esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The study sought to assess the different effects of the 2015 summer drought on wet and dry spruce forest ecosystems. Our findings corroborate the earlier published results by [27; 28; 29] that the year 2015 was characterized by severe drought conditions across Europe (Figure 1 and Table 4). Furthermore, it was found that the months of July-August of 2015 were the most affected by high mean Tair (>20 \({}^{\circ}\)C), high mean VPD (>10 hPa) and low mean SVWC values (<0.19 m\({}^{3}\) m\({}^{-3}\)) across both spruce forest stands. This shows that the drought effect in 2015 was severe in the months of July-August. Moreover, the results of this study also show that the rate of the forest ecosystem photosynthesis was significantly reduced (likely through the immediate closure of the stomata to protect the tree from desiccation) by the high VPD, Tair and soil water deficit during these two months in 2015. As such, there was an observed strong decline in both forest GPP and NEP across both spruce forest stands during the DY period of 2015, especially in CZ-RAJ compared to CZ-BK1 (M). This strong decline in forest GPP and NEP at CZ-RAJ was mainly due to the high atmospheric evaporative demand coupled with the low SVWC conditions experienced at this forest ecosystem, as compared to that in CZ-BK1 [30]. However, due to the humid climatic conditions at CZ-BK1, an increase in Tair during the months of July-August in 2015 only aided the increase in the kinetics of the enzymes participating in microbial decomposition and root respiration under warm conditions, thereby increasing the overall forest ecosystem respiration [65; 66; 67; 68; 69; 70; 71].  
Additionally, results from the piecewise regression analysis using the residuals from the LRC model highlight the effects of both VPD and SVWC on forest ecosystem GPP. This is because both photosynthesis and transpiration are mediated by stomatal conductance, which are affected by these environmental variables. However, a steeper decline in forest ecosystem GPP (Figure 3 and Table 7) was observed with high VPD values across both forest stands, even under non-drought years (when SVWC was non-limiting). This is consistent with recent studies, which show that high VPD values aggravate drought effects in forests, due to the abrupt changes over very short timescales within a day, even without dry soil conditions [7; 72; 73]. This further explains the strong suppression of GPP by high VPD on even non-drought years (2014 and 2016), as the SPEI (for determining dryness)only captures changes in the contributing environmental drivers (temperature and soil moisture) for longer time scales of weeks or months [74; 75]. Thus, we would recommend to include the influence of these abrupt changes in VPD on the rate of photosynthesis with SVWC limitations in future LRC models when analysing the impact of drought on GPP, especially for different forest ecosystems that are exposed to regular strong edaphic droughs [49; 62; 76; 77].
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 2 System Models  
### Network Model  
We consider a network with two transmitter-receiver pairs, as depicted in Figure 1. One pair (the upper pair in the figure) is communicating only through an RF channel(s) (RO). The other uses hybrid FSO and RF channels(HY). We investigate three different operating scenarios of the network according to the level of cooperation between RO and HY pairs: a non-cooperative reference model (Figure 0(a), referred to as REF in this paper), a cooperative model (Figure 0(b), referred to as COG in this paper), and an economic cooperative model (Figure 0(c), referred to as ECO in this paper). In the non-cooperative model that is used as a reference, both RO and HY have their own RF frequency (denoted by \(f_{1}\) and \(f_{2}\), respectively) and HY, in addition, has an optical channel \(f_{0}\). RO and HY send packets independently. HY uses RF channel \(f_{2}\) as a backup of the optical channel \(f_{0}\) (referred to as _stay-and-switching_ (SAS) in this paper). In SAS, HY uses \(f_{2}\) only when the quality of \(f_{0}\) drops below a certain level. However, we also investigate _simultaneous multipacket transmission_ (SMT) in which HY tries to transmit packets simultaneously through both \(f_{2}\) and \(f_{0}\) to enhance the throughput compared to SAS.  
