esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Machine Translation Approaches
### Statistical Machine Translation  
Until recently, SMT had been predominantly used in MT research and industry MT systems, such as Google Translate, given its more precise translation results compared to RBMT [7]. Because meaning inherently stems from the way words are used together, using a statistical approach that can detect regularities in text and context of use increases the quality of translation significantly [7]. The SMT approach analyzes very large amounts of linguistic data taken from previous translations and assesses their statistical probability of being the most appropriate translation. The data used are typically in the form of a corpus containing source and target sentences in pairs [1], referred to as bilingual corpora. The analysis of the statistical probability of the data first occurs in the translation model, in which the probability of the correspondence between sentences in the two languages is estimated. Adjacent to the translation model, the system learns to calculate the probability of word sequences being valid in what is called the target-language model [1]. The model must also consider the extent to which each language differs in syntax, so a distortion model is learned [1]. This model takes into account the probability that a word placed in a specific position within the source sentence will move to a different position in the target sentence [1]. For SMT to be able to translate text, a crucial third component must be used--the decoder. A decoder takes the source sentence and determines the different probabilities of each word within the translation model, which then outputs possible words and phrases. The output is used as an input within the target-language model, which then calculates and outputs the most likely translation according to complex mathematical and statistical methods [1]. SMT enhances the RBMT method, but its performance is hindered when we consider the fact that words between languages do not have a one-to-one correspondence.  
A crucial part of statistically based MT systems is the phrase transition model [8], which depends on either the conditional probability of generating a target sentence given the source sentence, or the joint probability of the concurrence of the source and target sentences [8]. The nature of such models makes them susceptible to viewing phrases that are paraphrased or different on the surface as distinct units. This prohibits the distinct units, though they may share linguistic properties, from sharing parameters of the model while translating, which leads to the problem of sparsity that affects morphologically rich languages [8], including the Semitic languages Hebrew and Arabic [9]. Because of these shortcomings in SMT, the need for novel algorithms that utilize machine learning became more pressing. NMT provides an alternative that aims to tackle these challenges, and hence it has risen in popularity recently.
## 3 Implementation Details
### Test Design  
The experimental design for this study was chosen to demonstrate the fundamental viability of our proposed ultrasound-based localization system. Our experiments focused on three key aspects:  
1. Static accuracy: Single-point and two-point measurements were conducted to establish the system's baseline accuracy and its ability to distinguish between different positions.
2. Dynamic tracking: A circular motion experiment was performed to evaluate the system's performance in tracking a moving target.
3. Time difference precision: Measurements with varying distances between emitters were taken to evaluate the system's general precision and sensitivity.  
While these tests are limited in scope, they serve as a proof of concept, providing concrete evidence of the system's functionality and potential.
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 4 LDPC Decoding Algorithm
### Log-Likelihood Ratio-Belief Propagation (LLR-BP) Algorithm
2 Updating the variable nodes: For each \(m\in M(n)\) we calculate:  
\[\lambda_{mn}^{(k)}=\lambda_{n}^{(0)}+\underset{m^{\prime}\in M(n)\backslash m }{\Sigma}\Lambda_{mn^{\prime}}^{n} \tag{30}\]  
The posterior LLR of each variable node is:  
\[\lambda_{n}^{(k)}=\lambda_{n}^{(0)}+\underset{m\in M(n)}{\Sigma}\Lambda_{mn}^ {(k)} \tag{31}\]  
4 Decoding decision:  
\[\hat{\mathbf{x}}_{n}^{(k)}= \begin{cases}0&\lambda_{n}^{(k)}\geq 0\\ 1&\lambda_{n}^{(k)}<0\end{cases}  \tag{32}\]  
According to the check formula, the corrector of the CW \(\hat{\mathbf{x}}^{(k)}\) obtained through decoding is:  
\[S=H\cdot\left(\hat{\mathbf{x}}^{(k)}\right)^{\mathrm{T}} \tag{33}\]  
If \(S=0\), the decoding has succeeded and the process ends by outputting the decoded CW \(\hat{\mathbf{x}}^{(k)}\) as an effective value. If \(S\
eq 0\), steps 2, 3, and 4 are repeated until the preset maximum number of iterations is reached. For the LLR-BP algorithm, the nonlinear function \(\Phi(x)\) is implemented through a lookup table during the hardware implementation process. The quantification of \(\Phi(x)\) directly impacts the decoder's functional implementation and resource consumption.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 2 Materials and Methods
### Data Processing Methodology
#### 2.3.3 Soil Profile Characteristics  
The AquaCrop model incorporates essential physical soil parameters to calculate water content in the root zone (Wr) and simulate soil water flow. This involves a comprehensive  
Figure 3: Daily values of atmospheric precipitation and ETo for the years 2018–2023.  
