esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture
* Cloud communication manager: this element manages all the sub-modules in charge of communicating with the specified cloud service. Like the other managers, it never handles data messages but only communicates with adjacent modules using control messages. * Cloud module processor(s): those components collect all the data messages coming from sidecar processors, eventually perform an additional processing (defined by specific policies), and then forward the result to one of the cloud module publishers; in most cases there is one loud module processor for each topic of interest. Like other operational modules it communicates only with the manager that coordinates its behavior, which in this case is the cloud module manager. * Cloud module publishers: those modules publish all the messages coming from the cloud module processors to the cloud services, and their implementation strictly depends on the scenario they are operating in. They also have the fundamental function of handling all the mechanisms inherent to the recovery of data messages in case of errors or faults (as will be detailed in the following sections); they are directly coordinated by the cloud comm manager. * Cloud subscriber: this \"operational\" component has the task of subscribing to all the cloud topics specified in the sidecar object configuration, forwarding the incoming messages to the designated sidecar processor performing little or no processing on the aforesaid messages. It never has sub-modules, and is directly coordinated by the cloud comm manager using control messages.
## 4 LC-Based SIW Phase Shifter Design  
### Structure  
We designed the LC-based SIW phase shifter with IPs that operate at 28 GHz. Inserting the IPs increases the cut-off frequency by reducing the effective width of the SIW, so the SIW before the insertion of the IPs was designed to have a cut-off frequency of 15 GHz with  
Figure 2: Inductive post inserted into an SIW: (**a**) top view of one inductive post inserted in SIW and (**b**) its equivalent circuit.  
a large enough margin. The 15 GHz cut-off frequency means that we can send our signal to another port entirely with phase changing while propagating.  
The proposed LC-based SIW phase shifter consists of five layers (Figure 3). Layers 1 and 5 are respectively the upper and lower metal planes of the SIW and are electrically connected through the metalized holes. The metal planes are copper and are 7 \(\upmu\)m thick. Layers 2, 3 and 4 are composed of Roger 4003C (\(\varepsilon_{r}\) = 3.38, tan\(\delta\) = 0.0021) as dielectric substrates. Designed thicknesses were 0.1 mm for layers 2 and 4 and 0.5 mm for layer 3. The LC was set inside a cavity (3.5 mm \(\times\) 10 mm, 0.5 mm high) in layer 3. The transition from grounded coplanar waveguide (GCPW) to SIW occurs at the end of the phase shifter for the integration applications of SIW [26; 27]. The GCPW was designed to have an impedance of 50 \(\Omega\)[28; 29]. Then, IPs with a radius \(r\) = 0.1 mm were placed with offset \(p\) = 0.9 mm to increase \(\Delta\phi\). The dimensions of \(r\) and \(p\) were chosen to optimize the trade-off between the insertion loss and the \(\Delta\phi\) that the posts induce. The IPs were located in the middle of the metalized holes of the SIW and continuously arranged at intervals \(s\), as shown in Figure 4.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design
### Terminal Tier  
Each UAV is equipped with a flight control unit (FCU), terminal processor, and communication module. The FCU acquires real-time state data including position, velocity, and attitude. The terminal processor handles mission-specific data processing, such as preliminary analysis of camera-captured video streams. Both processed results and raw data are then transmitted in real-time through the communication module to edge and cloud servers for subsequent management, analytical operations, and decision-making optimization. In this paper, we leverage the proposed cloud-edge-end architecture to offload computational tasks from UAVs to ground-based servers, thereby achieving a substantial reduction in computational energy consumption and enhanced operational endurance for UAVs. The communication terminal weighs 3 kg and measures 220 \(\times\) 135 \(\times\) 66 mm. It includes a Jetson Orin NX module with 25 W power consumption and 100 Tops AI inference capacity.
