly and production lines are surrounded by mixed small-sized machinery.  
As for the base station (BS) layout, we refer to the typical deployment in 3 GPP as shown in Figure 1. In this case, 18 BSs are distributed within a square lattice with spacing D, located D/2 from the walls. Here, we consider the small hall with L = 120 m, W = 60 m, and D = 20 m. Positioning devices are uniformly distributed over the convex hull of the horizontal BS deployment.  
The positioning procedure is a certain asymmetric procedure as shown in Figure 2. Multiple BS would transmit positioning reference signals (PRS) to the devices. On the device side, the positioning device would derive its coordinates according to the measurement of PRS from multiple BSs based on positioning algorithms.  
Figure 1: Typical BS layout.
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 3 System under Consideration
### Msc  
Since the generator output voltage and frequency do not match those of the grid, the AC/DC conversion unit at the machine side, transforms generator output voltage and current to DC values, thus becoming frequency independent. This conversion stage features a three-phase diode rectifier that converts generator output voltage to a DC voltage, followed by a boost chopper which normally achieves MPPT.  
The mean value of the rectifier output voltage \(\overline{V_{r}}\) is given by (7):  
\[\overline{V_{r}}=\frac{1}{\frac{\pi}{3}}\int\limits_{\frac{\pi}{6}}^{\frac{\pi }{2}}\sqrt{2}\;V_{LL}sin\bigg{(}\omega_{s}t+\frac{\pi}{6}\bigg{)}d(\omega_{s}t )=\frac{3\sqrt{2}}{\pi}V_{LL}\approx 1.35\;V_{LL} \tag{7}\]  
where \(V_{LL}\) is the rms value of the line-to-line input voltage and \(\omega_{s}\) is the input voltage angular frequency.  
The rectified voltage \(V_{r}\) is boosted to the DC-link voltage \((V_{dc})\) via the boost converter of transfer function given by (8) assuming continuous inductor current.  
\[\frac{V_{dc}}{V_{r}}=\frac{1}{1-d} \tag{8}\]  
where \(d\) is the duty cycle, defined as (9):  
\[d=\frac{t_{on}}{T} \tag{9}\]  
where \(t_{on}\) is the switch ON time, \(T\) is the switching period and \(t_{off}\) is the switch OFF time.  
The inductor ripple current \(\Delta i_{b}\) of the boost converter can be expressed by (10), while the converter peak-to-peak output voltage ripple \(\Delta v_{dc}\) is given by (11):  
\[\Delta i_{b}=d\;(1-d)\frac{V_{dc}\;T}{L_{dc}} \tag{10}\]  
\[\Delta v_{dc}=\frac{d\;T\;V_{dc}}{R_{Load}C_{dc}} \tag{11}\]  
where \(L_{dc}\), \(C_{dc}\) and \(R_{Load}\) are the boost chopper inductor, output capacitor and equivalent load resistance, respectively.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 3 Model Construction  
As previously mentioned, we utilize two distinct datasets with varying sampling frequencies: (1) stock and sentiment data sampled at a daily frequency, and (2) single stock data sampled at 15-min or 30-min intervals. Due to the inherent misalignment in sampling frequencies, these datasets cannot be directly input into the same network. To address this, we leverage the strengths of different models: LSTM and GRU are employed to process the low-frequency sentiment and stock data, while Bi-LSTM and CNN-GRU are used to handle the high-frequency stock data. We adopt an ensemble learning-like approach to integrate the outputs of these models for the final prediction. The overall architecture is illustrated in Figure 1.