esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Implementation Scenario  
The Kyoho table grapes (_Vitis vinifera_ L. \(\times\)_V. labrusca_ L. cv. Kyoho) (Bole, China) cold chain logistics were chosen. The quality of firmness was assessed using a handheld durometer (AGY-2, Beijing, China) by measuring force used for pressing grapes by 1 mm and expressed as Newton per meter. Ten grapes were randomly selected, and analyses were performed in triplicate.  
Figure 3 indicates the sensor nodes and coordinator deployment at the refrigeration truck in cold chain logistics. The WSN, which comprised 27 sensor nodes and a network coordinator node, was deployed in the field of a refrigeration truck for 15 days of transportation in cold chain logistic from Xingjiang to Guangdong province in China. The length, width and height of the refrigerated truck are about \(3.0\) m \(\times\) 2.5 m \(\times\) 2.4 m. Each sensor node, supplied with a 3.7-V, 1800-mAh lithium battery, was integrated into a plastic box with external antenna and put into the box of the table grapes before loading. The network coordinator, supplied with a 5.0-V, 2.0-A adaptor, was deployed at the driver's cab, and the sensor data can be transmitted in real time to the remote control center located at the company's office in Beijing via the GPRS (China Mobile Communication Co. Ltd., Beijing, China).  
The data acquisition interval of the sensor nodes is set to 1 s, and the data sending interval of the network coordinator is set to 1 min. The length of a transmitted data packet is nine bytes, which includes the sensor ID (one byte), the temperature data (four bytes) and the battery voltage (four bytes). The sensor nodes calculate in real time the weight coefficients and fuse the sensor data, while the network coordinator aggregates the temperature and relative humidity data acquired from the sensor nodes every data acquisition interval and finally transmits the fusion data to the remote control center for further analysis and processing via the GPRS every data sending interval during the cold chain logistics.  
The cold chain logistics process of table grapes and the data fusion of the temperature and relative humidity are analyzed and evaluated to improve the transparency and traceability of the temperature and  
Figure 2: Flow chart of the optimal weighted data fusion in the sensor node.  
relative humidity in the cold chain logistics and to ensure the quality and safety of the table grapes according to the implementation scenario.
## 7 Diffusion Probabilistic Models
### Denoising Diffusion Probabilistic Models  
Denoising Diffusion Probabilistic Models (DDPM) reformulate the variational bound in Equation (11) [7] as follows:  
\[L:=\mathbb{E}_{q}\Bigg{[}D_{KL}(q(\mathbf{x}_{T}|\mathbf{x}_{0})||p(\mathbf{x} _{T}))+\sum_{t>1}D_{KL}(q(\mathbf{x}_{t-1}|\mathbf{x}_{t},\mathbf{x}_{0})||p_{ \theta}(\mathbf{x}_{t-1}|\mathbf{x}_{t}))-\log p_{\theta}(\mathbf{x}_{0}| \mathbf{x}_{1})\Bigg{]} \tag{13}\]  
where the forward process variance schedule \(\beta_{t}\), which may be learned, is fixed to constants. This removes all learnable parameters from the first term of Equation (13); removing it from consideration during training. The reverse process covariance matrix \(\Sigma_{\theta}(\mathbf{x}_{t},t)=\sigma^{2}\mathbf{I}\) is also set to step dependent unlearned constants; which removes it from consideration during training also. Reparameterizing the sampling procedure in Equation (12) with \(\mathbf{x}_{t}(\mathbf{x}_{0},\boldsymbol{\epsilon})=\sqrt{\tilde{\alpha}_{t}} \mathbf{x}_{0}+\sqrt{1-\tilde{\alpha}_{t}}\boldsymbol{\epsilon}\) where \(\boldsymbol{\epsilon}\sim\mathcal{N}(\mathbf{0},\mathbf{I})\) leads to the parameterization  
\[\boldsymbol{\mu}_{\theta}(\mathbf{x}_{t},t)=\frac{1}{\alpha_{t}}\bigg{(} \mathbf{x}_{t}-\frac{\beta_{t}}{\sqrt{1-\alpha_{t}}}e_{\theta}(\mathbf{x}_{t},t )\bigg{)},\quad\sigma_{\theta}(\mathbf{x}_{t},t)=\tilde{\beta}_{t}^{1/2},\quad \text{where }\tilde{\beta}_{t}=\left\{\begin{array}{ll}\frac{1-\alpha_{t-1}}{1- \alpha_{t}}\beta_{t}&t>1\\ \beta_{1}&t=1\end{array}\right. \tag{14}\]  
where \(\epsilon_{\theta}\) is a learnable denoising function that reverses the forward process. With this parameterization, the DDPM loss function reduces to  
\[L_{DDPM}(\theta):=\mathbb{E}_{t,\mathbf{x}_{0},\boldsymbol{\epsilon}}\Big{[} \|\boldsymbol{\epsilon}-\boldsymbol{\epsilon}_{\theta}(\sqrt{\tilde{\mathbf{x}}} \mathbf{x}_{0}+\sqrt{1-\tilde{\alpha}_{t}}\boldsymbol{\epsilon},t)\|^{2} \Big{]} \tag{15}\]  
The DDPM model includes a decoder in the final step of the reverse process to improve sampling. The decoder relies on the linear scaling of data on the range \([-1,1]\) and allows the direct sampling of \(\boldsymbol{\mu}_{\theta}(\mathbf{x}_{1},1)\) without any addition of noise.