## 1 Iterative Methods for Solving Quadratic Equations Presented in [1]  
According to Kung-Traub conjecture (KTC) [2], an iterative method without memory for solving nonlinear equations in the case of simple zeros, could achieve a maximum convergence order of \(2^{d-1}\), where \(d\) is the number of function evaluations. Recently, an article was published for solving quadratic equations [1] with arbitrary order of convergence by using one function and two derivatives evaluations. The details of the proposed formulation in [1] is given as follows. Let \(f(x)=\kappa_{2}\,x^{2}+\kappa_{1}\,x+\kappa_{0}\) be a quadratic function where \(\kappa_{2}\
eq 0\) and \(\kappa_{1}\), \(\kappa_{0}\) are constants. The proposed iteration function \(\psi_{(r+2)^{\text{th}}\text{BQIM}}(x)\) in [1] is  
\[\left\{\begin{array}{l}u(x)=\frac{f(x)}{f^{\prime}(x)}\\ \tau=\frac{f^{\prime}(x-2/3u(x))}{f^{\prime}(x)}\\ H(\tau,r)=1+\sum\limits_{i=1}^{r}a_{i}\,(\tau-1)^{i}\\ \psi_{(r+2)^{\text{th}}\text{BQIM}}(x)=x-u(x)\,H(\tau,r)\end{array}\right. \tag{1}\]  
The error equation of Equation (1) is given as \(\psi_{(r+2)^{\text{th}}\text{BQIM}}(x)-x^{*}=C_{r}\left(c_{2}^{r+1}\right) \left(e^{(k)}\right)^{r+2}+O\left(\left(e^{(k)}\right)^{r+3}\right)\) where \(C_{r}\) is asymptotic error constant, \(x^{*}\) is the simple root of quadratic equation and \(c_{2}=1/2f^{\prime}(x^{*})^{-1}f^{\prime\prime}(x^{*})\). The error equation clearly shows that KTC is not valid in the case of quadratic equations. By using binomial expansion, the weight function \(H(\tau,r)\) can be written as  
\[\begin{split} H(\tau,r)&=1+\sum\limits_{i=1}^{r}\sum \limits_{j=0}^{i}a_{i}\,\binom{i}{i-j}(-1)^{(i-j)}\,\tau^{j}\\ H(\tau,r)&=1+\sum\limits_{i=0}^{r}b_{i}\,\tau^{i} \end{split} \tag{2}\]  
where \(b_{i}\) is constant and can be computed by comparing two expressions of \(H(\tau,r)\) in Equation (2). The powers of \(\tau\) can be computed recursively and hence the iteration function Equation (1) is written as  
\[\left\{\begin{array}{l}u(x)=\frac{f(x)}{f^{\prime}(x)}\\ y=x-2/3\,u(x)\\ \tau=\frac{f^{\prime}(y)}{f^{\prime}(x)}\\ \phi_{0}=u(x)\\ \text{for}\;\;i=1,r\\ \phi_{i}=\tau\,\phi_{i-1}\\ \text{end}\\ \psi_{(r+2)^{\text{th}}\text{BQIM}}(x)=x-\phi_{0}-\sum\limits_{i=0}^{r}b_{i}\, \phi_{i}\,.\end{array}\right. \tag{3}\]  
The computationally efficient vector version of iteration function Equation (3) is  
\[\left\{\begin{array}{l}\mathbf{F}^{\prime}(\mathbf{x})\,\boldsymbol{\phi}_{0 }=\mathbf{F}(\mathbf{x})\\ \mathbf{y}=\mathbf{x}-2/3\,\boldsymbol{\phi}_{0}\\ \text{for}\;\;i=1,r\\ \mathbf{F}^{\prime}(\mathbf{x})\,\boldsymbol{\phi}_{i}=\mathbf{F}^{\prime}( \mathbf{y})\,\boldsymbol{\phi}_{i-1}\\ \text{end}\\ \boldsymbol{\psi}_{(r+2)^{\text{th}}\text{BQIM}}(\mathbf{x})=\mathbf{x}- \boldsymbol{\phi}_{0}-\sum\limits_{i=0}^{r}b_{i}\,\boldsymbol{\phi}_{i}\,. \end{array}\right. \tag{4}\]
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 4 Experiment and Discussion
### Experiment Setting
#### 4.1.3 Evaluation Metrics  
For labeled datasets, accuracy and the Fowlkes-Mallows index (FM index) were used for evaluation. The accuracy metric aligns the predicted labels with the ground truth usingthe Hungarian algorithm and computes the proportion of correctly classified samples. The FM index assesses the alignment between the clustering outcomes and the actual labels, with a scale from 0 to 1. A higher value indicates that the results are closer to the true labels, thus representing better clustering performance.  
