esa-sceva/satcom-chunk-collection huggingface dataset:
# Si Photonics for Practical LiDAR Solutions
## 5 Other Si Photonics Use Cases for LiDAR
### FMCW Coherent Receiver  
The concept of a FMCW LiDAR is introduced in Section 2.1 and is graphically described in Figure 8a. FMCW requires coherent detection which mixes a local oscillator, usually from the same laser source, with reflected light signal in optical domain. A balanced photodiode read by differential amplifiers is often used for 3 dB SNR gain and the prevention of amplifier saturation from ambient photocurrent. A simplified system diagram is shown in Figure 8b. Similar optical coherent detection has been adopted for long haul fiber optic communications for nearly 20 years and a Si photonics solution, which integrates an optical mixer, a balanced photodiode and a local oscillator beam splitter, emerged about 10 years ago [82]. By integrating more elements such as IQ modulators, polarization splitters and combiners, fully integrated Si photonics dual polarization QPSK transceivers were demonstrated [83] and successfully commercialized by startups and incumbent system companies. Such Si photonics coherence receivers can be readily tuned for use in FMCW LiDAR.  
It is conceivable to integrate such coherent detectors with an OPA to form a complete FMCW OPA LiDAR solution, however, there are challenges to make a steered output beam and reflected light spatially coherent for optical mixing. One way to tackle the spatial coherency issue is to use another OPA as a receiver to selectively receive light from the same angle of the steered output beam and mix them in the waveguide [66]. However, this approach limits the receiving optical aperture to the size of the OPA antenna array. This aperture size is often much smaller than that in a traditional receiver optical system using an optical lens therefore the received signal is proportionally smaller leading to a poorer system sensitivity.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure  
### Magnetic Resonance Imaging  
The need to extract biological information from intact biological systems using MRI and fMRI introduced new technologies that allowed the study of human brain activity with greater detail and resolution [32]. MRI offers more tissue contrast possibility than other radiological methodologies, such as computerized tomography. This is possible by properly setting the time in which the Radiofrequency (RF) impulsive excitement is produced and its derived signal is registered. The contrast helps in discriminating different biological tissues and structures. However, it depends on both the longitudinal relax time (T\({}_{1}\)) and transversal relax time (T\({}_{2}^{*}\)) [32] (T\({}_{1}\) is the time necessary to recover 63% of the longitudinal magnetization present before the RF impulse [4; 32]).  
In MRI, high T\({}_{1}\)is necessary to distinguish white matter, gray matter, and cerebrospinal liquid. \(T_{2}\) is the time required for the transversal magnetization vector to decrease at 37% of the maximum of its intensity [4]. In MRI, low T\({}_{2}\) are necessary to distinguish the three above-cited cerebral tissues [4]. As a matter of fact, to perform studies on AD using MRI images, data acquired with high \(T_{1}\) and low \(T_{2}\) are necessary [4]. With these settings, the structural features can be adequately evaluated [4; 32].  
The MRI possibilities are often limited by the achievable SNR and the Contrast-to-Noise Ratio (CNR). Both the SNR and CNR have been shown to increase with the static magnetic field strength \(B_{0}\)[32]. Besides the influence of the magnetic field \(B_{0}\), the SNR is affected by other parameters, such as Time Echo (TE), Time Repetition (TR), Flip Angle (FA), slice thickness, and matrix size [4; 32]. The work of [PERSON] et al. [4] contained further details about these parameters and their influence on SNR. The intensity of the magnetic resonance signal is defined by the tissue volume. Indeed, high tissue volumes produce an intense MR signal, which in turn increases the SNR. However, this MRI measurement involves the so-called partial volume effect, which consequently lowers the image resolution levels [33]. This is a relevant aspects in the elaboration of MRI images. In fact, the poorer SNR of thin slices can be compensated to some extent by increasing the number of acquisitions or by a longer TR [34]. Considering the work of [PERSON] et al. [35], the SNR strictly depends on T\({}_{1}\), T\({}_{2}^{*}\), TR, TE, FA, and the sensitivity of the receiver coil.  
Since this work made use of morphological data arising from MRI, the influence of all the cited parameters [4; 32; 35] was critically considered when patients and images to analyze were selected.
