esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cooling System Design  
Current research in the field of electronics cooling is constantly looking for new ways to reduce the heat load of electrical components and thus improve their functionality and extend their lifetime. In view of all these criteria and requirements, the present article deals with the cooling of the interior of electrical enclosures by a gravity loop thermosiphon. The cooling system consists of two main parts, namely the evaporator and the condenser. The cooling process works on the principle of evaporation of the working substance in the evaporator so that only heat is dissipated from the internal space of the electrical equipment. In the condenser, the steam condenses, so that the heat is transferred to the outside. In our experimental measuring device, the evaporator is placed inside the cabinet at the back and the condenser is located above the cabinet construction. The heat dissipation to the ambient is carried out by forced convection on the steam-to-air heat exchanger.  
The cooling system was designed for the real type of electric enclosure of energy converters. The main idea of the cooling system design was a dustproof electric enclosure. Therefore, a gravity loop heat pipe was used to dissipate heat from the electrical enclosure. The construction of the enclosure was supplemented with seals so that dust particles from the external environment could not leak into the indoor environment. The cooling system consisted of three basic parts: an evaporator, condenser, and working medium. The evaporator of the cooling system is located on the rear wall inside the cabinet, and the condenser is located above the cabinet. The evaporator and the condenser are connected by a pipeline through the working substance flows. The distance between evaporator and condenser is 30 cm. There is a heat transport working medium inside the cooling device. Heat transfer from the evaporator to the condenser occurs due to phase changes of the working fluid.
## 2 Description of a FEM Model of the BEAM structure  
The model used is formed by a straight beam consisting of BEAM3 elements, while attached to the ground by three springs (longitudinal \(k_{l}=89\times 10^{5}\) Nm\({}^{-1}\), transverse \(k_{t}=89\times 10^{5}\) Nm\({}^{-1}\), tilting \(k_{T}=5\times 10^{5}\) Nm rad\({}^{-1}\)) at one end and free at the other end. Its dimensions, as well as the region that simulates the local decrease in stiffness of length \(\Delta x_{D}=30\) mm, are shown in Figure 1.  
Damage is simulated by local reduction in the Young's modulus of elasticity to match the equivalent change in beam diameter, so inertial properties of the prismatic beam are maintained. The modified [PERSON]'s modulus \(E_{D}\) results from the comparison of the changed stiffness \(k_{D}\) through the modification of the quadratic moment of area and the modification through the change in the [PERSON]'s modulus of elasticity. Equation (2) states that by keeping the original [PERSON]'s modulus \(E\), but changing the squared moment of the cross-section area \(J_{D}\) (non-prismatic shape), the same stiffness can be obtained as by keeping the original squared moment of the area \(J\), but changing the [PERSON]'s modulus of elasticity \(E_{D}\) (prismatic shape). For the quadratic moment of the area of the circular cross-section of the beam, the following applies [24]:  
\[J=\frac{\pi d^{4}}{64}\ ;\ J_{D}=\frac{\pi(d-\Delta d)^{4}}{64} \tag{1}\]  
\[k_{D}\approx\frac{E_{D}}{\Delta x_{D}^{3}}=\frac{E_{D}J}{\Delta x_{D}^{3}} \tag{2}\]  
Figure 1: Model of a beam fixed on one end and free on the other end. Fixation is simulated by the spring system using a COMBIN40 element in ANSYS. All dimensions are in mm.  
Since the quadratic moment of the area is related to the geometry of the beam and the Young's modulus is only a material property, it is possible for simulation purposes, to consider the damage as a region with the Young's modulus \(E_{D}\), which is equivalent to the reduction in the diameter \(\Delta d\). \(E_{D}\) in Equation (3) is derived from Equations (1) and (2).  
\[E_{D}=\frac{\left(d-\Delta d\right)^{4}}{d^{4}}E \tag{3}\]  
Relative damage is defined as  
\[\Delta d_{r}=\frac{\Delta d}{d} \tag{4}\]
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 3 Code-Frequency Two-Stage Compression Acquisition  
In this section, we introduce a new frequency domain compression to preprocess the incoming signal to reduce the influence of large Doppler frequency offset.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 4 System Design and Implementation
### System Deployment
#### 4.2.3 Communication Layer  
The communication layer facilitated efficient data transfer and interaction between different components of the system. It consisted of the following components:  
1. MQTT Broker: Utilizing the MQTT protocol, the MQTT broker served as a messaging intermediary between the edge computing unit (RPi 4) and the mobile unit. It ensured reliable and low-latency communication, allowing the RPi 4 to send warnings and accident notifications, receive trip instructions, and synchronize data with the mobile unit.