For unlabeled datasets, performance evaluation primarily relies on internal clustering metrics. In this study, we used the silhouette coefficient, the Calinski-Harabasz (CH) index, and the Davies-Bouldin (DB) index as evaluation metrics. These three indices are able to measure the closeness and separation of clusters in order to assess the intrinsic quality of the cluster results. The value of the silhouette coefficient ranges from \(-1\) to \(1\), with higher values indicating better clustering performance. It quantifies the degree of cluster definition by calculating the average distance between each sample and other points within the same cluster, as well as the average distance to the nearest points in different clusters. The CH index is used to measure the balance between cluster separation and the compactness within clusters, while the DB index is used to assess the proximity between each cluster and its nearest neighbor. A higher CH index indicates better clustering, while a lower DB index indicates better separation and better clustering performance.
## 2 Mathematical Model
### Computational Geometry, Domain, and Grid  
There are great differences in the structure and size of the various kinds of upper vents, but pollutant emission can be roughly classified as vertical discharge and horizontal discharge. The computational fluid dynamics (CFD) model constructed above was used to simulate the diffusion movements of the pollutants, which were discharged vertically and horizontally from the upper vents. The area of the vertical outlet was 4 m\(\times\)4 m, and that of the horizontal outlet was 2 m \(\times\) 2 m (four sides), as shown in Figure 1.  
The fluid computational domains outside the upper vents were 200 m \(\times\) 200 m \(\times\) 100 m (length \(\times\) width \(\times\) height) (pollutant discharged vertically) and 400 m \(\times\) 400 m \(\times\) 50 m (length \(\times\) width \(\times\) height) (pollutant discharged horizontally). A hexahedral grid was used to subdivide the whole computational domain. To accurately simulate the jet effect of the air flow at the upper vents and to ensure computational efficiency, accuracy, and reliability, smaller grids were put around the upper vents, while larger grids were placed at the outer edge of the fluid computational space. Thus, the grids radiated from the upper vents and gradually became sparse. Figure 2 shows the partial profile of the grid around the upper vents for the two patterns of pollutant discharge. The total grid number was about 1.21 \(\times\) 10\({}^{6}\) for vertical pollutant discharge and 1.47 \(\times\) 10\({}^{6}\) for horizontal pollutant discharge.  
Figure 2(a) shows the velocity profile at a vertical height of 10 m from the ground, using different grid mesh numbers: 0.09 \(\times\) 10\({  
shows the velocity profile at a transverse distance of 10 m from the horizontal outlet, using different grid mesh numbers: 0.11 \(\times\) 10\({}^{6}\), 0.52 \(\times\) 10\({}^{6}\), 1.47 \(\times\) 10\({}^{6}\), and 3.00 \(\times\) 10\({}^{6}\). This indicates that the velocity distribution is not influenced by the increasing mesh number of the grids after it reaches 1.21 \(\times\) 10\({}^{6}\) for the outlets of vertically discharged pollutants and 1.47 \(\times\) 10\({}^{6}\) for the outlets of horizontally discharged pollutants. It is concluded that the grid independence requirement was met when the grid number reaches this approximate value.
## 3 Numerical Modelling Setup
### Finite Element Analysis (FEA)  
The FEA setup was conducted using ANSYS Mechanical (18.2, ANSYS Inc., Canonsburg, PA, USA) with the material used for the analysis as Inconel 738 an overview of the boundary conditions is shown in with the specific boundaries highlighted Figure 8.  
* Temperature dependent properties were implemented into the material model using manufacturing data [23].
* The fluid solid interaction (C) was added to the boundary conditions of each FEA model, importing temperature and pressures across the surface of the blades as calculated by the CFD simulation under design point boundary conditions.
* Both blades were given the cylindrical support (B) applied to the bottom surface of the hub as shown below. The stator was fixed in the tangential direction, the rotor was fixed in the radial and axial directions.
* A rotational velocity of 16,635 rad/s was applied to the rotor (A)
* A displacement boundary (A) condition of 0 mm in the z direction was applied to the front surface of the stator hub to simulate a connection to the volute.
* The rotor analysis was simulated using the Coriolis effect.
# Dependable and Secure Voting Mechanism in Edge Computing
## 3 System
### System Model
#### 3.2.2 Client  
A client \(c\) sends a request of the execution to a set of \(2f+1\) edge nodes by sending a message \(\langle\textsc{REQUST},seq,\sigma\rangle_{pc}\). Requests are signed with session keys \(pc\) created for the operation. Requests with invalid signature are ignored. \(seq\) is the request identifier that is used to ensure that the request will only compute once. \(\sigma\) refers to the operations performed in the cloud instances.  