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 2 Integration and Mining of Electricity Market Data
### Electricity Information Data Fusion
#### 2.1.1 The Isolation Forest Algorithm  
Isolation Forest theory belongs to non-parametric testing and unsupervised learning and has been used for detecting anomalous data [28]. The core idea of this theory is to partition the data sample space using random planes. For each subsample space, based on the principle that there is only one type of user in each subsample space, sample attributes are continuously selected to perform subsample space partitioning. In this process, since anomalous data are outliers, the number of splits they undergo is generally less than the number of splits experienced by normal data. In the Isolation Forest algorithm, the path length of outlier values is typically short. When the data density is high, outliers require multiple splits to be found, whereas when the density is low, outliers can be found with just a few simple splits. Therefore, as a normal data point, \(x_{i}\) needs multiple splits to be isolated, while \(x_{0}\) requires few splits.  
Isolation Forest theory mainly includes two steps: training trees and scoring data points. For a training tree, the main steps include the following:  
Firstly, it is assumed that the dataset \(X=\{x_{1}\,\ \ldots\,\ x_{n}\}\), where \(x_{i}\) represents a data point with \(m\) dimensions. \(\eta\) data points are randomly selected as the training subsample set \(\gamma\), which is placed under the root node of the tree \(T_{l,r,h}\). Here, \(h\) denotes the height of the tree, \(l\) is the left node at height \(h\), and \(r\) is the right node at height \(h\).  
Next, a random dimension \(j\) is chosen from the \(m\) dimensions of the data points. Within the current node's dataset, a random split value \(p\) is selected for dimension \(j\), with \(p\) ranging between the maximum and minimum values of dimension \(j\) in the subsample set \(\gamma\). A hyperplane is formed using this split value \(p\), placing data points smaller than \(p\) on the left side \(l\) of the current node and those greater than \(p\) on the right side \(r\), creating two subsample sets \(\gamma_{l,h}\) and \(\gamma_{r,h}\).  
Finally, the steps described above are recursively applied to the datasets of the left and right branches of the node, generating new leaf nodes until the dataset at a leaf node contains only one data point, or the tree height reaches the designated growth height, at which point the splitting stops. To illustrate, in the case of user voltage data, abnormal data timestamps are identified using isolation forest theory detection, followed by an assessment of the existence of abnormal data.
## 4 Empirical Results
### Heterogeneity Impact on Policy Effect  
The PSM-DID estimation shows that China's ETS pilot policy has indeed achieved an ideal \"Porter effect.\" However, for the national ETS that is being gradually established, the impact of interprovincial differences on economic development and industrial development and their impacts on the final effect of the policy are bound to be a major problem. Based on the PSM-DID results, this study constructs a DDD model by introducing dummy variables for the economic development differences and industrial development differences and also studies the mechanism of the effect of heterogeneity of economic and industrial differences on the policy.
###### Abstract  
This work aims at investigating the reduction/oxidation (redox) reaction kinetics on iron oxide pellets under different operating conditions of thermochemical hydrogen storage. In order to reduce the iron oxide pellets (90% Fe\({}_{2}\)O\({}_{3}\), 10% stabilizing cement), hydrogen (H\({}_{2}\)) is applied in different concentrations with nitrogen (N\({}_{2}\)), as a carrier gas, at temperatures between between 700 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, thus simulating the charging phase. The discharge phase is triggered by the flow of a mixture out of steam (H\({}_{2}\)O) and N\({}_{2}\) at different concentrations in the same temperature range, resulting in the oxidizing of the previously reduced pellets. All investigations were carried out in a thermo-gravimetric analyzer (TGA) with a flow rate of 250 mL/min. To describe the obtained kinetic results, a simplified analytical model, based on the linear driving force model, was developed. The investigated iron oxide pellets showed a stable redox performance of 23.8% weight reduction/gain, which corresponds to a volumetric storage density of 2.8 kWh/(L bulk), also after the 29 performed redox cycles. Recalling that there is no H\({}_{2}\) stored during the storage phase but iron, the introduced hydrogen storage technology is deemed very promising for applications in urban areas as day-night or seasonal storage for green hydrogen.  
hydrogen storage; iron/iron oxide; redox reactions; reaction kinetics +
Footnote †: journal: Applied Physics Letters  
1 Laboratory of Sorption Processes, Technical University of Applied Sciences Regensburg, Galgenbergstrasse 30, 93053 Regensburg, Germany; [EMAIL_ADDRESS]  
2 Chair of Regenerative Energy Systems, TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Schuljass 16, 94315 Straubing, Germany; [EMAIL_ADDRESS]
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 5 A Neural-Symbolic Implementation of T-Pattern
### The Inference Engine  
The T-pattern model requires an inference technique in order to derive suitable logical rules. To this end, we identified in CILIOS [12] a promising inference engine for T-pattern. A brief description of CILIOS is provided below.  