## 2 Materials and Methods
### Virtual Parametric MV Modeling  
A normal parametric MV geometry at end diastole was designed to conduct a mechanistic study of the SC insertion location as described in our previous studies [25,26]. The representative structural dimensions of the mitral annulus, the anterior leaflet (a single cusp), the posterior leaflet (three cusps), the marginal chordae tendineae, and the papillary muscle tips were utilized in the present study. Briefly, the entire MV model was designed to be symmetric with respect to the center of the mitral orifice and aligned to the anterior-posterior (AP) plane of the annulus. The mitral annulus was created by implementing four primary landmark information with the cubic spline interpolation methods. The marginal leaflet boundary was created using a combination of sinusoidal functions, and a number of three-dimensional (3D) lines linking the mitral annulus and the free leaflet margin were constructed [25,32]. The non-uniform rational B-spline (NURBs) surface model was employed to generate a 3D surface model of the MV leaflets.  
This MV leaflet model was meshed using triangular shell elements and imported into ABAQUS. The papillary muscle tips were placed 22 mm from the mid-annular plane positioning the two papillary muscle tips 24 mm apart to each other [33]. The marginal chordae tendineae were designed linking the leaflet free margin and the papillary muscle tips, and the chordal insertion was spread around the papillary muscle tips.
## 3 A-ECMS Based on Driving Pattern Recognition
### Divvability Problems  
In real-time applications, if the fuel economy is the only criterion, drivability problems may arise. Typically, in a CVT-based HEV, the unconstrained control signals in Equation (13) will lead to frequent engine on/off events and drastic fluctuations of the CVT speed ratio. An engine on/off event often involves a driving mode transition; thus, excessive engine events will result in bad drivability. Additionally, when an abrupt change in the speed ratio occurs, the dynamic characteristics of the driveline will have a negative impact on the drivability. In engineering applications, CVT is usually controlled to adjust engine operating points along the optimal operating line to ensure fuel economy. The target speed ratio is a function of vehicle speed and engine output torque. Considering the dynamic response of the hydraulic system, the rate of change of the speed ratio is usually confined to a certain range [28]. As long as there is no sudden change in driving conditions, the speed ratio will not fluctuate significantly. However, in an optimization method, the algorithm is designed to select the best control policies that minimize fuel consumption, without considering the drivability performance. So, it is possible to cause erratic control signals. Therefore, control policies that are more representative of real-world driving behaviors and ensure good drivability are needed. In this paper, penalty terms have been incorporated into the cost function to constrain the frequent engine on/off events and large variations in the rate of change of the CVT speed ratio. The Hamiltonian in Equation (13) can be reformulated as  
\[H=\dot{m}_{f}(t)+\alpha\cdot I_{EE}+\beta\cdot(\Delta i_{CVT})^{2}+s(t)\cdot \dot{m}_{f,bat,eq}(t) \tag{20}\]  
where \(\alpha\) and \(\beta\) are the weighting factors, and \(I_{EE}\) is an indicator function that equals 1 when an engine on or off event take places. The speed ratio differences are quadratic in order to keep the penalty term always positive and penalize large variations of the speed ratio. The Hamiltonian described above not only minimizes instantaneous fuel consumption but also takes the drivability into account. Note that the tuning of the weighting factors needs some trial and error depending on the desired outcomes. Based on previous research [29], the weighting factors \(\alpha=0.3\) and \(\beta=1\) were selected in this study.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.14 BLAKE  
The BLAKE design [28] emphasizes simplicity and efficiency, with the objective of reducing complexity in implementation and debugging. This design choice has the effect of reducing the potential for errors and accelerating the deployment process, thereby making it accessible even to those without expertise in cryptography. BLAKE functions (e.g., BLAKE-224/256/384/512) operate through four main processes: initialization, permutation, compression, and finalization. Each of these steps employs modular addition, XOR operations, and bit rotations to achieve efficient and secure hashing [86].  
