esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ensemble-Guided Models  
### \(\Pi\)-Model and Temporal Ensembling for Semi-Supervised Learning  
This section describes self-ensembling models for the semi-supervised learning. In early research works for the semi-supervised task, there were two mainly improved methods that surpass previous research works: \(\Pi\)-model and temporal ensembling [24]. The \(\Pi\)-model utilizes the consistency between same network structures with different dropouts in order to handle the unlabeled data. As shown in Figure 2, the input data are processed twice by networks with different dropouts to generate two inference results, i.e., \(\mathbf{z}_{i}\) and \(\hat{\mathbf{z}}_{i}\). There are two components of loss function. The first is a classification cost between the inference results and the labels, which is commonly measured by cross-entropy. The second is a consistency cost between the models with different dropouts. The consistency cost can be measured by any distance functions, but the mean-squared error is a good choice in the previous research works. The \(\Pi\)-model successfully provides a prototype of consistency learning, but it has two main drawbacks: The reference of consistency, \(\hat{\mathbf{z}}_{i}\), is noisy because it is simply generated by another model with a random dropout, and the model should perform the inference step multiple times for a training step, thus making the training step slow.  
Figure 1: Problem description for the model-complexity-limited acoustic scene classification (ASC) task.  
In order to compensate for the drawbacks of the \(\Pi\)-model, a temporal ensembling technique is developed as shown in Figure 2. The temporal ensembling generates the reference of consistency \(\tilde{\mathbf{z}}_{i}\) by exponential moving average of inference outputs of previous epochs, instead of another model with different dropout, as [24]  
\[\mathbf{z}_{ema}(n) =\alpha\mathbf{z}_{ema}(n-1)+(1-\alpha)\mathbf{z}(n) \tag{1}\] \[\tilde{\mathbf{z}}(n) =\frac{\mathbf{z}_{ema}(n)}{1-\alpha^{n}} \tag{2}\]  
where \(n\) is an epoch number, \(\mathbf{z}\) and \(\mathbf{z}_{ema}\) are the prediction result and its exponential moving average, respectively, and \(\tilde{\mathbf{z}}\) is a target vector with bias correction. The temporal ensembling makes better results with faster speed than the \(\Pi\)-model; however, it requires additional storage space to save the ensemble predictions of the whole epoch, and the implementation is complicated.
## 3 Datasets
### Real Scale Field Dataset  
The field dataset was acquired to better characterize the groundwater system of the Montalto Uffugo test site [55; 59; 60; 61]. From a hydrogeological point of view, the test site can be divided in four formations (Figure 3a): a shallow phreatic aquifer (formation A) to a depth of 7 m; a shale layer from 7 to 11 m (formation B); the main confined aquifer, from 11 to 55 m, which is composed by silty sands alternated to conglomerate and clay lenses (formation C); and the shale substratum (formation D).  
The Montalto Uffugo system is monitored by 11 boreholes (B1-11), and two new PVC piezometers (P1 and P2 in Figure 3) were instrumented for CH-ERT measurements. For the confined aquifer, the water table is approximately at \(-6\) m and hydraulic conductivity is in the order of \(10^{-5}\) ms\({}^{-1}\)[55].  
A tracer test experiment was carried out at the site by the injection of 400 L of saltwater solution (concentration C = 100 g/L of NaCl) into the confined aquifer C through the borehole B1 (Figure 3). At the same time, to create a water flux longitudinal compared to piezometers P1-P2, 1 L/s of water was pumped in B9. The entire test lasted about 27 days and was stopped before the salt tracer could reach P2, due to the rupture of the pump. The tracer test was monitored by temperature and electrical conductivity of water in the piezometer P1, B5, and P2. In addition, CH-ERT monitoring based on time-lapse geoelectrical survey was carried out [56; 57; 58].  