The client expects to receive \(\langle seq,H(V_{\sigma})\rangle\) from the edge nodes. \(H(V_{\sigma})\) is a message digest from the outcome of the computation \(\sigma\), \(V_{\sigma}\). The client knows that the computation ended successfully, if it receives at least \(f+1\) equal \(H(V_{\sigma})\).
# Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions
## 4 Analysis and Discussion
### Analysis of Safety and Critical Velocity
#### 4.1.2 Nonlinear Critical Speed Analysis  
The critical speed is divided into a linear critical speed and a nonlinear critical speed. In general, the linear critical speed is 10-28% faster than the nonlinear critical speed. Therefore, calculating the critical speed of the train only needs to calculate its nonlinear critical speed. The nonlinear critical speed needs to give the train an initial speed that is much larger than the actual running speed and at the same time apply a force opposite to the running direction of the train body to make the train perform a uniform deceleration motion, let the train pass through a small section of lateral track excitation and then cancel so that the train continues to run on a smooth track. The nonlinear critical speed of the train is judged by observing whether the traverse amount of the wheelset converges in the postprocessing, and the minimum value of the convergence of the traverse amount of the wheelset is the nonlinear critical speed of the train.  
The nonlinear critical speed simulation selects 300 km/h as the initial speed of the train, which is significantly greater than the actual running speed of the train. The simulation results of the single-section trailer are shown in Figure 8. The lateral offset of the four wheelsets of the single-section trailer model is within \(\pm\)10 mm, and the convergence speed of the lateral offset of the four wheelsets is basically the same. Therefore, it can be concluded that the nonlinear critical speed of the model is 223 km/h.  
The simulation results of the marshaling train are shown in Figure 9. Figure 9 shows the \"M1, M2, T1, T2, M3, M4\" carriages of the six-car marshaling urban rail train. As a result, the nonlinear critical speed of each car relative to the single train model is different. The critical speeds of the T1 and T2 trailers are 217.5 km/h and 215.8 km/h, respectively, which are lower than the critical speed of the single-segment model. The critical speeds of the four motor cars M1, M2, M3 and M4 are 181.5 km/h, 183.3 km/h, 183.1 km/h and 181.2 km/h, respectively. Compared with the trailer, the motor vehicle has different parameters and more components, such as a gearbox and traction motor, so the critical speed of the motor vehicle is lower than that of the trailer. The critical speeds of M1 and M4 are lower than those of the other two, indicating that the dynamic performances of M1 and M4 are poor; the critical speed of T1 is greater than that of T2, indicating that the dynamic performance of T1 is better than that of T2, which verifies the previous analysis.  
Figure 8: The nonlinear critical speed of the single trailer.
## 3 Application to Field Data
### Dispersion Curve Calculation  
The collected noise data needed to be preprocessed, and the current preprocessing methods [39] include de-instrument response, mean removal, linear trend removal, bandpass filtering, spectral whitening, etc. In the paper, we mainly used mean removal, linear trend removal, and one-bit normalization. The specific processing is as follows: the noise record was segmented as a small window signal, with a general value of 5 s to 30 s, and the specific window value requires parameter testing; the spatial cross-correlation coefficients between each two sets of station pairs within the hour window were calculated, and the cross-correlation spectrum was averaged, which is the process of improving imaging resolution; by integrating the superimposed correlation spectrum, the frequency phase velocity energy spectrum of the noise signal was calculated, and the fundamental and higher-mode dispersion curves in the dispersion spectrum were further extracted.  
The spatial autocorrelation method and frequency-Bessel transform method were used to calculate the dispersion spectrum. The noise was divided according to the 20 s time window, and the frequency range was set between 1 Hz and 20 Hz, with a frequency interval of 0.1 Hz. The phase velocity calculation range was 10-1000 m/s, with a phase velocity interval of 5 m/s.