soil water balance that monitors daily water transfers, including inputs such as rainfall, irrigation, and capillary rise, and outputs such as percolation and evapotranspiration [25]. AquaCrop's soil profile consists of various soil horizons, each characterized by distinct physical properties [26]. When local measurements of soil characteristics are unavailable, AquaCrop provides default values for different Soil Texture Classes [23]. To ensure consistent model initialization, specific Soil Texture Classes for different crops were used, as presented in Table 1. For example, winter wheat was associated with a clay loam Soil Texture Class. Corresponding values for Soil Water Content (\(\theta\)), Total Available Water (TAW), and Saturated Hydraulic Conductivity (Ksat) were derived from AquaCrop's default values for this Soil Texture Class. This practice ensures that the model starts with similar initial soil moisture conditions across various simulations, enabling a more controlled assessment of model performance or the impact of different factors on soil moisture during the simulation period.
## 2 Materials and Methods
### Performance Metrics  
To evaluate the performance of machine learning models in predicting maternal health risks, we utilize four key metrics: accuracy, precision, recall, and F1 score. Accuracy measures the proportion of correctly classified cases among all cases, providing an overall assessment of model performance. Precision evaluates the proportion of correctly identified positive cases among all predicted positives, reflecting the model's ability to minimize false positives. Recall, also known as sensitivity, assesses the proportion of actual positive cases correctly identified by the model, emphasizing the detection of true positives--a crucial metric in maternal health where identifying high-risk cases is critical. The F1 score, a harmonic means of precision and recall, balances these two metrics to provide a single measure that accounts for false positives and false negatives. Using a combination of these metrics offers a comprehensive evaluation framework, enabling the comparison of models based on their ability to accurately, reliably, and effectively identify maternal health risks in resource-limited settings.  
\[Accuracy=\frac{\text{True\, Positives}\;(TP)+\text{True\, Negatives}\;(TN)}{\text{Total\, Number\, of\, Samples}}\]  
Figure 3: Stacking ensemble model for predicting maternal health risk.  
\[Precision=\frac{\text{\emph{True Positives }}(TP)}{\text{\emph{True Positives }}(TP)+\text{\emph{False Positives }}(FP)}\]  
\[Recall=\frac{\text{\emph{True Positives }}(TP)}{\text{\emph{True Positives }}(TP)+\text{\emph{False Negatives }}(FN)}\]  
\[F1=2\frac{\text{\emph{Precision }}*\text{\emph{Recall}}}{\text{\emph{ Precision }}+\text{\emph{Recall}}}\]  
After computing performance metrics, we employed stratified 10-fold cross-validation to compare the models. This method allowed us to preserve class distributions in each fold, which is critical, given the class imbalances in the datasets. To further assess whether differences in the model performance were statistically significant, we performed an ANOVA test. In the case of significant findings, we performed a post hoc test to identify specific differences between different machine learning models.
# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 2 Related Work
### Productivity Parameters of Cluster Tools  
JIT1 is one of the scheduling variables of cluster facilities, referring to the time when the wafer moves from the FOUP. For example, if JIT1 is N, the scheduler starts moving the wafer from the FOUP to airlock N seconds before. Depending on this time point, the total moving time of the wafer may change. The PM is chamber space in which the semiconductor wafer recipe operates as a process module. Process time is the processing time of wafers. WAC time is an abbreviation for waferless auto-clean, referring to the cleaning time of the chamber space for the following wafer process.  