In the cooperative model, both RO and HY have cognitive radio capability (CR). RO and HY can sense and borrow to temporarily use \(f_{2}\) and \(f_{1}\), respectively, if the channels are idle. In SAS, the borrowing is for backup, and sensing is needed when the assigned channel(s) fails. In SMT, on the other hand, borrowing is used to improve throughput, and sensing is performed every time to determine whether the channel is idle. In the economic cooperative model, \(f_{1}\) frequencies allocate to RO, but \(f_{2}\) frequencies do not allocate to HY due to lack of frequency resources. HY has CR and can operate in either SAS or SMT. In the cooperative models, RO and HY cannot send packets independently since the sensing can fail. Then, interference may occur by sending packets through the falsely borrowed channel.  
Each transmitter of RO and HY has a buffer of infinite capacity to store their incoming packets, respectively. Time is slotted, the transmission of each packet is assumed to take one-time slot, and the transmissions in RO and HY are assumed to be synchronized. The packet arrival processes are independent and jointly strictly stationary with mean \(\lambda_{\texttt{BO}}\) (packets per slot) and \(\lambda_{\texttt{HF}}\) for RO and HY, respectively. In addition, \(Q_{\texttt{RO}}\) and \(Q_{\texttt{HY}}\) denote their respective queue lengths.
# Numerical Performance Investigation of a Hybrid eCVT Specialized Agricultural Tractor
## 3 Hybrid eCVT Powetrain
### Hybrid eCVT Control  
#### 3.2.1 Kinematics  
In order to properly control the powertrain, kinematic relations between different components and wheels must be explored. The notation used in the equations was based on the following principles:  
* Subscripts S, C and R refer to sun, carrier, and ring gears, respectively.
* Subscripts 1 and 2 refer to first- and second-stage planetary gear, respectively. In accordance to these premises, the following equations could be obtained:  
\[i_{SR}=\frac{\omega_{S}-\omega_{C}}{\omega_{R}-\omega_{C}} \tag{8}\]  
If \(\omega_{R}=0\), it is possible to assert:  
\[i_{SC}=\frac{z_{S}+z_{R}}{z_{S}} \tag{9}\]  
Therefore,  
\[\omega_{C_{1}}=\frac{\omega_{S_{1}}}{i_{SC_{1}}}=\frac{\omega_{EM_{2}}}{1+ \frac{z_{R1}}{z_{S_{1}}}}=\omega_{R_{2}} \tag{10}\]  
Regarding the second-stage planetary gear, it is possible to write:  
\[\omega_{C_{2}}=\frac{\omega_{S_{2}}-i_{SR_{2}}\cdot\omega_{R_{2}}}{1-i_{SR_{2}}} \tag{11}\]  
Since \(\omega_{S_{2}}=\omega_{ICE}\), it is valid that:  
\[\omega_{EM_{2}}=\left[\omega_{C_{2}}*\left(1+\frac{z_{R_{2}}}{z_{S_{2}}}\right) -\omega_{ICE}\right]*\frac{z_{S_{2}}}{z_{R_{2}}}*\left(1+\frac{z_{R_{1}}}{z_{S _{1}}}\right) \tag{12}\]  
where \(\omega_{C_{2}}\) is kinematically linked to the wheels through the gearbox. Equation (12) enabled to control the EM\({}_{2}\) decoupling the rotational speed of the diesel engine from the actual vehicle speed. Figure 6 shows the values of EM\({}_{2}\) rotational speed and the second-stage carrier along the vehicle speed range of 0-40 km/h. At 15 km/h, the vehicle changed the speed range from low to high speed. Below a certain threshold speed for each regime, EM\({}_{2}\) is required to behave as a brake, while EM\({}_{1}\) can be used to recirculate power helping the ICE in this work range. Both electric machines could be used to charge the BP in the case of low SOC and low external load.
## 4 Demodulation Method  
We propose a demodulation method for determining a data symbol in a Wi-Fi reader with multiple antennas. To compare the performance along the number of antennas between the system proposed in this paper and the existing system in [12], we first transformed the ED method to suit the proposed system, and we additionally described three low complexity demodulation methods proposed to reduce the computational complexity of ED method.
