esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Datasets  
* CIFAR-10: This study addresses two forms of imbalances: step imbalance [51] and long-tailed imbalance [25]. We derive two specialized datasets from the balanced CIFAR-10, which consists of 50,000 training and 10,000 testing images [52]. These are CIFAR-10-STEP and CIFAR-10-LT, created by systematically resampling the original dataset. The imbalance ratio (IR) quantifies the disparity in class sizes within a dataset, expressed as the ratio of the largest class to the smallest class. This study delineates ratios of 10, 50, and 100, indicating scenarios in which the dominant class surpasses the least frequent class by factors of 10, 50, and 100, respectively. For CIFAR-10-STEP, we ensure equal sizes for each of the five minority and five majority classes, standardizing the minority class fraction (denoted as \(\upmu\)) at 0.5 across all settings, ensuring consistent class distribution during the training and testing phases. The CIFAR-10-LT exhibits a long-tailed distribution with a progressive decrease in sample sizes across classes, maintaining even distribution in the test sets.
* SVHN: The Street View House Numbers (SVHN) dataset includes images of real-world house numbers divided into 10 classes. We use a cropped version with fixed \(32\times 32\) pixel resolution and character-level ground truth. The SVHN has 73,257 training and 26,032 test images. Similar to CIFAR-10, we derive SVHN-STEP and SVHN-LT datasets by resampling the original dataset according to step and long-tailed imbalances.
* ImageNet-LT: Derived from the original ImageNet, ImageNet-LT [53] transforms it into a long-tailed distribution with the same 1000 categories, such as various animals and objects. The training set in ImageNet-LT features a pronounced imbalance, with the largest category containing 1280 samples and the smallest just 5. The validation and test datasets remain as per the standard ImageNet. This dataset provides a clear picture of class distribution imbalances on a large scale.  
The structure and details of these imbalanced datasets are presented in Table 2, offering a clear overview for understanding the experimental setup.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 4 Ways Forward  
Given the myriad of monitoring and evaluation methods, each having their own advantages and limitations, it is often beneficial to leverage more than one method to get a more complete picture of water and sanitation behavior. Combined methodologies reinforce the advantages, while addressing the limitations, of each of the monitoring technique that they are comprised of. Surveys, ethnographies, and direct observation provide context for electronic sensor readings that remain objective despite interim analyses. Sensors or spot checks may capture household characteristics, but surveys, and ideally, structured observations, are used to inform individual behavior, which further refines the algorithm or index for streamlined analysis during subsequent monitoring periods. Overall, combining methodologies promises to provide a comprehensive and instructive depiction of WASH usage.  
Some of the technologies and methods presented in this paper are well established, while others hold promise but require extensive field testing and validation, commercialization, and scaling. As such, we have not attempted to directly compare costs. Also, applications vary widely, and it is beyond the scope of our study to compare the relative value or reliability of different methods. Instead, we present a menu of options for policy makers, program implementers, and auditors to consider when designing impact measurement efforts.  
Senior authorship is not assigned.  
This paper builds on a previous publication by the authors published at the World Bank [53]. The World Bank granted to _Water_ a worldwide, non-exclusive license to publish its copyrighted material entitled. Author [PERSON] is a co-owner of SweetSense Inc., a company referenced in this work. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its executive directors, or the countries they represent. The findings, interpretations, and any remaining errors in this paper are entirely those of the authors.
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 5 Study of the Incentives under Non-Information-Sharing Conditions
### Design Details  
In this paper, the interaction between the game participants is formulated as a multi-intelligence POMDP. The details of its state space, action space, and reward function are as follows.  
State space: For the current iteration at time t, the state of each participant in the game is composed of its previous experiences and the experiences of all the other participants in the most recent training sets \(L\). Specifically, \(\mathbf{S_{0}^{t}}=\{\mathbf{\omega}^{t-L},\mathbf{\omega}^{t-L+1},...,\mathbf{\omega}^{t -1}\}\) is expressed as the state of the user, and \(\mathbf{S_{m}^{t}}=\{\mathbf{v_{0}^{t-L}},\mathbf{\omega}_{-m}^{t-L},\mathbf{v_{0} ^{t-L+1}},\mathbf{\omega}_{-m}^{t-L+1},...,\mathbf{v_{0}^{t-1}},\mathbf{\omega}_{-m}^ {t-1}\}\) is expressed as the state of ES \(m\).  