Wafers stored in the FOUP are in the initial status, and the OHT of the semiconductor fab transports the FOUP and moves it to the facility's load port. After wafer mapping on the loadport, the ATM arm returns it from the loadport to the AL, and the transfer chamber arm selects one of the processable PMs from the AL and returns the wafer [17]. The wafer arriving at the PM proceeds with the pre-programmed recipe process. When the wafer's recipe process is completed, again through the transfer chamber arm, it moves from PM to AL. Then, the ATM arm transfers it from the AL to FOUP, and the OHT retrieves the FOUP. After completing the process, the PM proceeds with the WAC process, a chamber-cleaning process to maintain the quality required for the following process in the recipe [18]. Suppose the cluster facility uses the clean recipe; in this case, the PM must proceed with removing residual chemicals and impurities formed inside the PM for the subsequent wafer processes, a process called WAC time. After this WAC time, subsequent wafers can move to PM. According to the recipe time, the schedule of the cluster facility, the robot's transmission time, and the availability of the PM and AL are determined. The above OHT return and wafer map verification process download and upload the recipe. These are carried out through communication based on the SECS/GEM communication protocol with the host of the semiconductor fab.
## 3 Experimental Testbed & Evaluation Methodology
### Evaluation Methodology
latency(3), latency(9) is the time to transport data on the fronthaul network and is given by:  
\[\text{latency(3,9)}=\sum_{i=1}^{\text{N}_{\text{link}}}\left(\text{t}_{\text{ tran(j)}}+\text{t}_{\text{prop(i)}}\right)+\sum_{i=1}^{\text{N}_{\text{ switch}}}\left(\text{t}_{\text{proc(i)}}+\text{t}_{\text{que(i)}}\right), \tag{2}\]  
where \(\text{t}_{\text{ran}}\) is the transmission time, that is, the time taken to transmit a packet over a fronthaul link and is defined as the packet size over the bit rate of the fronthaul link, \(\text{t}_{\text{prop}}\) is the propagation time, that is, the time taken for a packet to propagate between two nodes. The propagation time is calculated by the distance over propagation speed in the fronthaul. N\({}_{\text{link}}\) is the number of fronthaul segments between the (CU + DU) entity and the RU entity, t\({}_{\text{proc}}\) is the amount of time the switch requires to process a packet for routing (encapsulation/decapsulation and table lookup) and t\({}_{\text{que}}\) is the queueing delay in a switch when there is a contention at its ports. Figure 4 shows that 4 fronthaul paths are input to the switch, all of which must be directed to the same destination, that is, RU. Therefore, packets may encounter queueing delay at the switch. Therefore, t\({}_{\text{que}}\geq 0\) and there is one switch in the experimental setup, hence N\({}_{\text{switch}}\) = 1 in Equation (2). latency(4), latency(10) time taken for network driver to write packet descriptor in the queue. This latency is subject to a queueing delay when multiple data are sent over the Ethernet sockets. Figure 4: Switch internal architecture. Figure 3: Representation of the round trip time measurement. latency(5), latency(11) is the time that the fronthaul interface takes to read data from the network driver queue and depacketize data by removing the Ethernet header from the packet. latency(6) is the protocol stack processing time of the request at the (CU + DU) entity to prepare data. This latency depends on the processing capability of the central processing unit. The average latency of RTT is computed as:  
\[\text{RTT}_{\text{aver}}=\frac{\sum_{i=1}^{N}\text{latency(RTT)}_{\text{i}}}{N}, \tag{3}\]  
where \(N\) is the number of RTT samples collected during the execution of the experimental test.
## 3 Feature Selection Algorithms  
In the field of SHM, several signal processing techniques can be used to extract damage-sensitive features and detect, locate and estimate structural damage in the machines and structures of interest. Some of the techniques used in previous works in this field include ANFIS [8; 9], wavelet-based techniques [10], frequency-response functions (FRF) [11], Kalman filter-based techniques [12] and soft computing-based techniques [13].  