2. Standard HTTP Requests: HTTP requests were employed to facilitate the communication between the servers and the edge computing unit. They facilitated the exchange in vehicle, sensor, rides, and user data between the different system modules.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 5 Research Findings  
Automated construction progress monitoring can reduce schedule delay, enhance information visualization, and assist decision-making. In the past few years, the construction team has used various simulation tools to track construction progress. Unfortunately, while some paperless construction planning and tracking tools are available today, many construction companies do not use them. Because of the threat of cost, time, or complexity, construction companies around the world are putting digital and mobile strategies on the back burner and sticking to their old technologies. Compared with other 3D reconstruction methods, image-based 3D reconstruction seems to be a more critical and feasible technology, despite there are some challenges. Here are summaries of the uniqueness of this technology:  
1. Image has more advantages than other forms of data. There are various types of automatic acquisition technologies, which can be roughly divided into Enhanced IT technologies, Geospatial technologies, Imaging technologies, and Augmented reality [20]. However, the imaging technologies, especially photography and video shooting, have the advantages of intuitive, rich information, accurate, low cost, and low technical requirements, which is congenitally advantageous to be accepted by construction companies.
2. Easy and cheap access to massive image data. The acquisition of reliable data is supported by the development of hardware, including camera, monitor, storage device, smartphone, UAV, etc. Daily images can not only be collected systematically, but also recorded by workers on site, due to the diffusion of devices with built-in cameras. Abundant and sufficient data means that enough information can be extracted in theory, while information extraction is up to the software.
3. Rapid development of software technology. The booming new image processing technologies, especially the ones based on deep learning, have been fully and deeply applied in biomedicine, aerospace, transportation, public security, and other industries. However, in the field of AEC, the research and application of these technologies are still in its infancy.
4. Most of the research is based on the point cloud, rather than the image itself. The methods without point cloud are still worth exploring, for example, VR-based registration and object detection-based reconstruction.
5. New technologies that can be combined with image-based 3D reconstruction have emerged. With the vigorous development of hardware, software, algorithms, and data in computers and related industries, various new technologies have emerged. These technologies have made huge breakthroughs and are sought after by researchers. Many scholars have begun to integrate these emerging technologies with existing 3D reconstruction technologies and have achieved amazing results.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Optical Structure of the Dual-Gratings Type Imaging Spectrometer  
The optical path of the dual-gratings imaging spectrometer is shown in Figure 9. The object surface is a slit.The light is converted into collimated light by the reflector, and after being dispersed by the double gratings, the two bands will be imaged in two adjacent areas. The dual-gratings are arranged in parallel along the reticle direction and can be used with suitable filters.On the image plane, the image has two dimensions, one is the spectral dimension and the other is the spatial dimension. The arrangement of the double grating is parallel to the space dimension, and the reticle is also parallel to the space dimension. This structure provides the following advantages: 1. Since the two gratings can be rotated independently, as long as the detector has sufficient response to the bands, two very far apart bands can be selected. 2. According to the needs of the signal-to-noise ratio, the gratings with different line pairs can be chosen to obtain two spectra with different spectral resolutions. In this system, to reduce the spherical aberrations, a parabolic mirror can be used to replace the traditional spherical mirror.  
As introduced above, gratings with different line log numbers can be used according to the requirements of spectral resolution and signal-to-noise ratio. Figure 9 uses two gratings, 400 L/mm and 600 L/mm, to image the two wavebands 500 \(\pm\) 20 nm and 400 \(\pm\) 15 nm respectively. Since the number of line pairs per millimeter of the two gratings are different, the angle between the two gratings is small.