A number of 48 steel circular electrodes installed along P1 and P2 were used to perform the CH-ERT measurements within 6-52 m in depth. A total of 87 CH-ERTs (3 at days)  
Figure 3: (**a**) Tracer test at Montalto Uffugo test site: (Formation A) shallow aquifer; (Formation B) shale; (Formation C) main aquifer, silty sands alternated to conglomerate and clay lenses; (Formation D) shale. B1–11 are boreholes. P1–2 are piezometers for CH-ERT. (**b**) Pre-tracer test resistivity model and B11 stratigraphic log (from 41, modified); (**c**) water electrical conductivity (normalized at 20 \({}^{\circ}\)C) monitored in P1 at the depth of 40 m; (**d**) water electrical conductivity (normalized at 20 \({}^{\circ}\)C) logs measured 20 days after the salt injection (19 July), in boreholes B5 and P2. Adapted from [55].  
with 1836 measurements for each CH-ERT were taken with reciprocal configuration for errors assessment. Electrical resistivity data were acquired using a dipole-dipole array with injection current dipole and receiver dipole located in different boreholes [56; 57; 58].  
Figure 3b shows the starting resistivity model acquired two days before the tracer test and inverted using ResIPy software [62; 63]. The area under investigation was parameterized using a node-centered, 2D, quadrilateral mesh made by 30 \(\times\) 66 square elements, covering a region of 128 m \(\times\) 221 m to account for current flow towards infinite boundaries.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Routing Network
#### 4.3.1 Multiplexer  
Using the row-index values kept in the ROM1-location, the MULX unit chooses \(W_{v}=30\) blocks of \(Z_{c}=56\) bits from the SCND. However, instead of connecting each input to each output, this module requires fewer hardware resources by adopting the method of storing ROM1-location row-index values in ascending order [27]. For instance, the block column of any BGM has weight \(w_{v}\), such that \(w_{v}\leq W_{v}\). All of the \(W_{v}\) values of ROM1-location are arranged so that the top \(w_{v}\) block row-indices of the non-\({}^{\prime}\)-1\({}^{\prime}\) entries in that block column are in ascending order, while the remaining \(W_{v}-w_{v}\) values are empty data. The multiplexer designed to support the seven LDPC BAMs of the allowed code rate set with maximum column weight \(W_{v}=30\) requires 210 connections. By using this method [27], the number of connections decreased to 39% of those previously required.
## 2 Materials and Methods
### Threshold Warning Method Based on the DBA-LSTM and Numerical Simulation Algorithms  
Numerical simulations can not only analyze the current landslide stability but can also simulate the landslide failure scenario and calculate the landslide failure warning threshold. To ensure the accuracy of the numerical simulation results, the proposed method suggests that the physical and mechanical parameters of a numerical model should be corrected by the DBA-LSTM algorithm according to the relative displacement of rock and soil mass caused by dynamic construction or other reasons before implementing the numerical simulation. The algorithm flow is shown in Figure 6, and the detailed steps are as follows:  
Step 1: Establish a landslide grid model based on the topographic data, collect and organize the deformation monitoring data information, including the pre-processing of the original data and the node information of its grid located according to the coordinates of monitoring points, and define the numerical model based on the geological data.  
Step 2: When there are geotechnical parameters with uncertainty in the numerical model, construct machine learning training samples using the orthogonal test method, and after training, to obtain a reliable DBA-LSTM model, correct these uncertain parameters using the measured data and take them as the current equivalent mechanical parameters of the landslide.  
Step 3: Based on the modified numerical model, the current landslide factor of safety is calculated using the tension-shear damage strength reduction method [41], and the factor of safety \(F\) can be written as  
\[c^{\prime}=\frac{c}{F},\tan\varphi^{\prime}=\frac{\tan\varphi}{F},T^{\prime}= \frac{T}{F} \tag{6}\]where \((c,\tan\varphi)\) and \(T\) denote the shear strength and tensile strength indicators, and \((c^{\prime},\tan\varphi^{\prime},T^{\prime})\) represent the parameters after reduction. For the detailed method, refer to the literature [42].  
Step 4: Determine the potential failure conditions of the landslide, such as the dynamic excavation, groundwater level changes, etc. Taking the changes in the groundwater level as an example, the seepage module is invoked in the numerical simulation software to simulate and calculate the displacement calculation results under the limit equilibrium state of the landslide by continuously adjusting the water level conditions, and the corresponding warning criteria (e.g., water level and relative displacement thresholds) can be obtained.