In the initialization process, a 16-word (512/1024-bit) state matrix is initialized using inputs such as a chain value, a message block, a salt, and a counter. The initialization ensures that different inputs generate unique initial states. The state is represented as a \(4\times 4\) matrix, akin to ChaCha and AES, facilitating visualization and comprehension. In the permutation process, the input message block is divided into 16 chunks and permuted across rounds using predefined parameters. In BLAKE, the permuted values are subjected to XOR operations with 16 constants, thereby enhancing diffusion. The core component of the BLAKE system is the compression function, which employs eight G-functions to iteratively transform the internal state. These functions utilize addition, XOR, and bit rotation to update the state matrix, with variations in rotations distinguishing BLAKE-256 and BLAKE-512 from their counterparts. Upon completion of the designated rounds, the ultimate hash value is derived. The finalization process entails the combination of the internal state, chain value, and salt, resulting in the final output hash [28].
## 4 Conclusions  
This work explores the energy-harvesting performances of two tandem identical piezoelectric energy harvesters with cylinders in the water flow. The experimental results show that the performance of the upstream harvester is totally different from that of the downstream harvester. For the upstream harvester, given a specific spacing ratio, there is always an optimal water speed at which the maximum output power can be obtained. Furthermore, the maximum output power is relevant to the spacing ratio. According to the experimental results, the optimal performance (167.8 \(\upmu\)W) of the upstream harvester can be obtained at the water speed of 0.306 m/s and the spacing ratio of 2.5. Compared to the single harvester, the performance of the upstream harvester is enhanced when the water speed is around the optimal value. With the spacing ratio increasing, the enhancement is weakened or even disappears. For the downstream harvester, the performance is totally changed because of the effect of the vortex shedding from the upstream harvester. The output power increases with the water speed increasing. Compared to the single harvester, the performance of the downstream harvester is weakened in the low water speed range. However, the performance would be significantly enhanced  
Figure 10: Relationships among the output power, the water speed and the spacing ratio.  
Figure 9: Output powers of the downstream harvester and the single one as a function of the water speed for eight different spacing ratios.  
in the higher water speed. The output power of the downstream harvester (533.2 uW) is 29 times more than that of the single harvester at the water speed of 0.412 m/s and the spacing ratio of 1.7. With the spacing ratio increasing, the enhancement is weakened or even disappears as well.  
This study indicates that the energy-harvesting performances of the two tandem harvesters can be enhanced by appropriately matching the water speed and spacing ratio. It provides a basis to generate more energy by using a group of energy harvesters in the water flow.  
This work was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 2014059 and No. HIT. KISTP. 201412).  
All authors conceived and designed the experiments; [PERSON] and [PERSON] set up the experimental platform; [PERSON] and [PERSON] conducted the experiments; [PERSON] conceived and designed the experiments; all authors contributed to analyzing the experimental data and writing the paper.  
The authors declare no conflict of interest.
