esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Knowledge Tracing Integrating Temporal Causal Inference and PINN
## 2 Related Work
### PINN Model  
In the field of computational science, surrogate models currently play an increasingly important role, especially in situations that require handling complex physical processes and large data analysis. Neural networks are becoming increasingly popular tools, being widely applied to simulate the complex systems. However, traditional neural network models often have shortcomings in strictly adhering to physical laws. Recent research [23] has combined high-fidelity and low-fidelity data, using data fusion techniques to reduce noise impact and improve solution accuracy. This method effectively integrates data of varying quality when dealing with complex systems in the real world, thereby enhancing prediction performance.  
[PERSON] [24] combined the Gaussian process model with the linear differential equation. This method can automatically infer the parameters and structure of differential equations from data, addressing uncertainties in the process, thereby improving predictive performance. [PERSON] [25] employed the Gaussian process [26] regression to design function representations for given linear operators, accurately inferring solutions and providing uncertainty estimates for several prototype problems in mathematical physics. The Physics-Informed Neural Networks (PINN) model [27] is a type of machine learning model that combines deep learning with physical knowledge. Unlike traditional data-driven neural networks, PINN models use physical laws to guide the model during training, thereby enhancing its generalization capability. This is particularly evident in situations where data are scarce or noisy. The PINN model is typically composed of a deep neural network. Its characteristic is the inclusion of physics-informed terms in the loss function, which represent the physical laws being followed. In traditional machine learning methods, the learning process is primarily data-driven, and models heavily rely on large amounts of high-quality data. However, in practical applications, there often exist challenges such as data scarcity or noisy data. In such cases, relying solely on data-driven models makes it difficult to achieve accurate and reliable predictions. Taking a one-dimensional damped harmonic oscillator as an example, the working principle of the PINN model is as follows: First, define the physical problem and the corresponding physical laws, such as the motion equation of the harmonic oscillator. The inputs are time t and position x, while the outputs are velocity v and acceleration a of the neural network. The loss function of the model consists of data error terms and physics-informed error terms. The data error terms measure the difference between predicted values and actual observations, while the physics-informed error terms ensure that the predictions comply with the relevant physical laws. By optimizing the loss function, the neural network is trained to ultimately produce predictions that adhere to the physical laws.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Feature Ranking Methods
#### 7.1.4 Variance  
The variance ranking method is a simple, yet effective approach for feature selection, primarily used in unsupervised learning scenarios. This method ranks features based on their variance under the assumption that features with higher variance contain more information and are more likely to be useful for distinguishing between data points. Variance is often used as a baseline method for feature selection because of its computational simplicity and intuitive nature. For a given data set \(X\in\mathbb{R}^{n\times d}\), where \(n\) is the number of samples and \(d\) is the number of features, the variance of the \(j\)-th feature \(F_{j}\) is computed as  
\[\sigma_{j}^{2}=\frac{1}{n}\sum_{i=1}^{n}(x_{j,i}-\mu_{j})^{2} \tag{7}\]  
where \(\sigma_{j}^{2}\) is the variance of the \(j\)-th feature, \(x_{j,i}\) is the \(i\)-th sample of the \(j\)-th feature, and \(\mu_{j}\) is the mean of the \(j\)-th feature. The variance measures the dispersion of the data points around the mean of each feature. Features with higher variance are ranked higher, as they indicate greater variability and potential discriminatory power. Despite its simplicity, the variance ranking method has proven useful in a variety of applications, including dimensionality reduction, clustering, and preprocessing steps for more complex feature selection techniques. However, it assumes that high variance correlates with high relevance, which may not always hold true, especially in data sets with nonlinear relationships or noisy features.