## 3 Simulation Results
### Simulation Results According to NLoS Probability  
Figure 3 illustrates the impact of the NLoS probability on the EDM and localization errors. It is assumed that the NLoS probability follows a Bernoulli distribution. As the NLoS probability increases, the error for distance measurements naturally increases, resulting in a worse localization performance. Therefore, the non-NN-based techniques are in line with this general situation. On the other hand, a different phenomenon appears from the information-theoretic perspective. In the NN-based methods, i.e., deep-learning-based methods A and B, DAE-EDMR, and T-DAE-EDMR, the EDM error takes the form of a concave function for the NLoS probability. This is related to the fact that the variance in and the entropy of \(\text{Ber}(p)\) are \(p(1-p)\) and \(-p\log(p)-(1-p)\log(1-p)\), respectively, which are in the form of concave functions. Interestingly, our proposed methods show a superior localization performance in the extreme case where the NLoS probability is 1 and can therefore be effectively applied in environments where NLoS conditions occur frequently. For future work, an interesting denoising task may involve modeling the distance measurement error due to NLoS conditions as a joint random variable, considering both the event occurrence and distance biasing. This approach could provide valuable insights into further enhancing the denoising techniques in the presence of errors due to NLoS conditions.
## 4 Channel Estimation Based on Deep Learning for RIS-Aided Communication Systems
### Application of Deep Learning in Channel Estimation for RIS-Aided mmWave Systems
In particular, they used two DnCNNs to find the row and column supports and then the LS algorithm to estimate the angular cascade channel. In contrast to the DD-FF-CE, the DD-FS-CE also utilizes the support information from each subcarrier to cope with frequency selectivity. In the experiments, these two schemes exhibit excellent NMSE performance and low computational complexity while having less pilot overhead.
## 3 Comprehensive Data Workflow
### Data Processing
#### 3.3.2 Data Cleaning  
The dataset initially contained several extraneous columns; therefore, we retained only the three columns corresponding to the responses to the questions asked. Following this, we launched the data cleaning process. After a thorough review of all the data, we defined the criteria and guidelines for the data cleansing procedures.  
When we asked the questions, we provided users with a number of suggestions to make their answers easier. Unfortunately, some users referred to our suggestions using numerical values in their answers. As a result, we systematically cross-referenced and replaced these numerical entries with the corresponding alphabetical values to ensure data consistency and accuracy.  
We initially aimed to collect responses in the Moroccan dialect using Arabic script. However, some users, lacking an Arabic keyboard, opted to respond using Latin letters to represent Arabic words. Our objective was to convert these entries into Moroccan dialect expressions rendered in Arabic characters, ensuring the preservation of their original meaning.  
Additionally, we identified irrelevant responses, prompting us to either remove them or invest time in generating contextually appropriate answers that aligned with the users' intended expressions. We also encountered several unanswered entries, which we subsequently deleted.  
Furthermore, we removed emojis, as they did not contribute to meaningful textual data. Some sentences exhibited an inaccurate structure due to reversed meaning, necessitating corrective measures. A meticulous review also revealed spelling errors; rather than discarding these responses, we saved them in a separate sheet for future use in testing the model's performance and refining the dataset. Next, we eliminated special characters, replacing them with blank spaces. Finally, we removed all unnecessary spaces to ensure data uniformity [20; 21].
###### Abstract
This has required the introduction of optimization algorithms able to deal with stringent latency, high density, and big data. With this in mind, in this paper, we focus our attention on two main elements. On one side, AO, a novel concept that arises from the optimization algorithm parallelization, which allows us to reduce the time required to reach the problem solution. On the other side, there is recent momentum in ML algorithms, mainly due to their ability to solve complex problems; however, when several actors come into the system, traditional ML is unable to scale effectively--this is why we discuss distributed machine learning, a novel concept where traditional ML is considered to be distributed on several elements of the system. This paper discusses, in detail, the challenges associated with IoV and how parallelization in optimization algorithms allow for the re-optimization of systems in just a few seconds. The rest of the paper is organized as follows: In Section 2, the main concepts related to IoV scenario and use cases identified by the standardization organizations are introduced. Section 3 reviews some related work on IoV, by focusing in particular on the optimization problems. Section 4 describes in more detail how AO algorithms work. Section 5 focuses on the DML algorithms and their application to IoV scenarios. Section 6 reviews some of the most popular vehicular networking models and provides a numerical analysis of different applications of AO algorithms. Finally, Section 7 summarizes the main contributions of the paper and provides future research lines.