Action space: According to the game decision variables, the user's action at iteration time \(t\) is defined as \(\mathbf{a_{0}^{t}}=\mathbf{v_{0}^{t}}=\{R_{1},R_{2}\}\), and the action of ES \(m\) at time \(t\) is defined as \(\mathbf{a_{m}^{t}}=\mathbf{\omega_{m}^{t}}=\{f_{m},r_{m}\}\). And, in order to increase the learning efficiency, the action values of each agent are restricted to the range of 0-1 using the Min-Max normalization method.  
Reward function: Based on the utility function and constraints, the agent reward function is designed since the maximum amount that the user can offer is \(R_{max}\). Thus, a  
Figure 3: DRL-based incentive mechanism framework.  
penalty factor \(\mu\) must be added to the reward function when the total price exceeds \(R\)max. Then, the reward function of the user at the moment of iteration \(t\) is set to be  
\[R_{0}^{t}=\begin{cases}U&if\quad R_{1}+R_{2}\leq R_{max}\\ U-\mu(R_{1}+R_{2})&\text{otherwise}\end{cases}. \tag{30}\]  
The reward function of ES \(m\) at time \(t\) is  
\[R_{m}^{t}=u_{m}. \tag{31}\]
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results
### Evaluation Indicators and Experimental Results
#### 3.5.1 Subjective Evaluation Indicators  
The subjective assessment method, the mean opinion score (MOS), is commonly utilized. The MOS assessment criteria are shown in Table 2.  
For the subjective evaluation, five Tibetan university students were invited and ten Tibetan texts were randomly selected. They were asked to score the same text for listening, and were provided the speech synthesized with different models using the original speech as a reference. Ultimately, the average score from all the listeners was calculated as the final result.
## 1 Introduction
The triaxial load cell (TLC) introduced in this study serves as a transducer that transforms force into an electrical output signal, facilitating real-time linear measurement and correlation with the applied force. A notable technological edge over conventional force measuring devices is that the TLC can be integrated onto or within the tool or product being handled by workers during task execution, eliminating the need for placement on the skin or hand. Thus, the prototype load cell proposed in this work differs from other devices in its use and can be integrated into ergonomics, biomechanics and human-machine interaction system applications. A comparison of characteristics between the current device and the TLC proposed in this paper is shown in Table 1. As can be seen, the novelty lies in measuring the applied force exerted by hands or fingers in real time, with a device implemented in the product or tool, with a non-invasive measuring technique. The rest of this paper is organised as follows: The Materials and Methods are described in detail in Section 2. The Results of this study are presented in Section 3. The Discussion and Conclusion of the investigation are presented in Sections 4 and 5, respectively. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **It Measures in Real Time** & **It Is Non-Invasive** & **It Can Be Unplemented in Tools** & **It Can Be Used during Task Development** & **It Measures the Triaxial Force** \\ \hline [PERSON] et al. [11] & ✓ & ✓ & & & \\ [PERSON], et al. [20] & ✓ & ✓ & & & \\ Tekscan, Tactile Grip Measurement System [21] & ✓ & ✓ & & & \\ [PERSON] et al. [22] & ✓ & ✓ & & & \\ [PERSON]. [PERSON], et al. [23] & ✓ & ✓ & & & \\ Tekscan, Body Pressure Measurement System & ✓ & ✓ & ✓ & & \\ (BPMS) [24] & ✓ & ✓ & ✓ & ✓ & \\ [PERSON], et al. [25] & ✓ & ✓ & ✓ & ✓ & \\ [PERSON], et al. [26] & ✓ & & ✓ & & \\ [PERSON], et al. [27] & ✓ & ✓ & ✓ & & ✓ \\ [PERSON], et al. [28] & ✓ & ✓ & ✓ & & ✓ \\ Three axial load cell proposed in this paper & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of characteristics between devices that measure the applied force from the wrist.
## 2 Materials and Methods
### Unipolar Pulser Generator Analysis  
High-voltage unipolar pulses are widely used in ultrasound applications because of their large bandwidth. A unipolar pulse signal is described in Figure 6. The shape of this pulse is analyzed into 3 parts. The first part consists of logic level 0 (rising edge), the second part is the time when the logic level is highest, and the last part is the overshot of the signal (falling time). For high-frequency transducer stimulation, signals require a small duty to click, thus reducing the rising time and falling time of the signal. In order to reduce these two parts of the signal, the impedance must be matched between the source and the load. At the same time, the signals need to be similar in length.  