## 1 Introduction
All of these approaches can monitor the integrity of the GNSS in the IMU/GNSS integrated navigation system. Different from the above studies, [PERSON] proposed an integrity assurance mechanism for an EKF-based IMU/GNSS integrated system against IMU faults [18]. She calculated the PLs using the EKF innovations and additional uncertain noise boundary terms, by deriving the IMU faults propagation process in the EKF [18]. In her study, the full-state inertial navigation equations, including the attitude, velocity, and position update equations, were used as the EKF system propagation equations. Moreover, the velocity update equation was used as an example to explain how the IMU faults propagate in the EKF. In our study, different from the method published by [PERSON] et al. [18], the error-state inertial navigation equations, including the attitude error, the velocity error, and the position error update equations, were used to analyze the IMU faults propagation process which are more commonly used as the EKF system propagation equations in IMU/GNSS integrated navigation than the full-state inertial navigation equations [18; 19; 20]. While being inspired by the previous published research in this area [16; 17; 18; 20], the error overboundings against the IMU faults for the EKF state estimation using the error-state inertial navigation equation is derived in this paper. Moreover, the error overboundings using error-state and full-state inertial navigation equations are compared to determine which kind is more conducive to assuring the integrity for the IMU/GNSS integrated navigation architecture against IMU faults. After using the inertial navigation error equation, the magnitude of each dimension of the state vector keep close or consistent, and the occurrence of singular matrices in the filtering calculation process is avoided, which will shorten the filtering time, improve the converge speed, and increase the calculation efficiency. To simplify the description below, the EKF using the full-state inertial navigation equations is named the full-state EKF, whereas the EKF using the error-state inertial navigation equation is named the error-state EKF. Accordingly, the remainder of this paper is organized as follows: (1) the general structures of the IMU/GNSS integrated navigation architecture using the IMU error-state model are introduced, (2) the detailed IMU faults propagation process in the error-state EKF is specially derived, (3) a description of the real-time error overboundings (PLs) equations in the error-state EKF for IMU faults based on innovations, (4) the simulations for the error overboundings calculation under IMU faults are presented, and (5) the summary and concluding remarks close the paper.
# Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android
## 4 Findings
### Mobvoi (TicWatch) Artifacts
#### 4.3.2 Activity-Related Artifacts  
In terms of health-related data, TucWatch stores these data in multiple database files. The first of these databases is the \(\backslash\)_databases_\(\backslash\)_fitness.db_ database. TheTable _RECORD_ in this database stores health and activity information, such as heart rate, step count, calories, start time, end time, and distance (see Figure 25). Each entry has a **HASH** related to it, as well as a **TYPE** and a **DEVICE_ID**. It is difficult to determine the purpose of the hash value and which activities the types relate to. However, given enough time and context from other stored tables, one could potentially determine what the related health data represent. It is possible that **TYPE** identifies the exact activity that was recorded by the watch. The **DEVICE_ID** in this table corresponds to the smartwatch's device ID. The _fitness.db-wal_ file was also examined to determine whether any information was discoverable outside of the database file, but the information contained data that were already available in the associated database file.  
More relevant health data was recovered from the _health_common.db_ database which contains the _data_point_ table (see Figure 26). The _data_point_ table includes the **device_id**, which refers to either the Mobvoi Ticwatch or Samsung phone as shown by the **data_source** table (see Figure 27), a **wwid**, which may be an unique identifier for the user, a **type**, which identifies the type of activity data being recorded, and **time_to** and **time_from** timestamps, which denote the activity's start and end times. Moreover, the _data_point_ table appears to be a superset of the _RECORD_ table in the _fitness.db_ database, which means given these two tables, one could potentially extrapolate more meaningful information about the health activities.  
Figure 25: A reduced view of the different activities recorded in the _RECORD_ table of the Mobvoi.  
Figure 26: Reduced view of health-related data recorded by the _TicWatch_.  