The selection of damage-sensitive features by manual means represents a problem, as it requires expertise to identify candidate damage-sensitive signals. Algorithms for producing these features have been explored in the literature and two approaches to the generation of these features are widely proposed. One common approach is to propose novel features based on signal processing techniques. Another is to let a machine model the task of automatically extracting some sensitive features from data hidden on the surface. Composite damage indicators can also be constructed from the combination of damage-sensitive features. For example, [PERSON] et al. [14] used residual drift ratios and lateral stiffness to define damage levels and quantify the amount of damage to structures due to seismic events.  
A comprehensive review of some damage-sensitive feature construction algorithms can be found in the review of [PERSON] and [PERSON] [15]. Among the most recent techniques applied to the problem of extracting useful information from data for structural damage identification are principal component analysis (PCA), the wavelet transform (WT), along with its variants, and the application of empirical modal decomposition (EMD) in combination with the Hilbert-Huang transform (HHT).
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 4 Simulation
### Dataset  
We evaluated the proposed improved YOLO5 model on three aerial remote sensing image datasets: NWPU VHR-10 [23], UCAS-AOD-CAR [24], and RSOD [22].  
The NWPU VHR-10 dataset is a geographic remote sensing dataset for spatial object detection. It contains 650 images containing objects and 150 background images, a total of 10 categories of objects, which were taken from Google Earth. In the process of data processing, we uniformly adjust each image to a size of 640 \(\times\) 640 pixels and divide the image into training set, validation set and test set with a ratio of 7:2:1. Among them, there are 560 images in the training set, 160 images in the verification set and 80 images in the test set.  
The RSOD dataset contains 976 images, four categories, and 6950 labeled objects. It has a uniform size of 1000 \(\times\) 1000 pixel. Although the RSOD dataset is widely used in object detection tasks, the number of objects in different categories is not balanced. The further to enrich the content of the dataset, data augmentation is performed based on the original dataset; the original image is cut, flipped, and pixel-filled under the premise of keeping the image aspect ratio unchanged. In the end, the size of the processed dataset is increased from 976 images to 1500 images.  
The UCAS-AOD dataset is an aerial remote sensing image dataset for vehicle and aircraft detection. It contains 300 vehicle images and 600 aircraft images, which were taken from Google Earth in 2014. All images in the dataset are 1280 \(\times\) 659 pixels in size and evenly distributed in orientation. To verify the detection effect of the improved YOLOv5 framework on dense tiny objects, we selected vehicle datasets with denser distribution in the dataset for simulation experiments. We uniformly adjust each image to a size of 640 \(\times\) 640 pixels and randomly assigned the image into training set, validation set and test set with a ratio of 7:2:1. After image processing, the number of training sets, test sets and validation sets is 210 images, 30 images and 60 images, respectively.
## 3 System Model
### Task and Computation Model  
Consider that the computational tasks for IoT devices arrive unpredictably during each time slot and conform to a Poisson distribution. Let \(\theta_{m,t}\) signify the computational task that arrives at IoT device \(ID_{m}\) during time slot \(t\). This task \(\theta_{m,t}\) is characterized as \(\theta_{m,t}=(D_{m,t},F_{m,t})\), where \(D_{m,t}=\lambda_{m}\tau\) denotes the size of the task \(\theta_{m,t}\), and \(F_{m,t}\) indicates the CPU cycle count necessary to process each bit of data in the task \(\theta_{m,t}\), where \(\lambda_{m}\) signifies the typical task arrival rate for device \(ID_{m}\).  