In this study, we needed to generate a unipolar pulse with a duty of less than 10 ns to trigger the transducer. This unipolar pulse was generated from the FPGA, then entered the input of the STHV800 chip to raise the voltage and trigger the transducer. The rising time and falling time components are described in Figure 6. To achieve such a signal, we needed to solve four major problems in the PCB design: signal reflection, signal noise, signal crosstalk and signal timing [28; 35; 36]. To solve these problems, based on the design of the PCB, we needed to ensure the proper values of the following factors: impedance, matching and spacing. The undershoot ringing in particular was solved by selecting damping impedance values suitable for each frequency of the transducer [37]. Reducing the undershoot ringing was essential for raising the bandwidth of the P/R device.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
###### Abstract  
Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system's operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model's computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model's accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.  
auto-regressive; control and optimization; energy management; recurrent neural network; long short-term memory; microgrid; switched model predictive control +
Footnote †: journal:  
Article
# Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
## 3 Materials and Methods  
Although synthetic data is increasing in popularity, it is still in the experimental stage. Its current use is limited to exploratory analyses, with the final analysis almost always obtained from the original dataset [40]. In fact, the utility of the synthetic data is still a matter under investigation and will eventually determine whether synthetic data will be used outside exploratory analysis.  
To study the effect of synthetic data generation and usage settings on the utility of the generated synthetic data and its models, we attempt to answer the following 4 questions:  
* Does preprocessing real data prior to the generation of synthetic data improve the utility of the generated synthetic data?
* When calculating prediction accuracy, does importing the real data settings (tuning-settings) to the synthetic data lead to improved accuracy results?
* If real-data settings are not imported, should we apply tuning on the synthetic datasets at all? Or would non-tuning lead to a better accuracy?
* As propensity is widely regarded as the most practical measure of synthetic data utility, does a better propensity score lead to better prediction accuracy? in other words, is propensity score a good indicator for Prediction accuracy?  
Note that, for the purpose of this investigation, _preprocessing_ represents imputing missing values in the data, encoding categorical values as integers, and standardizing numeric features. _Tuning_ consists of choosing the best hyperparameters of the model and selecting the best set of predictors (feature selection). Consequently, _tuning settings_ are the hyper parameter values chosen and the features selected. In _non-tuned_ models all features are kept and the hyper parameters values are set to default (default values as provided by the sklearn library).  
To answer the above questions, we generate multiple synthetic datasets from 15 different public datasets (Table 1) using the (aforementioned) four synthetic data generators. Multiple different generation paths are followed to answer the above questions (resulting in a generation of 2,359 synthetic datasets in total). The paths are explained in details in Section 3.1 followed by a presentation of the synthetic data generation process in Section 3.2.
## 2 Literature Review
### Dynamic Scheduling in Production Systems  
Scheduling in production systems ensures the coordination of activities to increase efficiency and reduce operational costs. In stochastic and dynamic production environments, traditional scheduling solutions based on classic objectives like makespan may not suffice. Additional criteria capable of addressing stochastic disruptions need to be considered due to the random disturbances that may occur in the system. Often, rescheduling is employed to enhance system performance and counteract the effects of random disruptions.  
In many real-world environments, scheduling is an ongoing reactive process due to the presence of various unforeseen interruptions, making continuous the reevaluation of predetermined schedules necessary. Solutions developed to address static scheduling problems are often impractical in real-world environments, as schedules optimized based on predicted data may become invalid when deployed in the workshop. Most production systems operate in dynamic environments where unforeseeable real-time events can lead to changes in scheduled tasks, rendering a pre-implemented schedule unfeasible upon presentation to the workshop. Examples of such real-time events include machine breakdowns, the arrival of urgent tasks, changes in delivery deadlines, and more.  
Dynamic scheduling strategies aim to adapt to changing conditions and uncertainties in manufacturing environments. [PERSON] and [PERSON] [8] emphasized the utilization of real-time information for effective dynamic scheduling, highlighting the importance of timely adjustments to changing conditions. [PERSON] and [PERSON] [9] provided a comprehensive survey of dynamic scheduling in manufacturing systems, outlining various approaches and challenges in dynamic scheduling environments. They laid the groundwork for understanding the complexities and requirements of dynamic scheduling systems.  