There is a _POINT_ table located in the _fitness.db_ database that seems to record finer-grained and raw data collected from the smart watch before meaningful data are extrapolated from it. Columns of interest in this table include **TIME, HEART, LAT**, and **LONG**. However, the _POINT_ table is not populated in our image. Another potentially relevant, but unpopulated, database that may hold fine-grained data is the _pedometer.db_ database. This database contains 26 tables, including tables that can record 24-h heart rate data (**rate_24_hour_table_name**), blood pressure data (**blood_pressure_all_data_table**), and temperature data (**temperature_table**). These databases and tables need further investigation in future population and analysis.
## 4 TIR Observations in Asteroid Proximity Phase
### High Resolved Global and Local Thermal Images of Ryugu  
Higher-resolution global and local thermal images of Ryugu were taken by TIR from 5 km altitude several times during the asteroid proximity phase [4; 26], and on the way to the lower altitude down to 1 km altitude for the gravity measurement campaign on 6 to 7 August 2018, and during the hovering operation at 3 km altitude after the release of MASCOT lander on 3 to 4 October 2018 [4].  
A high-resolution global thermal image set of Ryugu was taken on 1 August 2018 during the Mid-Altitude Observation Campaign, from the altitude of 5 km, at the solar distance of 1.056 au, and from the SPE angle of 19.0\({}^{\circ}\). Figure 4 shows a thermal image at rotation phase of 120\({}^{\circ}\)E. Its spatial resolution is 4.5 m per pixel. In this image, surface geologic features such as large boulders are distinguished from the surroundings more clearly than in the images at the home position. Large boulders of tens of meters scale are clearly identified one by one in the thermal images and their temperatures are almost the same as the surroundings. Despite temperature variation for each of the boulders, craters, or other geologic features, no \"hot spots\" nor \"cold spots\" are apparently identified in the thermal images of this spatial resolution.  
Thermal images show almost homogeneous temperature distribution on a boulder whose surface has both an apparently sedimented area and an apparently non-sedimented bare rock surface area, indicating that the sediments have the same thermal inertia with the surroundings. This implies that the sediments should not be fine regolith made of soils and granules but probably fragments of porous boulders with the same thermal inertia as the huge boulders, whose size must be larger than the thermal skin depth. [20; 26].  
Figure 3: Pseudo-color oblique thermal image of Ryugu on 31 August 2018 taken by TIR from the dusk side (SPE angle of 42\({}^{\circ}\)). The largest crater Uroshima (diameter of 270 m) is seen at the equator, and the largest boulder Othomie is seen at the south pole. The night region shown as the blue color area in the right end of the asteroid is also seen in the thermal image. (Image: hyb2_tir_20180831_130142_11.fit, ©JAXA).
## 4 Evaluation  
In this section, to verify the effectiveness of the proposed method, a series of objective and subjective evaluation tests are presented. Several aspects are considered in the tests to assess the separation quality including source number, the angle between sources, and the environment.  
NTT [27], as a speech database including various speakers from different countries, has been chosen as the testing database. In addition, all the data are monoreceptors and the energy of all speech in NTT database is the same. Thus, this database is suitable for evaluating the quality of multiple speech object separation methods. For the evaluation in simulated scenarios, all of the test segments are derived from the database. Each test segment representing a speech source is created with a length of 8 s. In order to evaluate the separation quality when different types of multiple speech sources are active simultaneously, a complicated situation where different proportions of male and female speakers are simultaneously talking is considered in this work.  
To evaluate the proposed method in different environments, we used Roomsim [29] to simulate a room measuring \(6.25\times 4.75\times 2.5\) m\({}^{3}\) with different reverberation conditions. The main parameters of the simulated rooms are illustrated in Table 1. The B-format microphone was placed in the center of the room parallel with the \(z\)-axis, and the power of sound sources from different directions was equal in each simulation. It should be noted that the B-format microphone was simulated via Roomsim, and was completed by simulating the recording condition of each channel. In addition, the radius of the cube (radius of the circumscribed circle) is 12 mm. The azimuth and elevation pairs of the four channels (i.e., FLU, FRD, BLD, and BRU) are \(\{(45^{\circ},45^{\circ}),(-45^{\circ},-45^{\circ}),(135^{\circ},-45^{\circ} ),(-135^{\circ},45^{\circ})\}\). For the objective evaluation in real environments, all the test data was recorded in a room measuring \(6.25\times 4.75\times 2.5\) m\({}^{3}\), \(SNR=20\) dB, \(RT60=0.5\) s.  