## 2 Materials and Methods
The EH2 unit has a platform [42] designed for ultrafast liquid chemistry, which covers several complementary X-ray techniques, e.g., pump-probe XAFS [19; 21; 27; 43; 44], pump-probe X-ray emission spectroscopy (XES) [45], and pump-probe X-ray liquid scattering. The monochromatized X-ray pulses were focused with a Be compound refractive lenses (CRLs) [46] to give a beam size of 100 \(\times\) 100 \(\upmu\)m\({}^{2}\) at the sample position. Two types of X-ray detectors were utilized to measure the fluorescence X-ray intensity emitted from samples. One was a large 2-dimensional detector developed by RIKEN, Japan, which is referred to as a multi-port charge-coupled device (MPCCD) detector [47]. The other was a Si photodiode detector (Hamamatsu Photonics, S3590-09). The MPCCD can be utilized for various types of X-ray measurements. Each pixel in the MPCCD has an energy resolution (<1 keV); therefore, the MPCCD can be used as an energy-dispersive detector similar to a solid state detector (SSD) or a silicon drift detector (SDD), which are widely used in XAFS beamlines. On the other hand, the photodiode detector produces a charge pulse upon the acceptance of a fluorescence X-ray photon, which is processed by charge-sensitive and shaping amplifiers. The output signals from the amplifiers are evaluated by a waveform fitting [48]. The incident X-ray intensity was measured with a transmissive intensity monitor for the normalization of the fluorescence X-ray intensity. Inside the intensity monitor, there were photodiodes that measured X-ray intensity scattered by a polyimide or CVD film [42]. The frequency-doubled Ti-sapphire laser was employed as an excitation laser, where the pulse duration was 70 fs and the estimated fluence at the sample position was 520 mJ-cm\({}^{-2}\). The timing jitter between the excitation and X-ray laser pulses was typically 500 fs, which limited the time resolution. The timing jitter was corrected for the ultrafast experiment (\(\leq\)100 fs) using an arrival-timing monitor (ATM) [49]. The ATM developed at the SACLA recorded the time difference between the probe X-ray and the pump laser pulses using the change of the transmittance of a gallium arsenide (GaAs) thin film by the irradiation of a short X-ray pulse. Analysis of the time difference for each X-ray pulse revealed the net delay time of the laser pulse. This post-data-processing improves the time resolution of the measurements. The time resolution using the ATM can be estimated as ca. 12 fs [27; 50]. (It should be noted that the actual time resolution is dependent on other factors of the experimental setup, such as the pulse duration of the pump laser and the thickness of the sample.)
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 5 Conclusions  
In this study, we introduced the void area curve (VAC) as an innovative approach for accurately predicting soil density based on digital image analysis. The VAC methodology represents a significant departure from conventional techniques, offering a novel perspective on soil density prediction. While our investigation highlighted the Otsu method as the optimal choice among automatic thresholding methods, it was the simplicity and robustness of the \(n^{\prime}\) metric calculated through simple thresholding that demonstrated a superior correlation with soil density. Recognizing the inherent complexities associated with threshold value selection, our research addressed this challenge by introducing the VAC. This method not only enhances the precision of soil density predictions but also eliminates the need for manual threshold determination.  
A remarkable outcome of our study was the remarkable accuracy achieved in predicting the dry density of soil, through the standard deviation of the VAC. However, it is essential to acknowledge some limitations. The applicability of the VAC method may vary depending on soil types, and further research is needed to explore its performance under diverse geological conditions. Additionally, while the VAC simplifies threshold selection, fine-tuning for specific applications might still be necessary in some cases. In conclusion, the standard deviation of the VAC emerges as a powerful tool with the potential to revolutionize soil density prediction, offering a universally applicable approach. Future analyses should aim to refine the VAC's applicability across soil types and investigate additional factors that may influence its predictive capabilities.  
Conceptualization, D.K. and Y.S.; data curation, D.K. and Y.S.; formal analysis, D.K.; funding acquisition, D.K. and Y.S.; investigation, D.K.; methodology, D.K.; project administration, Y.S.; resources, D.K.; software, D.K.; supervision, Y.S.; validation, D.K. and Y.S.; visualization, D.K.; writing--original draft, D.K.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A03045271) and the Rural Development Administration \"New Agricultural Climate Change Response System Construction Project\" (RS-2023-00219113).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Performance of Blob-Level Classification  
#### 3.4.1 Image Registration  
The registration performance using the diffeomorphic demons algorithm showed over 98.6 \(\pm\) 0.2% of Intersection over Union (IoU), which was the ratio of the area of overlap to the area of union between registered (VNIR) and reference images (SWIR). Note that a pixel-level registration of two chicken fillet images would be very challenging due to the lack of prominent features on the surfaces of meat to match between two HSI modalities. A further study is necessary to investigate an image registration technique for pixel-level alignment of relatively featureless images of chicken breast meat.
