esa-sceva/satcom-chunk-collection huggingface dataset:
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 5 Conclusions  
Among all of the tested weighting schemes, the GREC_GxS is the best. In this scheme, a stochastic model developed for GPS is scaled for other GNSS using SISRE information. When compared to the GPS-only solution, this weighting scheme improves all criteria of the assessment: formal errors, convergence time and coordinate repeatability. Moreover, these improvements are found during both test periods as well as for each station. We noticed an average reduction of formal errors of about 38% in each coordinate component. The mean formal error of 3D coordinates is reduced from 4.4 mm to 2.7 mm. The mean convergence time is reduced from 5 h 43 min to 3 h 1 min (47%), and from 3 h 34 min to 2 h 11 min (39%) for the horizontal and vertical component, respectively. GREC_GxS is the only scheme that improves the coordinate repeatability, however, the improvement is not spectacular. The coordinate repeatability in 3D in GPS-only solution is 15.2 mm and in GREC_GxS is 14.2 mm, which gives a 6% improvement of the precision.  
The presented results allow us to recommend GREC_GxS as the best weighting scheme for multi-GNSS real-time PPP processing. Nevertheless, we have to be aware that the scheme uses information about the quality of the real-time products (orbits and clocks) expressed with SISRE. The ongoing development and the continuous improvement of the orbital models used in real-time services may change the SISRE values that are presented in this paper, and therefore make the developed scheme obsolete. Thus, another suggestion is to prepare some kind of a service for regular monitoring of SISRE values for real-time products, which could be applied in future works connected with multi-GNSS real-time positioning. Real-time users have to mindfully take advantage of the multi-GNSS constellation whose integration is not direct and origins from the differences between all of the systems.  
This work has been supported by the Polish National Science Centre research Project No. UMO-2014/15/B/ST10/00084 and the Wroclaw Center of Networking and Supercomputing ([[http://www.wcss.wrocl.pl/](http://www.wcss.wrocl.pl/)]([http://www.wcss.wrocl.pl/](http://www.wcss.wrocl.pl/))) computational Grant using MATLAB Software License No: 101979. The authors gratefully acknowledge the International GNSS Service (IGS) for providing multi-GNSS observations, the Centre National d'Etudes Spatiales (CNES) for providing real-time streams, the Bundesamt fur Kartographie und Geodasie (BKG) for providing the open-source BNC software.  
[PERSON] raised the scientific problem and prepared GNSS data and products used in this study; [PERSON] and [PERSON] implemented the functional and stochastic modelsfor real-time multi-GNSS processing in WARP software, analyzed results and wrote the paper; [PERSON] developed the GPS weighting scheme and did the processing; all authors contributed to the development of multi-GNSS weighting schemes; [PERSON] edited the manuscript and provided valuable insights.  
The authors declare no conflict of interest.
# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 3 Materials and Methods
### Parameter-Shared Intelligent Media Analytics Framework  
Our proposed framework, PIMA, is an end-to-end intelligent media analytics system for the joint classification of sentiment, emotion, irony, and hate speech in a low resource language, i.e., Greek. PIMA is comprised of a transformer-based LM and an encoder block that constitutes the parameter sharing layer and the multi-headed classification layer. The shared parameters leverage pre-trained and pre-existing knowledge from the transformer-based LM and learn multi-task representations from the joint training approach. Specifically, we use the Greek variation of BERT, named Greek BERT [28], as the baseline LM in our framework. As it was trained on modern Greek text from Wikipedia, the European Parliament Proceedings Parallel Corpus, and the Greek part of OSCAR [48], it serves as a more suitable LM for our tasks.  
An overview of the framework architecture is shown in Figure 1.  
The basic architecture of the model consists of (i) the tokenized input layer, (ii) the transformer-based parameter sharing layer, and (iii) the task-specific classification heads. Initially, the model takes a sentence as a token sequence as input and captures contextual features using the pre-trained Greek BERT-based weights. An additional encoder block is applied to the initial output to generate the multi-task representations via parameter sharing.  
The encoder block captures contextual information in the hidden representation layer and creates the output vector to effectively capture all relations in the input. The hidden representation is passed through the parameter sharing layer to the classification heads via linear projection. The applied task-specific feed forward layers classify the projected representations with a \(Softmax\) activation function.  