# On the Energy Performance of Iridium Satellite IoT Technology
###### Abstract  
Most Internet of Things (IoT) communication technologies rely on terrestrial network infrastructure. When such infrastructure is not available or does not provide sufficient coverage, satellite communication offers an alternative IoT connectivity solution. Satellite-enabled IoT devices are typically powered by a limited energy source. However, as of this writing, and to our best knowledge, the energy performance of satellite IoT technology has not been investigated. In this paper, we model and evaluate the energy performance of Iridium satellite technology for IoT devices. Our work is based on real hardware measurements. We provide average current consumption, device lifetime, and energy cost of data delivery results as a function of different parameters. Results show, among others, that an Iridium-enabled IoT device, running on a 2400 mAh battery and sending a 100-byte message every 100 min, may achieve a lifetime of 0.95 years. However, Iridium device energy performance decreases significantly with message rate.  
s 2021 217235  
results as a function of different communication protocol and link parameters. Our study shows that an Iridium-enabled IoT device, running on a 2400 mAh battery and sending a 100-byte message every 100 min, may achieve a theoretical lifetime of 0.95 years, whereas the maximum achievable lifetime for the same device (i.e., the asymptotic theoretical device lifetime as the message sending period increases) is 3.55 years. However, the device energy performance decreases significantly as the message rate increases compared to long-range IoT technologies such as LoRaWAN and Sigfox.  
The remainder of the paper is organized as follows. Section 2 reviews the related work. Section 3 overviews the Iridium satellite system, along with the communications system used by Iridium IoT devices. Section 4 provides a current consumption model of Iridium IoT satellite technology. In Section 5, we use the model derived in the previous section to evaluate the energy performance of an Iridium IoT device. Section 6 compares the energy performance of Iridium with that of LoRaWAN and Sigfox. Section 7 concludes the paper with the main remarks from this work.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 3 Proposed Model
\[f(x)=\frac{1}{1+e^{-x}} \tag{6}\]  
Generally, CNNs use convolutional layers followed by fully connected layers for classification tasks. The data from each layer are also known as sequential networks; they are transferred to the next layer. Nevertheless, as the size of the sequential network increases, a problem arises in the form of vanishing gradients. This problem may create challenges for the effective training of these kinds of networks. The GRU architecture was used in order to resolve the vanishing gradient problem. Finally, there is a classification  
Figure 3: CNN + GRU model. process in our approach. The main purpose here is to classify the messages in the dataset through binary classification. The unwanted messages, attempting phishing or information theft, are included in the spam class. The other class indicates solicited messages, posing no threat to users. The CNN + GRU algorithm is explained in detail in Algorithm 1. In this study, conducted by using deep learning algorithms, data cleaning and hyperparameter tuning were significant in terms of obtaining a high rate of accuracy. A hyperparameter consists of a set of various parameters and is used with the aim of training the neural network structure within the deep learning model. These hyperparameters are tuned in order to increase the performance of a neural network. We try to attain the optimum values of hyperparameters such as the activation function, learning coefficient, optimizers, and dropout through the fine-tuning procedure performed here [55]. The number of hidden layers was determined as 32 and the ReLU function was used in order to prevent overfitting in the embedded layers in this study. In addition to this activation function, the dropout value was determined as 0.15 in the dropout step. Finally, the sigmoid function was used in the dense layer, ensuring the split of the messages as spam or non-spam. Through the use of the CNN + GRU model in this study, we tried to prevent the issues of overfitting and long response times experienced in deep learning algorithms. The GRU algorithm was used instead of various algorithms used in other studies such as LSTM and RNN, and, by this means, fewer numbers of tensor operations were conducted and response times became shorter. The CNN algorithm, on the other hand, has a lower possibility of overfitting compared to other deep learning algorithms while maintaining the calculation power in the model. Notwithstanding, deep learning algorithms or hybrid combinations of them have been used in various studies according to the area of usage and the operation to be conducted. Figure 2.0 GRU cell architecture.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 3 Experiments
### Impact of the Weighted Loss Function  
A series of experiments are carried out to verify the scientificity of the weighted loss function. The experiment results are shown in Figure 9. The left represents the influence of the weight coefficient (\(w\)) on pixel accuracy, while the right shows a relationship between \(w\) and the testing accuracy. When setting \(w\) to 3, it can achieve the best pixel accuracy and testing accuracy.