## 3 Joint Sparse Sampling and Learned Acquisition for MR-PET Imaging
The discriminator architecture \(D_{\phi}\) follows a standard patch-based design [38], in which the image domain is divided into patches, and each patch is classified independently. This approach encourages the generator to produce realistic texture at both global and local scales, which is critical for perceptual quality in anatomical imaging. The entire GAN-based pipeline is trained end-to-end, with the generator, discriminator, and motion correction networks updated jointly using stochastic gradient descent. During training, extensive data augmentation is performed, including random motion perturbations, intensity variations, and elastic deformations [39], to improve robustness and generalization. Importantly, the GAN framework is fully integrated with the structured joint sparsity prior described in Section 3[27] and the optimized sparse sampling strategy from Section 3[33]. The generator loss \(\mathcal{L}_{G}\) includes the structured sparsity regularization term, ensuring that the super-resolved outputs are consistent with the shared anatomical structures across modalities. In summary, the application of GANs within the framework of super-resolution image reconstruction provides a powerful approach for overcoming the limitations of traditional voxel-wise reconstruction methods. By combining adversarial training, perceptual supervision, motion correction, and structured sparsity regularization [27], the proposed system achieves well-established performance in MR-PET super-resolution from highly sparse and noisy raw data. The resulting images exhibit enhanced anatomical detail, reduced motion artifacts, and improved perceptual quality, thereby enabling more accurate quantitative analysis and clinical interpretation of hybrid MR-PET studies.
###### Abstract  
This study numerically investigates the propagation characteristics of hazardous noxious substances (HNSs) spilled from transport ships and suggests the metal model for predicting the HNS propagation velocity varied with the current velocity and HNS density. The commercial computational fluid dynamics (CFD) code ANSYS FLUENT (V. 17.2) was used for two-dimensional simulation based on the Reynolds-averaged Navier-Stokes (RANS) equation together with the standard \(k\)-\(\epsilon\) model. The scalar transport equation was also solved to estimate the spatial and transient behaviors of HNS. The main parameters to analyze the near-field propagation characteristics of HNSs spilled from the ship were layer thickness, HNS concentration, and propagation velocity. It was found that advection becomes more dominant in propagating an HNS layer that becomes thinner as the current velocity increases. When the current velocity increased beyond a certain level (\(\sim\)0.75 m/s), the mixing effect made the HNS layer less dense but thicker. Consequently, lower-density HNS causes increased HNS concentrations at sea level. As the current velocity increased, the concentration distribution became homogeneous regardless of HNS density. In particular, the second-order response surface model provided for three variables on the basis of the numerical results for 15 cases with the use of the general least-squares regression method, showing a good fit. This model would be useful in estimating the propagation velocity of HNS spilled from a ship.  
hazardous noxious substance (HNS); computational fluid dynamics (CFD); propagation velocity; regression method; Reynolds-averaged Navier-Stokes (RANS) +
Footnote †: journal: Applied Physics Letters
## 5 Experiments and Results  
To evaluate QuEry Attack we set out to collect commonly used algorithms for comparative purposes. A somewhat disconcerting reality we then encountered involved our struggle to find good benchmarks and software for comparison purposes. Sadly, we found ourselves wasting many a day (which, alas, turned into weeks) trying to run buggy software, chasing down broken links, issuing GitHub issues, and so forth. Perhaps this is due in part to the field of adversarial attacks being young. Our experimental results are summarized in Table 1. The code is available at [[https://github.com/razla/QuEry-Attack](https://github.com/razla/QuEry-Attack)]([https://github.com/razla/QuEry-Attack](https://github.com/razla/QuEry-Attack)), accessed on 13 September 2022. We evaluated QuEry Attack by executing experiments against three different pre-trained image-classification models, taken from PyTorch [31]--Inception-v3 [32], ResNet-50 [33], and VGG-16-BN [34]--over three image datasets: ImageNet, CIFAR-10, and MNIST. We employed 200 randomly picked and correctly classified images from the test sets. For ImageNet, Inception-v3 has an accuracy of 78.8%, ResNet-50 has an accuracy of 76.1%, and VGG-16-BN has an accuracy of 73.3% (these are top-1 accuracy values; for ImageNet, top-5 accuracy values are also sometimes given, which in our case are: Inception-v3--94.4%, ResNet-50-92.8%, VGG-16-BN--91.5%). CIFAR10 accuracy values are: Inception-v3--93.7%, ResNet-50--93.6%, and VGG-16-BN--94.0%. For the MNIST dataset we trained a convolutional neural network (CNN), whose architecture is delineated in Table 2, which attained 98.9% accuracy. We chose to use the above models since they are the most commonly used CNN architectures. QuEry Attack exploits the nature of convolution layers by using square
# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 4 Simulation and Results
### Simulation Environment  
Due to the nature of the semiconductor manufacturing fab, the export of internal data is extremely limited per regulations, such as those regarding data security. Therefore, the study of this paper used a simulation of Lam Research, a semiconductor manufacturing equipment company. The simulation of Lam Research can create an environment similar to the existing facilities by using a variety of changeable parameters. Since each component can set the allowable interval for temperature, pressure, and other parameters, the simulation settings were set similarly to the environment of the facilities in the semiconductor fab. It can be said that it is functional, as shown in Figure 7, and it was possible to change the DIOs, AIOs, and characteristics according to the SW version of the facility. In addition, it was possible to change not only the process time and WAC time of the wafer process but also the transfer time of the ATM robot and VTM robot and the pump/vent time of the airlock. Therefore, it was possible to reproduce the performance of the existing facility by setting it similarly to the environment of the etch cluster facility in the semiconductor manufacturing fab. Additionally, the SECS/GEM communication environment between host and cluster equipment can be simulated. This simulation scenario can be applied to various settings of process time and WAC time by predicting the optimal JIT1 according to process time and WAC time using the ML-based KNN algorithm suggested in the previous study and applying it to the real-time etch cluster facility. This simulation environment provides a highly reliable test environment in which the throughput result is kept constant if no parameter changes. Therefore, the optimal JIT1 value is determined according to the throughput result. Various commands of factory host can be executed, and it is possible not only to collect/transmit real-time data but also to check and verify recipes.
## 2 Problem Statement
### The Components of FL Systems
#### 2.2.2 The Different Types of FL Systems  
The base concept of FL is to be able to utilize data that are shared across multiple devices. There are a few ways that these data can be distributed across devices, such as being partitioned by examples or partitioned by features [28]. The categorization of these data can be a prerequisite step for building an FL environment [24]. Some characteristics of data distribution are factors such as heterogeneous data and the clients' participation. In this subsection, a brief introduction is presented about the few FL settings that can be applied based on the distribution of data and other characteristics.  
There are a few types of FL systems based on the way the data are distributed across the environment [36]. They can be categorized as:  
* _Horizontal Federated Learning:_ This type of FL system is when the data from various devices have a similar set of features in terms of the domain but with different instances. This is the original sense of FL learning, where data from each domain are homogeneous, as depicted in Figure 3. Then, domains contribute together to train the global ML model [24]. This can be explained using the original example that is presented by Google [6], wherein the global model is an aggregate of multiple locally trained participating devices [37].  
Figure 2: A general overview of how FL works [8].  
* _Vertical Federated Learning:_ The data distributed across in a vertical FL setting are common data between unrelated domains. This could perhaps be called a feature-based learning scheme as the datasets involved in the training process perhaps share the same sample ID space but may differ in feature space. An example could be where a bank and an e-commerce business in the same city have a mutual user base, as shown in Figure 4. The bank has user-sensitive data, such as credit card ratings or revenue. At the same time, the e-commerce business has a purchase and browsing history. Here, two different domains can use their data to maybe create a prediction model based on the user and product information [29].
* _Federated Transfer Learning:_ This type of system is different from the aforementioned systems, where neither the samples nor the features have many similarities [38]. An example could be where two data sources, such as a bank in the United States and an e-commerce business in Canada, are restricted by geography but still have a small intersection with each other, being different institutions, similar to a vertical FL. However, this is just the method of partitioning the data by the ML model being similar to the traditional ML method of transfer learning, where the ML model used is a pre-trained model on a similar dataset. This method can provide better results in some cases compared to a newly built ML model [24]; this is further shown in Figure 5.  