In addition, the width of TF-analyzed region mentioned in Section 2 is determined by applications. In this work, in order to obtain a most efficient width of the TF-analyzed zone, we took a number of tests and found that the score keeps stable for five different widths: \(\{128,64,32,\text{ and }16\}\). We chose 64 as the width in [21] for \"single-source\" zone detecting, so the width of analyzed TF zone was also set by a constant of 64 considering both efficiency and low computational complexity. Other allocation strategies might improve the quality of individual speech sources or balance the quality amongst all sources, which will be investigated as the future work.
###### Abstract  
This study investigates the thermal dynamics and material behavior involved in the baking process for Lebanese flatbread, focusing on the heat transfer mechanisms, water loss, and dough expansion under high-temperature conditions. Despite previous studies on flatbread baking using impingement or conventional ovens, this work presents the first experimental investigation of the traditional Lebanese flatbread baking process under realistic industrial conditions, specifically using a high-temperature tunnel over with direct flame heating, extremely short baking times (\(\sim\)10-12 s), and peak temperatures reaching \(\sim\)650 \({}^{\circ}\)C, which are essential to achieving the characteristic pocket formation and texture of Lebanese bread. This experimental study characterizes the baking kinetics of traditional Lebanese flatbread, recording mass loss pre- and post-baking, thermal profiles, and dough expansion through real-time temperature measurements and video recordings, providing insights into the dough's thermal response and expansion behavior under high-temperature conditions. A custom-designed instrumented oven with a steel conveyor and a direct flame burner was employed. The dough, prepared following a traditional recipe, was analyzed during the baking process using K-type thermocouples and visual monitoring. Results revealed that Lebanese bread undergoes significant water loss due to high baking temperatures (\(\sim\)650 \({}^{\circ}\)C), leading to rapid crust formation and pocket development. Empirical equations modeling the relationship between baking time, temperature, and expansion were developed with high predictive accuracy. Additionally, an energy analysis revealed that the total energy required to bake Lebanese bread is approximately 667 kJ/kg, with an overall thermal efficiency of only 21%, dropping to 16% when preheating is included. According to previous CFD (Computational Fluid Dynamics) simulations, most heat loss in similar tunnel ovens occurs via the chimney (50%) and oven walls (29%). These findings contribute to understanding the broader thermophysical principles that can be applied to the development of more efficient baking processes for various types of bread. The empirical models developed in this study can be applied to automating and refining the industrial production of Lebanese flatbread, ensuring consistent product quality across different baking environments. Future studies will extend this work to alternative oven designs and dough formulations.  
Lebanese flatbread; baking process; water loss; thermal profiling; expansion; oven design; industrial baking; empirical modeling 2025
## 1 Introduction  
In past years, robotic arms were introduced in several industrial applications, from automated manufacturing to space robotics, with the aim to guarantee a constant level of quality, higher accuracy, and speed, and to work in dangerous environments [1, 2, 3].  