## 2 Methodology
### Siamese Encoder-Decoder Structure  
CNX Encoder: Utilizing a hierarchical, pre-trained ConvNeXt (CNX) network [38], the encoder extracts multi-level features \(\{F_{E1},F_{E2},F_{E3},F_{E4}\}\) from input image pairs \(\{T^{1},T^{2}\}\in\mathbb{R}^{\mathcal{C}\times H\times W}\), as defined in Equation (2). The resulting top-down encoding, within a Siamese structure, efficiently captures local features and streamlines the CD process.  
In Figure 3a, each backbone feature level consists of a downsampling layer (with multipliers \(n\in\{4,2,2,2\}\)) and multiple basic blocks; LN denotes Layer Normalization:  
\[F_{DI}^{\prime}=\text{LN}(\text{Conv}^{(n,n)})(F_{DI}),\ l\in\{1,2,3,4\} \tag{10}\]  
where \(F_{l}^{\prime}\in\mathbb{R}^{\mathcal{C}\times\frac{H}{n}\times\frac{W}{n}}\), Conv superscript \"\((n,n)\)\" indicates the kernel size and stride of the Convolution (Conv) operation, respectively. The CNX block computation is as follows:  
\[\text{CNX}(F)=\text{Conv}(\text{GELU}(\text{Conv}(\text{LN}(\text{Conv}^{(7) }(F))))\oplus F \tag{11}\]  
\[F_{D(l+1)}=\text{CNX}_{(m)}(F_{DI}^{\prime}),m\in\{3,3,9,3\} \tag{12}\]  
where \(m\) is the per-stage CNX repetition count. Downsampling of \(F_{DI}^{\prime}\) followed by \(m\) CNX computations yields the input features for stage \(F_{l+1}\). Superscript \({}^{(7)}\) denotes a \(7\times 7\) Conv, while the absence of a superscript indicates a \(1\times 1\) Conv. The two \(1\times 1\) Convs expand the channel dimension by a factor of 4 and then recover it.GELU [39] is an adaptive activation function. \"\(\oplus\)\" denotes element-wise addition.  
Unified Decoder and Detector (UDD): The UDD module, illustrated in Figure 3b, combines compressed multi-level feature representations with decoder outputs, using skip connections to the encoder for improved gradient flow. The UDD comprises a UDD Squeeze unit (UDDSqz) and a UDD Residual unit (UDDRes), both composed of several Basic Units (BU): Conv \(\rightarrow\) BN \(\rightarrow\) GELU:  
\[\text{UDD}(\dots)=\text{UDDRes}(\text{UDDSqz}(\dots)) \tag{13}\]\[F^{\prime}_{DI}=\text{UDDSqz}(\dots)=\text{BU}_{(l)}(\dots) \tag{14}\]  
\[F_{DI}=\text{UDDRes}(\dots)=\text{BU}_{(l)}(\text{BU}_{(l)}(F^{\prime}_{DI})) \oplus F^{\prime}_{DI} \tag{15}\]  
where Equation (14) utilizes a \(1\times 1\) Conv. Equation (15) employs \(3\times 3\), \(1\times 1\), respectively.  