CILIOS (Connectionist Inductive Learning and Inter-Ontology Similarities) is a system originally developed to enhance agent collaboration in multi-agent environments (MAS). This system enables the construction of knowledge representations, known as ontologies, that support collaborative decision-making and personalized recommendations.  
The architecture of CILIOS combines connectionist learning techniques with symbolic ontology models. In CILIOS, observed behaviors are translated into logical rules using neural-symbolic networks. Each CILIOS agent can dynamically represent and update its own ontology, which describes the relevant concepts and categories for interaction.  
By integrating ontologies, CILIOS facilitates efficient cooperation, adapts to changes, and continuously improves interaction capabilities, increasing the system's ability to respond to complex contexts like the Web and IoT networks. Furthermore, the system is designed to be flexible and scalable, supporting a broad range of applications where continuous learning and dynamic adaptation are essential.  
In order to understand how CILIOS can be used to extract T-patterns (a complete description can be found in [12]), we briefly describe the underlying idea on which it is conceived. This idea is based on the possibility that symbolic knowledge can be represented by a connectionist system, as a neural network, in order to build an effective learning system. In particular, it is proved that, for each extended logic program \(P\), there exists a feed-forward neural network \(N\) with exactly one hidden layer and semi-linear activation functions, which is equivalent to \(P\) in the sense that \(N\) computes the model of \(P\). In our current application to the IoT described in this paper, a set of T-patterns as, for example, that represented in Equation (2) can be viewed as a logic program \(P\), and it can be derived following the constructive definition provided in [12]. In particular, this construction passes through the construction of a feed-forward neural network that will be trained on the data provided by the smart objects' interactions and that will be then analyzed to be transformed in a logic program representing the T-patterns.
# Correcting Ski Resort Trajectories Extracted from Video
## 2 Perspective Correction
### Problems in Data Extraction  
For the analysis of the collective motion of skiers, we are interested in the individual trajectories. These can be extracted from the videos similar to the techniques used, e.g., in pedestrian dynamics. However, whereas data in the latter case are usually obtained in laboratory conditions where all relevant parameters are known, here, we have to rely on observational data with several unknowns, e.g., the exact geometry, especially the slope of the area, and the distance of the camera to the objects. Furthermore, other corrections might be relevant, e.g., lens corrections. Therefore, we have to use approximations and estimates when correcting the extracted trajectories.  
Figure 1 illustrates the geometry and coordinate systems, with the intention of providing a clearer understanding of the issues related to mapping the video trajectories onto the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Observation** \\ \hline Resolution & \(1920\times 1080\) pixels \\ Frame Rate & 25 fps \\ Motion Blur & Minor inaccuracies in rapid \\  & skier movements \\ Low Contrast & Challenges in detecting skiers in \\  & poor lighting \\ Camera Perspective Variations & Reduced accuracy at field of view edges \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration and limitations of PeTrack software in skiing videos.  
\"real\" trajectories. In the video, the coordinates are represented as \((x^{\prime},y^{\prime})\), given in pixels, while the corrected coordinates in the real-world plane are denoted as \((x,y)\).
## 5 Case Study and Analysis
### Feature Learning and DNN Parameters Setting  
CAE feature training is performed to extract the noise from the original data and improve the robustness of DNN prediction model based on the data sets obtained from the time-domain simulation using PSO. Once the data is normalized, the next step is to divide it into training and validation sets. In this case, 90% of the original data is assigned to the training set, while the remaining 10% is used for validation purposes. These sets are then utilized to create the training data and the validation data.  
The selection of the number of hidden layers and neurons per hidden layer in DNN can impact the accuracy of the model, as well as the time required for offline training and online decision-making in combining optimal combined inertial control parameters. In this paper, the strategy of setting the number of layers and the number of neurons per layer of DNN is layer by layer, increasing in order. The optimal number of neurons in each hidden layer is determined experimentally and kept constant, and then the number of hidden layers is gradually increased until the MSE of the optimal combination of combined inertial control parameters no longer decreases, and the training is stopped.  
According to the above strategy of setting, the optimal configuration for the wind power frequency regulation combined inertial control deep learning neural network in this test system is determined to be three hidden layers, with 6, 12, and 6 neurons in each respective layer.