## 2 System Model
### AoI Model  
In this scenario, the task vehicles obtain decision information derived through a series of processing steps. We use AoI to evaluate the timeliness of this decision information. \(AoI_{i}(t)\) denotes the time elapsed since the last time the \(i\)-th task vehicle completed computing the task at the head of queue. Denote \(t_{i}^{\text{next}}\) as the generation time of the second task in the queue of the \(i\)-th task vehicle and \(Len(Q_{i})\) represents the length of task queue in the \(i\)-th task vehicle. If the \(i\)-th task vehicle is able to finish the task at the head of the queue in time slot \(t\) and there are other tasks in the queue, \(AoI_{i}(t)\) is updated to the time elapsed since the generation of the second computational task; if the \(i\)-th task vehicle is not able to finish the task at the head of the queue in time slot \(t\), \(AoI_{i}(t)\) increases linearly, otherwise \(AoI_{i}(t)\) is set to 1. Therefore, the evolution of AoI for the \(i\)-th task vehicle in time slot \((t+1)\) is expressed by  
\[AoI_{i}(t+1)=\begin{cases}t-t_{i}^{\text{next}},&D_{i}^{\text{rem}}(t)\leq D_ {i}^{\text{loc}}(t)+D_{i}^{\text{off}}(t),Len(Q_{i})>0,\\ AoI_{i}(t)+\tau,&D_{i}^{\text{rem}}(t)>D_{i}^{\text{local}}(t)+D_{i}^{\text{off}}( t),\\ 1,&\text{otherwise}.\end{cases} \tag{14}\]
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.6 Better Network Performance  
As discussed in the policy enforcement section, a policy engine can be used to convert high-level policies into rules by considering the policy requirements and in-network knowledge. Consider a network monitoring task that involves monitoring the resource utilization of network devices. KDN can improve resource utilization by identifying and eliminating network inefficiencies, which include identifying underutilized network resources and reallocating them to areas of the network that require resources. Network performance can be improved by real-time network monitoring for fault management, where faults in the network can be detected in real time using fault management policies and in-network knowledge, which can help network administrators quickly identify and troubleshoot network issues. Predictive network analysis can be used to predict events even before they occur, such as network congestion, possible device failure, loss of communication with the base station, etc., where early actions can be taken to reduce undesired effects. Forinstance, by predicting whether a mobile device is likely to have a loss of communication with the base station, a handover mechanism can be activated to assign the mobile device to an available base station before the loss of communication with the current base-station occurs, such that the mobile device's communication loss time is minimized. KDN can automatically optimize network infrastructure based on network usage patterns, which includes identifying bottlenecks and adjusting the network resources to ensure that traffic flows smoothly. KDN can shape traffic in real time by dynamically allocating resources based on traffic patterns, which will ensure that critical applications have the necessary resources such as energy, bandwidth, etc. to operate efficiently and a high priority in forwarding traffic, while non-critical traffic is deprioritized.
## 2 Principle  
### Fringe Projection System  
The setup of a typical fringe projection system is shown in Figure 1. This system mainly includes a projector, a camera, and measured objects. The patterns are projected by the projector onto the measured objects from one direction, modulated by the objects' surfaces, and then captured by the camera from another direction. In Figure 1, Points \(O_{c}\) and \(O_{p}\) respectively denote the optical centers of the camera and the projector. The optical axes of the projector and the camera intersect at point \(O\) on the reference plane. Note that line \(O_{c
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On/Off-Policy
#### 3.3.1 Benchmarking  
In [21], the authors developed a unified simulator environment to evaluate RL algorithms by integrating OpenAI Gym with an ROS and Gazebo, as shown in Figure 16. This platform helps to evaluate various robotic applications in realistic simulated settings. The work provides a detailed explanation of the framework architecture and potential applications, highlighting the importance of the ROS in robot platforms, communication interfaces, and simulations.  