## 4 Discussion
Heat sinks are also constructed around LED active area structures to further buffer the temperature dependence effects. Temperature can reduce the efficiency of an LED as a light emitter, particularly when it is used for a long time. However, in the reverse function, i.e., in the detection of X-rays, the processing time (X-ray exposure time) is very short (in milliseconds), hence the temperature factor could be negligible. Therefore, based on these latter considerations, we assumed the temperature around the LED active areas would not increase due to X-ray absorption. All in all, active area-based amplification was simple, direct, and, above all, inexpensive--in comparison to the amplifier board that was quite costly (\(\sim\)100 \(\upupupup\)) and was associated with some limitations such as dark currents and saturation. Despite these setbacks, a hybrid amplification mode, amalgamating both the amplifier board and active area amplifications, would be a prospective amplification modal with summed advantages. Alternatively, amplifier boards with higher operating output voltage, low dark currents, and low saturation possibilities would be employed for higher electronic amplification gains. Additionally, LEDs of different emission wavelength spectrums [29] could also possibly be another signal amplification option to exploit because LEDs detect photons with a wavelength that is similar to the one that they emit [15]. In this survey, we implemented cold white LED strips, because they were presented with a better dosimetric performance as compared to the warm white, red, green, and blue LED strip colors in our prior survey [14]. Future work could, however, explore diagnostic X-ray detection using other LEDs colors, for instance, ultraviolet (UV) LEDs. Herein, heating effects, which may arise from the interaction between the X-rays and LED PN junction, were negligible. The impact of such effects on the generated photovoltaic signal might also be explored and quantified in future studies. From Section 3.1, LEDs demonstrated high sensitivity to visible light photons. Future studies could, therefore, convert the X-rays to visible light using scintillators. The visible light photons emitted from these scintillators could then be detected using LEDs. LEDs fabricated using higher atomic mass semiconductor material could also similarly produce higher X-ray radiation-induced signals since higher atomic mass semiconductors are generally associated with higher X-ray photon absorption capabilities [30]. However, this could be associated with higher dark currents due to the shorter bandgap of these high atomic mass materials [30]. In this event, charge localized in valance traps can easily jump over the short bandgap, after ambient energy absorption, hence excitation that implies a dark current.
## 2 Related Works
### Wireless Positioning Systems
UWB positioning technology is characterized by high positioning accuracy, high rating, and strong resistance to multipath interference [32]. Current recent research has mainly focused on how to reduce the impact of non-line-of-sight (NLOS) paths in high NLOS scenarios when applying UWB positioning to decrease positioning errors. [PERSON] et al. [33] applied LSTM networks in UWB localization in indoor scenarios to migrate the negative effects from both NLOS conditions and TOA errors. Compared to the conventional method, it can reduce the mean position error to 7 cm. Although UWB has a high positioning accuracy, the high cost of base stations and tags for UWB positioning makes it not a universally applicable positioning solution. For positioning systems in WWAN, Assist-Global Positioning System (A-GPS) technology is widely used in the location services of smartphones. It leverages cellular mobile communication networks to broadcast GNSS information and its auxiliary data, thereby assisting UEs in shortening the satellite's initial search time and improving location accuracy during satellite navigation [34]. The GNSS signal can be easily blocked, and recent research has attempted to enhance GPS using the UWB systems. [PERSON] et al. [35] proposed an RCP scheme that evaluates the positioning performance by generating a dataset in real urban scenarios. Experimental results show that this scheme can robustly resist adverse effects on positioning performance. Positioning technologies in cellular networks have evolved from 2G to 5G, and now to 5G NR and 5G-Advanced. In 5G NR and 5G-Advanced, the requirement for positioning accuracy has reached to centimeter level, leading to a significant focus on research based on CSI and CPP. Meanwhile, some studies have emphasized machine learning and fingerprint recognition. [PERSON] et al. [36] developed a novel Attention-Aided Residual Convolutional Neural Network (AAresCNN) for CSI-based indoor positioning, achieving a state-of-the-art performance on public datasets. [PERSON] et al. [37] proposed a novel positioning system, iPOS, using commercial 5G-NR CSI fingerprints for indoor positioning, incorporating CSI pre-processing and feature reconstruction modules. In the current development of 5G-Advanced, [PERSON] et al. [38] utilized CIR to extract position-related features and enhance positioning accuracy through cooperative deep learning, combined with NLOS recognition. Unlike other positioning systems, cellular network positioning does not require additional infrastructures and can achieve centimeter-level accuracy, which significantly reduces positioning costs. The use of CSI and CPP as measurement values, along with machine learning positioning strategies, demonstrates advancements in both 3 GPP standards and recent research.