Considering that IoT devices often have limited computational capabilities and constrained energy resources due to their compact size and design, they are not suited for processing computationally intensive tasks locally. Performing such tasks would result in significant delays and quickly deplete the device's battery life. Therefore, rather than executing computational tasks themselves, IoT devices offload these tasks to relay satellites. Relay satellites possess enhanced processing power and energy availability, enabling them to handle tasks efficiently either through onboard computation or by forwarding tasks to ground stations for processing. This offloading strategy not only conserves the limited resources of IoT devices but also improves overall system performance by leveraging the capabilities of more powerful network nodes. Define \(z_{m,t,k,t^{\prime}}\in\{0,1\}\) as the variable indicating the transmission of task \(\theta_{m,t}\). A value of \(z_{m,t,k,t^{\prime}}=1\) means that the task \(\theta_{m,t}\) is sent to the relay satellite \(RS_{k}\) during time slot \(t^{\prime}\), while \(z_{m,t,k,t^{\prime}}=0\) means it is not. Furthermore, let \(\lambda^{g}_{m,t,k,t^{\prime}}\in\{0,1\}\) denote the decision variable for selecting the ground station offloading mode for task \(\theta_{m,t}\). If \(\lambda^{g}_{m,t,k,t^{\prime}}=1\), it signifies that the relay satellite \(RS_{k}\) chooses the ground station mode for task \(\theta_{m,t}\) during time slot \(t^{\prime}\), whereas \(\lambda^{g}_{m,t,k,t^{\prime}}=0\) indicates otherwise.
## 2 Materials and Methods
### Evaluation of Resistance Profile  
Kirby-Bauer disk diffusion was employed to ascertain the resistance rates of the confirmed _P. aeruginosa_ isolates and further interpreted following CLSI guidelines. The _P. aeruginosa_ isolates were tested against the following antibiotic disks: imipenem (IMII), amikacin-(AK), aztreonam (ATM), ceftazidime (CAZ), ciprofloxacin (CIP), tobramycin (TOB), gentamicin (GEN), meropenem (MEM), levofloxacin (LEV) and norfloxacin (NOR). _P. aeruginosa_ isolates, resistant to more than three antibiotic classes, were classified as multidrug-resistant (MDR).
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics
## 3 Results and Discussion
### Influence of Cutting Depth
#### 3.2.2 Cutting Force and Workpiece Temperature  
Figure 9 shows the tangential force and normal force during cutting with different cutting depths. The tangential force increases with the increase of the cutting depth. Tangential force with a cutting depth of 0.5 nm increased rapidly at the beginning, and then suddenly decreases. The normal force with the cutting depth of 0.5 nm increases at the beginning, unlike the rest. Normal force grew gradually with the chip forming beneath the cutting tool if the atoms beneath the cutting tool are not heavily distorted. Figure 10 shows the temperature of the 4H-SiC workpiece during cutting with different cutting depths. The temperature also increases with the increase of the cutting depth, as more  
Figure 8: Structure transformation of the workpiece after cutting with different cutting depths. (a) Cutting depth of 0.5 nm. (b) Cutting depth of 1.0 nm. (c) Cutting depth of 1.5 nm.  
atoms are chipped off when cutting depth is larger. The temperature of the workpiece with a cutting depth of 0.5 and 1.0 nm grew linearly during cutting, while the slope of the temperature with a cutting depth of 1.5 nm became larger after 20 nm.  
Cutting depth has a significant impact on the 4H-SiC structure during cutting. With a larger cutting depth, more atoms on the surface are changed to amorphous phase, and larger chip is formed in the front of cutting tool. The process is more like scratching than cutting, and no defect exists in the workpiece when the cutting depth is small. When the cutting depth is larger, the material remove rate is higher, and more defects exist. There is a trade-off between the efficiency and the quality. The cutting force and the temperature are also sensitive to the cutting depth. More atoms are involved in the cutting, which leads to the increases of force and temperature. Since the simulation region is adiabatic, if we ignore the energy variation induced by the structure transformation, the increment of the temperature can be described as:  
\[\Delta T=\frac{FV\Delta t-P\Delta t}{3/2 Nk} \tag{1}\]  
where \(F\) is the tangential force, \(V\) is cutting speed, \(P\) is cooling capacity, \(N\) is the number of atoms, and \(k\) is the Boltzmann constant. When the tangential force is constant, the increment of the temperature is linearly correlated to the increment of time. As temperature is computed with the velocity of atoms, the rotation and the translation of the chip would induce workpiece temperature bias. Large temperature bias results in the slope increase of temperature.  
Figure 10: Workpiece temperature of the cutting with different cutting depths.  
Figure 9: Tangential force and normal force of the cutting with different cutting depths.