Following this section, we first describe the parameter sharing layer and our joint end-to-end multi-task model learning approach for multi-task classification.  
Figure 1: Architecture overview of our proposed parameter-shared intelligent media analytics framework. S, I, H, and E denote the sentiment, irony, hate speech, and emotion classification heads, respectively,and DAGB and TBEL denote the domain-adapted Greek BERT language model and the transformer-based encoder layer, respectively. \(N_{L}\) denotes the number of encoder layers.
## 4 Experiments
### Model Efficiency  
Table 5 quantifies the computational cost and complexity of different methods using Floating-Point Operations (FLOPs) and parameter size (Params) to evaluate model computational efficiency. Among them, FC-EF, as a pure convolutional network architecture, has the lowest FLOPs and Params; although SNUNet has a small number of parameters, it generates extremely high numbers of FLOPs due to the extensive use of densely connected multi-scale features, leading to a significant increase in computational load. Compared with AMTNet, the proposed MDNet, despite having higher Params and FLOPs, exhibits superior performance in key metrics such as F1-score and IoU, as shown in Tables 2-4, achieving an average F1 gain of 1.72% across the three datasets; although WS-Net++ achieves better performance on the CLCD dataset, its Params and FLOPs are significantly higher than those of MDNet. Notably, although MDNet integrates modules such as the EMF, CASAM, DPEM, Transformer, and CAM, it still maintains highly competitive complexity. Its FLOPs (26.43 G) are 51.8% lower than those of SNUNet (54.83 G), which uses dense multi-scale connections; its Params (36.62 M) account for only 4.0% of those of WS-Net++ (906.16 M), as the latter introduces a large number of redundant parameters due to its wavelet transform and semi-supervised mechanism. In summary, MDNet achieves a good balance between computational efficiency and model complexity, verifying the superiority of this method  
Figure 9: Visualization results of different methods on the CLCD dataset. In the prediction results, white represents true positive (TP), black represents true negative (TN), red represents false positive (FP), and green represents false negative (FN). In brief, the lower the proportion of red and green, the better the prediction performance of the model. A and B represent two-phase remote sensing images, Label is the ground - truth change annotation, FC-EF, SNUNet, BIT, AMTNet, WS-Net++ are comparative models, and MDNet (Ours) is the proposed model.  
in resource utilization efficiency. The improvement in detection accuracy and reliability can reasonably offset the additional computational overhead, providing a more practically valuable solution for change detection tasks.
## 2 Three-Closed-Loop Control of an Unbalanced Three-Phase Four-Wire Inverter  
### Control Principle of a Three-Phase Four-Wire Inverter  
The split-capacitor three-phase, four-wire inverter circuit is shown in Figure 1, and the control principle is shown in Figure 2. The voltage outer loop is operated by collecting the three-phase voltages for RMS values and then making a difference with the target value for PI regulation and multiplying the regulated output with the unit sine wave \(\sin\omega t\) to obtain the instantaneous command voltages \(u_{\mathrm{a}}\), \(u_{\mathrm{b}}\) and \(u_{\mathrm{c}}\), which are transformed by _abc-dq_ coordinates to generate \(u^{*}{}_{\mathrm{d}}\) and \(u^{*}{}_{\mathrm{q}}\). Then, voltage loop PI regulation and feedforward decoupling generate the command currents \(i^{*}{}_{\mathrm{dL}}\) and \(i^{*}{}_{\mathrm{q}}\) for the current's inner loop. The difference between the commanded current and the coordinate transformed actual currents \(i_{\mathrm{dL}}\) and \(i_{\mathrm{dL}}\) for the PI regulation of the current inner loop and feedforward decoupling generates \(u_{\mathrm{nd}}\) and \(u_{\mathrm{rq}}\). Finally, the SPWM technique is used to generate PWM waves to complete the control of the three-phase, four-wire inverter, three-closed-loop system [16].  
Figure 1: Split-capacitor three-phase four-wire inverter circuit.
###### Abstract  
Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage.  
gearbox; bearing: vibrations; diagnosis; spectral kurtosis +
Footnote †: journal: Applied Sciences
# Lifetime Measurement of Cesium Atoms Using a Cold Rydberg Gas
## 3 Experimental Method and Results  
### Lifetime Measurement  
In Figure 2, we present the measured population of \(|65D_{5/2}\rangle\) Rydberg state as a function of the delay time \(t_{D}\), from which the lifetime for a given Rydberg state is obtained. The extracted lifetime of \(|65D_{5/2}\rangle\) state is 94.1 \(\pm\) 2.5 \(\upmu\)s and the related decay rate is 10.63 \(\pm\) 0.29 kHz. The inset of Figure 2 displays the Rydberg spectrum when we scan the second excitation laser frequency for the delay time \(\tau_{D}\) = 5 \(\upmu\)s.  