Figure 4: Vertical FL [37].  
Figure 3: Horizontal FL [37].
## 4 Experimental Datasets and Evaluation
### Datasets
#### 4.1.2 China Rural Building UAV Imagery Dataset  
This dataset uses UAV images obtained by DJI Inspire2 with a spatial resolution of up to 0.1 m, clearly capturing building boundary features and providing delicate visual information to meet the requirements of deep learning for high-quality image samples. It covers sample images from provinces, including Shaanxi, Jiangsu and Sichuan, with extensive geographical distributions. The images cover common rural building types in China, including masonry, stone and brick, with strong representativeness [45]. The dataset contains 6060 original images and annotated samples, 4848 of which were selected for model training and 1212 for testing. An example of an input image and its corresponding label are presented in Figure 7. The dataset has been made public, and the data service system website is [[https://www.scidb.cn/en/detail?dataSetId=807518619180204032&version=V1](https://www.scidb.cn/en/detail?dataSetId=807518619180204032&version=V1)]([https://www.scidb.cn/en/detail?dataSetId=807518619180204032&version=V1](https://www.scidb.cn/en/detail?dataSetId=807518619180204032&version=V1)) (accessed on 8 May 2023).
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 3 Inversion Analysis of Initial Ground Stress
### Traditional Inversion Analysis Method
In 1999, [PERSON] et al. [29] put forward the idea of combining artificial intelligence with numerical analysis to estimate the initial geostress. In the early stage of theoretical research on initial in situ stress inversion, it was assumed that the initial in situ stress field is a uniformly distributed stress field, or a superimposed stress field formed by considering the linear distribution of gravity stress and tectonic stress. However, the engineering application of the initial stress value mainly relies on measured data and in situ stress measurement methods [30]. Therefore, it is difficult to meet the needs of engineering construction when the number of measuring points is small. As a result, researchers proposed an inversion analysis method for the initial geostress of rock mass during tunnel excavation using a small number of measuring points.
## 4 Experimental Results  
To evaluate the effectiveness of machine learning and deep learning algorithms in spam detection, twelve different models using MLP (multilayer perceptron), hybrid autoencoder, and GRU (gated recurrent unit) algorithms were developed, trained, and tested with spammy message data. This comprehensive study investigates the performance of different model architectures and data preprocessing techniques in spam detection.  
In some models, the 8440-dimensional form of email messages vectorized with CountVectorizer or TF-IDF (Term Frequency-Inverse Document Frequency) is used as input data. CountVectorizer provides a representation based on the frequencies of terms in the text data, while TF-IDF provides a weighting method that reflects the importance of terms in documents. Thanks to these vectorization techniques, the text data are converted into numerical form, enabling the models to process the text data efficiently.  
Figure 1: Proposed method.  
In other models, additional inputs are provided as nine or nineteen features derived from the message statistics. These features include textual statistics such as word count, character count, average word length, capitalization, and the presence of special characters and numbers. The model includes additional features to improve classification performance by considering the texts' structural characteristics.  
Confusion matrixes were created for all models, and metrics such as precision, recall, F1-score, and accuracy were calculated for performance evaluation. The complexity matrices allowed a detailed analysis of the models' classification performance by showing true positive, false positive, true negative, and false negative values. These metrics quantitatively demonstrated the effectiveness of the models in spam detection.  
The experimental study is analyzed under two main headings: the dataset and proposed spam detection models' results. The first section details the characteristics of the message data used, data preprocessing steps, and vectorization methods. The second section discusses the architectures of the developed models, training processes, hyperparameter tuning, and performance results. This distinction contributes to a more systematic and understandable presentation of the work.  
This comprehensive approach helps to identify the most effective combinations of models and features by exploring in depth the effects of different machine learning algorithms and data preprocessing techniques on spam detection. The findings shed light on the development of models that provide a higher accuracy and robustness in spam detection and contribute to the literature in the field.