The authors in [21] used Q-Learning and SARSA RL algorithms in their experiments to gain insight into the strengths and weaknesses of each algorithm. Their approach works on the hyper-parameter selection and reward system design, ensuring fairness and consistency in comparisons. The authors' analysis of the results provides practical insights into the use of Q-learning compared to SARSA for robot navigation. Q-learning leads to faster convergence but potentially riskier behavior, while SARSA prioritizes a smoother and safer trajectory. This result emphasizes the importance of balancing learning speed with the robustness and safety of the policy.  
The above-mentioned work recognized the limitations of discretization and the trade-off between simplified learning and precision. This work is a significant contribution to the fields of RL and ROS for robotics. Their benchmarking framework enables the exploration of alternative algorithms, robotic platforms, and simulated environments, therefore advancing the field of robotics.
# Reduction of Bias and Light Instability of Mixed Oxide Thin-Film Transistors
## 3 Results  
TFT current-voltage (\(I\)-\(V\)) characteristics exhibit a clear dependency on \(t_{\rm IGZO}\) (Figure 4a). Previous reports also show similar effects of \(t_{\rm IGZO}\) on TFT parameters [17; 18; 19; 20]. The drop in the on-state currents and positive shift of V\({}_{\rm ON}\) with decreasing \(t_{\rm IGZO}\) are consistent with a decrease in carrier concentration [18] or an increase in deep electron traps at the a-IGZO film surface as the \(t_{\rm IGZO}\) becomes very small, as previously reported [18; 20]. However, it could also be as a result of 20 nm being the thickness in which gate modulation is effective, given that it becomes significant for \(t_{\rm IGZO}<20\) nm. Similarly, the C-V characteristics shift to the negative \(V_{\rm GS}\) direction with increasing \(t_{\rm IGZO}\) (Figure 4b). Table 1 lists the TFT parameters as a function of \(t_{\rm IGZO}\).  
Figure 4: Amorphous indium-gallium zinc-oxide (a-IGZO) thin-film transistor characteristics as a function of a-IGZO thickness (\(t_{\rm IGZO}\)), (**a**) current-voltage (\(I\)-\(V\)) and (**b**) capacitance-voltage (\(C\)-\(V\)) characteristics.  
Figure 3: Material properties of amorphous indium-gallium zinc-oxide films. (**a**) X-ray diffraction patterns, (**b**) transmittance, (**c**) Tauc’s plot, and (**d**) X-ray photon electron spectroscopy.  
For all the \(t_{\rm IGZO}\) investigated, the a-IGZO semiconductor should be fully depleted, given that the off-state leakage currents do not significantly change with increasing \(t_{\rm IGZO}\)[22]. The negative shift of \(V_{\rm ON}\) with increasing \(t_{\rm IGZO}\) is interesting, particularly because hydrogenated amorphous silicon (a-Si:H) TFTs do not exhibit such a dependency on semiconductor thickness [22; 23]. It is also interesting to note that SS increases with increasing \(t_{\rm IGZO}\), consistent with the stretching out of the C-V characteristics of TFTs with thick a-IGZO layers (Figure 4).  
The negative \(\Delta V_{\rm ON}\) induced by NBIS decreases with decreasing \(t_{\rm IGZO}\) (Figure 5a-e). Table 1 also shows TFT parameters extracted after the application of NBIS and Figure 5e shows the time dependency of the \(\Delta V_{\rm ON}\) for the TFTs. Note that when \(t_{\rm IGZO}\) = \(\sim\)3 nm, the \(\Delta V_{\rm ON}\) is negligible.  
Figure 5: Effect of negative-gate bias stress combined with visible light illumination (NBIS). (**a**–**e**) thin-film transistor (TFT) current-voltage (\(I\)-\(V\)) characteristics before and after NBIS for varying amorphous indium-gallium zinc-oxide thickness (\(t_{\rm IGZO}\)). (**f**) Turn-on voltage (\(V_{\rm ON}\)) dependence on NBIS Time. Channel width \(W\) = 2000 \(\upmu\)m and channel length \(L\) = 10 \(\upmu\)m. The NBIS-induced negative turn-on voltage (\(V_{\rm ON}\)) shift increases with increasing \(t_{\rm IGZO}\). NBIS is accomplished by holding the TFT gate voltage at \(-\)20 V for 10,000 s, while biasing the source and drain electrodes at zero volts, under white light illumination (9000 nit).