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
## 2 Fundamental Measurement Principles
### Reconstruction Algorithm Based on Tucker Decomposition
In the temperature measurement process, when the k measuring point positions are determined, the corresponding measurement matrix can be constructed, and the temperature measurement process can be expressed as a mathematical model:  
\[\mathbf{t}_{\mathbf{M}}=\mathbf{M}\mathbf{t}_{\mathbf{x}}, \tag{14}\]  
where \(\mathbf{t}_{\mathbf{M}}\)--a vector of small number of temperature measurements, \(\mathbf{t}_{\mathbf{M}}\in\mathbf{R}^{k\times 1};\mathbf{M}\)--the measurement matrix, which characterizes the location of the measurement points and \(\mathbf{M}\in\mathbf{R}^{k\times n}\). Combining Formula (14) and Formula (15), the relationship between the measured temperature and the coefficient vector to be solved can be established:  
\[\mathbf{MA}_{\mathbf{1}\textit{unfold}}\mathbf{f}_{\mathbf{1}\mathbf{x}}^{T}=\mathbf{t}_{\mathbf{M}}, \tag{15}\]  
In Formula (16), \(\mathbf{t}_{\mathbf{M}}\) is obtained by measurement, \(\mathbf{M}\) is a known condition after the position of the measuring point is fixed, and \(\mathbf{A}_{\mathbf{1}\textit{unfold}}\) can be calculated by Tucker decomposition of the prior data set. Set the objective function of minimizing reconstruction error based on the measured data:  
\[J\left(\mathbf{f}_{\mathbf{1}\mathbf{x}}^{T}\right)=\text{arg min}\|\mathbf{t}_{\mathbf{M}}-\mathbf{ MA}_{\textit{unfold}}^{T}\mathbf{f}_{\mathbf{1}\mathbf{x}}^{T}\|^{2}, \tag{16}\]  
where \(J\left(\mathbf{f}_{\mathbf{1}\mathbf{x}}^{T}\right)\)--the objective function. Through Formula (17), the temperature distribution reconstruction is transformed into a problem of obtaining the optimal solution of the reconstruction coefficient vector:  
\[\mathbf{B}\mathbf{f}_{\mathbf{1x}}^{T}=\mathbf{k}, \tag{17}\]  
where \(\mathbf{B}=\left(\mathbf{MA}_{\mathbf{1}\textit{unfold}}^{T}\right)^{T}\left(\mathbf{MA}_{\textit {unfold}}^{T}\right)\); \(\mathbf{k}=\left(\mathbf{MA}_{\textit{unfold}}^{T}\right)^{T}\mathbf{t}_{\mathbf{M}}\). Solve the Formula (18), and then calculate the Formula (14) to obtain the three-dimensional reconstructed temperature distribution. The specific process of temperature field reconstruction by Tucker decomposition is shown in Figure 1 below.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 4 Conclusions  
In the current study, the use of saline water for greenhouse irrigation increased the salinity level of soil and in turn decreases lettuce yields. In contrast, the application of desalinated irrigation water did not negatively affect crop growth, crop yield, or soil salinity. In addition, the results from the experiment with desalinated irrigation water seemed to indicate that the additional application of essential nutrients (e.g., Ca\({}^{2+}\) and Mg\({}^{2+}\)) is recommended in order to reduce the soil sodicity risk as well as to compensate for the removal of essential nutrients by the desalination system. The response of crops treated with desalinated irrigation water may change due to environmental factors such as soil composition, weather conditions, agricultural practices including irrigation methods and the use of fertilizers, as well as the quality of desalinated water. Nevertheless, the present study suggests that irrigation with desalinated water could reduce the pressure on water resources without drastically compromising crop yields and soil quality.  
[PERSON] and [PERSON] designed the experiment, performed the experimental work and statistical analysis, produced the tables, and wrote the paper. [PERSON] and [PERSON] performed the experimental work, data collection, and chemical analyses. [PERSON], [PERSON], [PERSON], and [PERSON] all read and made improvements to the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Research Resettlement Fund for the new faculty of Seoul National University and Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through the Agri-Bio industry Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (grant number 114060-3).  
The authors declare no conflicts of interest.  
Figure 7: Effect of (**a**) saline and (**b**) desalinated irrigation water on Na+ concentration in the leaves. Bars represent the mean \(\pm\) standard deviation, \(n=5\). Different letters indicate significant differences among treatments by [PERSON]’s honest significant difference test at \(p<0.05\). BS: before water treatment using the BWD system, i.e., saline water irrigation; AS: after water treatment using the BWD system, i.e., desalinated water irrigation.