As can be seen in Figure 1, dynamic scheduling, crucial for the successful implementation of scheduling systems in real-world scenarios, is categorized under three main types [7; 10; 11; 12; 13]: completely reactive scheduling, predictive-reactive scheduling, and robust proactive scheduling. The possible machine environments are also demonstrated in Figure 1 according to [PERSON]'s classification [1].
## 3 Pose Estimation for Multi-Marker Systems  
Our proposed methodology extends the single-marker approach by incorporating multiple ArUco markers to enhance pose estimation robustness and accuracy. The framework establishes a representative marker coordinate system, one of the ArUco markers within the marker array. Based on the assumption that the closer a detected marker is to the center of the camera image, the more stable the pose estimation result, we selected the marker closest to the image center as the representative marker among the detected markers. The pose estimation process follows a structured pipeline that systematically processes the pose information from all detected markers to compute the final pose estimate relative to the camera coordinate system.  
The structured pipeline for pose estimates is described in Figure 1. Figure 1a shows an initial stage in which pose was computed relative to the camera coordinate system for all identified markers. Figure 1b,c shows a two-stage fault detection process to identify and exclude unreliable measurements. Figure 1b shows spatial consistency verification to detect invalid markers. The algorithm analyzed the spatial relationships between identified markers by comparing their relative orientations. Markers with significant differences in orientation measurements relative to the consensus of the marker array were identified as invalid markers. Figure 1c shows temporal stability analysis to detect outliers in estimates. The algorithm monitored the temporal evolution of each marker's pose measurements. Markers displaying abrupt changes in their orientation measurements beyond established thresholds were identified as temporal outliers and excluded from subsequent processing. After removing estimates from markers with unreliable measurements, pose relative to the camera coordinate system was calculated by averaging estimates from multiple markers as shown in Figure 1d.
## 4 Results
### Effect of Deep Rolling Pressure
The periodic nature of the pressure application, as shown in the graph, helps distribute the compressive stresses evenly across the surface. The dynamic movement of the tool ensures that the pressure is applied cyclically, enhancing the uniformity of the stress distribution. The deep rolling pressure applied experimentally (150 bar) and in the FEA simulation (157 bar) leads to significant localized deformation and the formation of compressive residual stresses. The FEA model accurately captures the pressure distribution and dynamic fluctuations, closely approximating the experimental conditions. The high pressure in the red zones leads to beneficial surface strengthening, improving the material's overall fatigue resistance. By understanding the effect of deep rolling pressure, engineers can optimize the process for enhanced material performance. Figure 14b illustrates the pressure variation during the deep rolling process, with the green line showing dynamic pressure fluctuations as the tool moves, characterized by periodic peaks (\(\sim\)157 bar) at maximum contact and troughs during transitions, reflecting oscillatory loading and unloading. The red line indicates regions of minimal or negative pressure, correlating with tensile stress zones or low deformation areas, while the blue line represents stable baseline pressure or residual stress in regions unaffected by tool dynamics. Together, these variations highlight the localized plastic deformation at high-pressure zones, essential for inducing beneficial compressive residual stresses and ensuring stress uniformity across the material.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 2 Data
### CryoSat-2 Data  
The radar altimeter on CryoSat-2 makes observations in the Ku-band using different sampling modes. The observation mode of SIRAL switches between the low-resolution mode (LRM), synthetic aperture radar (SAR) mode, and SAR interferometric mode according to a geographical mode mask [6]. In this study, we use data from the Radar Altimeter Database System (RADS), which is maintained by the Delft Institute for Earth-Oriented Space Research [17]. Along with times and locations, the 1 Hz significant wave height is provided for use in this study. To obtain a consistent dataset throughout the ocean regions  
Figure 1: CryoSat-2 orbits (black) in the Pacific and Atlantic oceans, as well as the Bering Sea, for the period from 1 August 2020 to 1 August 2021, that are coincident with ICESat-2 and contain wave height measurements (green). 136 CryoSat-2 orbits from this period have been found to be both coincident with ICESat-2 and more than 40 km from land. The analysis has been limited to latitudes between 0 and 70 degrees north. White stars indicate the locations of the in situ stations used to validate the CryoSat-2 data.  
where SIRAL was operating in SAR mode, RADS converts the data to \"Pseudo-LRM\" data, providing the 1 Hz measurements with a 15 km footprint that are used in this study [18; 19].