# A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
## 3 Prototype Development
### System Program Process  
The entire system operates as shown in Figure 10:  
1. Node 1 measures the room temperature and then sends the temperature information to node 2 via synchronous communication;
2. Node 2, after receiving information on the room temperature, measures the light level on the working surface near the window, followed by inputting the temperature and illumination information into the fuzzy controller;
3. After obtaining the angle of the blinds from the fuzzy controller, the output angle of the blinds is optimized through Equation (9), and then Raspberry Pi (node 2) sends out the corresponding pulse wave through the pins to drive the servo to rotate the blinds and change the amount of natural light radiation, completing the first indoor illumination adjustment;
4. Node 1 and node 2 measure the illuminance value of the working surface simultaneously; then, node 2 sends the illuminance information to node 1 via synchronous communication;
5. After receiving the illuminance information from node 2, node 1 applies Equation (5) to calculate the illuminance value generated by natural light at each measurement point based on the illuminance measured by the sensor and the information about the luminaire turn-on level;
6. The natural illuminance value at each measurement point is used as input to obtain the optimum opening of the luminaire by means of the DDPG algorithm;
7. The LED is then controlled by the PWM wave output from the corresponding pin of Raspberry Pi (node 1), thus achieving a second adjustment of the room illumination.  
Figure 9: The hardware configuration for the prototype chamber.  
Throughout the process, the synchronous communication process means that all nodes simultaneously send relevant information to all their neighboring nodes, and then each node filters and uses the received information according to its own task, thus enabling the flow of information. Synchronous communication is a block of code already integrated in this open-source distributed platform.  
To prevent overshoot leading to instability in the blind control process, the system uses half of the difference between the fuzzy controller output and the current blinds' angle as the new change, as well as the sum of the current blinds' angle and the new change as the new value of the blinds' angle, as shown in Equation (9). After a series of minor adjustments, the blinds will eventually remain at the optimal angle without instability, such as opening and closing fluctuations. Even after multiple adjustments, the blinds' real-time performance is not significantly affected by the 1-s interval between each adjustment.  
\[B_{new}=[(C_{fuzzy}-B_{current})/2]+B_{current} \tag{9}\]  
In Equation (9), \(B_{new}\) is the latest value of the blinds' angle, \(C_{fuzzy}\) is the angle value output by the fuzzy controller, and \(B_{current}\) is the current angle value of the blind.
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 2 Proposed Method  
As shown in Figure 1, the retinal vessel segmentation framework proposed in this paper is divided into two stages, namely training stage and testing stage. The overall process are as follows:  
* Data preprocessing: The original retinal blood vessel image cannot be effectively segmented due to factors such as uneven illumination, low noise, and low contrast. Therefore, image preprocessing is required before training to ensure the maximum possible increase in contrast between the retinal blood vessels and the background, thereby effectively improving the segmentation effect.
* Data expansion: Supervised segmentation training requires manual segmentation of images as labels, so data acquisition is difficult, resulting in insufficient training data for deep learning training. To solve this problem, we randomly divided each preprocessed image data into many smaller image blocks to achieve the purpose of expanding the data set.
* Model training: The image block is divided into a training set and validation set according to the ratio of 9:1. The training set is used as the input of U-Net model, and the corresponding gold standard image block is used as the label. The gradient descent method is used to train the model. According to the training effect of the training set and validation set, the model is continuously optimized until the model reaches the optimum.
* Effect test: Firstly, preprocess the test data in the same way; secondly, orderly segment the test image; then, input the segmented image blocks into the trained U-Net model to obtain the corresponding segmentation results; and finally, the segmentation results of each image block are combined to obtain a complete retinal blood vessel segmentation image.
# A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique
## 3 Technical Background  
### AE Monitoring of Reinforced Concrete (RC) Structures  
A common way to monitor AE is observing the structural degradation. If a crack takes place, a stress wave is generated, and it is regarded as an AE source. Due to the stress wave, the resulting stimulus acts upon the specimen material and creates a local plastic deformation [35]. After that, the stress wave flows starting from the source to the surface of the structure and is recorded by an AE sensor. The role of sensors is important for an AE system, since it converts the stress wave to an electrical signal [35]. Signal amplification is necessary prior to transmission given the fact that the sensor is nonintegral. Integral sensors have embedded amplifiers. The wave is directly transferred to the AE instrument for these sensors. Next, the wave is transmitted for data acquisition in order to perform recording, storing, and analysis. A basic pictorial view of the AE data acquisition for a reinforced concrete beam is presented in Figure 1.  
It is quite challenging to perform AE monitoring for composite materials like RC beams because of the attenuation effect. RC beams are heterogeneous materials consisting of cement, coarse aggregates, fine aggregates, and steel bars as reinforcement. Additionally, it is full of flows like pores, air voids, etc. Therefore, a suitable sensor with a lower resonant frequency and appropriate frequency range should be chosen.