In most of these contexts, robots work together to accomplish the required tasks, interacting with the environment and with each other. The positioner-worker-watcher taxonomy proposed in [4] is useful to catch, in simple terms, the complexity of modern workcells: _positioners_ are in charge of moving and positioning the workpiece, so that _workers_ can interact with it (usually by exerting force on it, such as in drilling, cutting, soldering); _watchers_ carry sensors to make inspection tasks or even generate trajectories for the other robots in case of occlusions and/or unforeseen events. The tasks usually require frequent interactions with the environment, and changes in the environment itself have to involve changes in the robots' behavior. Heterogeneous hyper-flexible robotic workcells are a further development of the concept above, where different types of robots work together to accomplish the desired tasks and provide the system with the necessary flexibility, making it adaptable to different applications without requiring excessive human intervention [5]. Ideally, on the one hand, it should be possible to replace, in no time, the robots with others of a different brand or model while keeping the same control algorithms, or, on the other hand, to assign the same robot with diverse tasks requiring very different control strategies and parameters.  
On the software side, this degree of flexibility can be achieved through a perfect modularization of the manufacturing systems, both in terms of software and hardware, and a consistent middleware of automation modules to connect robots, peripheral devices, and other industrial systems without reprogramming. In this context, the philosophy of the Robot Operating System (ROS) [6], today the de-facto standard for robot programming, is to provide small and simple software control units that can be easily transferred from one robot/application to another, including simulated ones [7]. Its industrial counterpart, ROS-Industrial [8], has extended the main ROS characteristics of modularity, hardware-agnosticity, and software quality to the industrial context, making the design of hyper-flexible robotic workcells a reality.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 3 Satellite-Aided NOMA with Cooperative D2D Communication  
In this section, details on the proposed satellite-aided NOMA with cooperative D2D (SANOCO-D2D) scheme are given. Moreover, in order to better illustrate the SANOCO-D2D technique, a toy network is presented.
## 3 Proposed Method
### Node Collaborative Scheduling Algorithm (NCS)
In the beginning, all sensors are divided into inner or outer areas according to their geographic locations, and they need to confirm which reconstructed subregion they belong to, then all sensor nodes send their current geographic locations to the sink, and the sink selects the nodes that satisfy the coverage rate threshold in each reconstructed subregion according to the NCS to collect data. When a node dies in the reconstructed subregion, the nodes that satisfy the coverage rate threshold will be re-scheduled to work. We call the process of all sensor nodes generating data once to the sink receiving the data as a round, the improved geographic routing protocol, tree-based routing protocol, and the link detection and repair protocol in the algorithm are not carried out in every round, they run frequently will cause a large amount of wasted network energy and accelerate the paralysis of the network, and run too few is difficult to obtain the ideal network performance, they have an optimal number of rounds running value. Figure 6: A schematic of four nodes collaboration. ```
Input: The network parameters Output: The network performance  Network initialization and partitioning // To determine their neighbor nodes and positions  Each sensor node sends its location to sink for each reconstructed subregion // Start NCS process for each node in this reconstructed subregion  Find the five nodes closest to five maximum coverage contribution points to collaborate endfor  The sink notifies these nodes to work for data collection endfor for each sensor node in outer area // Start improved geographic routing process  Enter the algorithm 1 process endfor for each sensor node in inner area // Start tree-based routing process  Sensor node selects nexthop according to geographic routing protocol  All the nodes can connect to the sink, which formulates the tree structure  Sink moves a communication distance and notifies its two-hop nodes  A new tree structure is created endfor for each working node // Start link detection and repair process  Enter the algorithm 2 process endfor while (the network lifetime is not over)  Perform energy calculation and count communication rounds if (a node is dead)  goto NCS process endif endwhile
```  
**Algorithm 3** ERG-NCS algorithm  
Figure 7 demonstrates the principles of link detection and repair and node collaborative scheduling. The link detection and repair scheme allows nodes to clearly obtain their link status and select a reliable node as the next hop, which effectively solves the problem of data transmission caught in an infinite loop. The node collaborative scheduling algorithm makes full use of the spatial correlation between nodes and further reduces the number of working nodes on the basis of the disk-based greedy scheduling algorithm, which reflects the contribution of the proposed algorithm to improve the energy efficiency of the network as well as the reliability of the network. Figure 7: Link detection and repair and node collaborative scheduling principles.