Decoder and Detector: The decoder collects outputs from the encoder and utilizes a UDD module at each layer. The multi-layer outputs then serve as input for the detector, which receives \(F_{D1},F_{D2},F_{D3},F_{D4}\) from the decoder. As depicted in Figure 2, the features are interpolated to the same shape and Equation (13) is applied twice. Finally, a \(1\times 1\) Conv to generates the change _Mask_:  
\[Mask=\text{Softmax}(\text{Conv}(\text{UDD}(\text{UDD}(F_{D1},F_{D2},F_{D3},F_{ D4})))) \tag{16}\]
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Path Prediction Training
#### 2.5.3 Degree Component  
The objective of \(\mathcal{L}_{\text{degree}}\) is to enforce structural constraints by penalizing predictions based on node degree values. This component ensures that the predicted graph contains no branches. For \(\hat{Y}\), the predicted adjacency matrix, let \(a_{i}^{\text{out}}=\sum_{j}(\hat{Y}_{ij})\) be the out-degree of every node \(i\) and \(a_{i}^{\text{in}}=\sum_{j}(\hat{Y}_{ji})\) be the in-degree of every node \(i\). We also define \(\mathcal{A}=\{i\mid d_{i}^{\text{in}}+a_{i}^{\text{out}}>0\}\), where \(\mathcal{A}\) is the active set of non-isolated nodes. To define \(\mathcal{L}_{\text{degree}}\), we compute:  
\[\begin{split} P_{\text{start}}&=(\sum_{i\in| \mathcal{A}|}1[d_{i}^{\text{in}}=0]-1)^{2}\\ P_{\text{end}}&=(\sum_{i\in|\mathcal{A}|}1[d_{i}^ {\text{out}}=0]-1)^{2}\\ P_{\text{int}}&=(\sum_{i\in|\mathcal{A}|}1[d_{i}^ {\text{in}}\
eq 1\lor d_{i}^{\text{out}}\
eq 1])-2\end{split} \tag{8}\]  
The three intermediary components penalize the network when it predicts a graph which contains **(a)** multiple potential start nodes (\(P_{\text{start}}\)) or **(b)** multiple end nodes (\(P_{\text{end}}\)) or **(c)** when intermediary nodes do not have exactly one incoming edge and one outgoing edge (\(P_{\text{int}}\)). We average over batch size to compute the degree component, written as follows:  
\[\mathcal{L}_{\text{degree}}=\frac{1}{N}\sum_{n=1}^{N}(P_{\text{start}}+P_{ \text{end}}+P_{\text{int}}) \tag{9}\]  
The degree component ensures that the predicted graph follows a structured, single-path format without branches. This is achieved by analyzing the _in-degree_ and _out-degree_ of each node in the active set, which represent the number of incoming and outgoing connections, respectively. If multiple nodes are identified as potential starting points (having no incoming edges) or multiple nodes are seen as ending points (having no outgoing edges), the model is penalized, and parameters are updated accordingly. Additionally, intermediary nodes should have exactly one incoming and one outgoing connection to maintain a proper sequence. The loss function sums up these penalties across all graphs in a batch, ensuring the network consistently learns to predict non-branching paths.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 2 Theoretical Analysis
### Simplified Diffuser-Induced Engine Order Excitation  
The excitation generated from the impeller-diffuser interaction is the main excitation acting on the impeller. The excitation frequency is proportional to the engine speed and the spatial nature of the excitation is in the form of a traveling wave [17; 39]. The unsteady force generated from the interaction is mainly acting near the trailing edges of blades [25; 27]. According to the Fourier analysis of the static pressure distribution at the impeller exit, the first harmonic pressure is dominant and the higher order pressures attenuate rapidly [40]. Thus, we can only use the first harmonic pressure in forced response calculation as a kind of simplification [27; 41]. Unlike the conventionally designed impellers, the amplitude of the aerodynamic force acting on the main blades is usually higher than that on the splitter blades [25; 26; 27].  
Suppose the \(j\)th blade and the \((j+1)\)th blade represent the main blade and the splitter blade of the \(i\)th sector, respectively, then the aerodynamic force acting on them can be written in the following form:  
\[\left\{\begin{array}{l}f_{j}=F_{MB}\cos(n\Omega t+n\Delta\theta j)\\ f_{j+1}=F_{SB}\cos(n\Omega t+n\Delta\theta(j+1))\end{array}\right. \tag{19}\]  
in which, \(F_{MB}\) and \(F_{SB}\) are amplitudes, \(n\) is the engine order of excitation, \(\Omega\) is rotating speed of the impeller, \(j=2i-1\) and \(i=1,2,\ldots,N\) is sector index and all blades are equally spaced by the angle \(\Delta\theta=2\pi/2N\).