The radiative lifetime of an atom at \(T\) = 0 K mainly comes from the spontaneous decay rate, which is the inverse of the Einstein A-coefficient. For the \(nL\) Rydberg state and transition \(nL\to n^{\prime}L^{\prime}\) (\(L\) and \(L^{\prime}\) denote the angular quantum number), the Einstein A-coefficient is written as [1]  
\[A(nL\to n^{\prime}L^{\prime})=\frac{4\omega_{nn^{\prime}}^{3}}{3c^{3}}\frac{L_ {max}}{2L+1}R^{2}(nL\to n^{\prime}L^{\prime}) \tag{1}\]  
where \(R(nL\to n^{\prime}L^{\prime})\) is the Radial transition matrix element, \(L_{max}\) is the maximum of \(L\) and \(L^{\prime}\), and \(\omega_{nn^{\prime}}\) is the transition frequency between \(nL\) and \(n^{\prime}L^{\prime}\), respectively. Considering all possible transitions of \(nL\to n^{\prime}L^{\prime}\), the spontaneous decay rate of \(nL\) state, \(\Gamma_{sp}\), is taken as  
\[\Gamma_{sp}=\Sigma_{E_{nL}>E_{n^{\prime}L^{\prime}}}A(nL\to n^{\prime}L^{ \prime}) \tag{2}\]  
The radiation lifetime \(\tau_{sp}\) of the given \(nL\) state can be obtained by \(\tau_{sp}\) = 1/\(\Gamma_{sp}\). For \(|65D_{5/2}\rangle\) Rydberg state, the calculated spontaneous radiation decay rate is \(\Gamma_{sp}\) = 6.27 kHz and the related lifetime is \(\tau_{sp}\) = 159.4 \(\upmu\)s.  
The measured lifetime of \(|65D_{5/2}\rangle\) state, shown in Figure 2, \(\tau_{exp}\) = 94.1 \(\pm\) 2.5 \(\upmu\)s, is much less than the calculated spontaneous lifetime \(\tau_{sp}\) = 159.4 \(\upmu\)s. We attribute this dramatically decrease of the lifetime to two reasons. Firstly, at room temperature \(T\) = 300 K, BBR photons of low-frequency microwave (MW) fields can provide successive energies to couple adjacent Rydberg states due to the Rydberg energy difference within the MW field range. The high number of MW photons per mode in the BBR field results in that the decay of a single Rydberg atom is orders of magnitude faster than that of \(T\) = 0 K. Secondly, the interaction between Rydberg atoms will increase the collision loss rate of the given Rydberg state, thereby accelerating the decay of Rydberg state and decreasing its lifetime.
## 4 Evaluation
### Results and Discussion  
This section presents and discusses the simulation results of FLARoute and the routing proposals selected for comparison in two parts that are detailed in Sections 4.2.1 and 4.2.2. Section 4.2.1 first evaluates the proposals under high degree of spatial locality in the Skudai scenario described in Section 4.1.1. This includes: the percentage difference between MaxBE's estimate of the maximum burden among neighbouring nodes and the actual maximum burden in the network; and results of FLARoute and the routing proposals based on the selected performance metrics. Next, Section 4.2.2 evaluates PRoPHET, CCAF and FLARoute in the Helsinki simulation area (i.e., the medium scenario) described in Section 2.2.2. This is followed by the impact of FLARoute and the choice of its parameters in Section 4.2.3.
## 3 Test Results and Analysis
### Load-Deflection Curve  
Figure 12 shows the load-deflection curves of the mid-span section of the test beams, and the symbol on the curve indicates the yield point. from which it can be seen that the loading process of all the test beams can be divided into the elastic stage, the crack development stage, and the failure stage.  
Before the crack of the test beam, the load-deflection curve is straight. With the increase in the load, the concrete in the tension area cracked, the load-deflection curve takes the first turn, and the test beam entered the crack development stage. With the continuous occurrence and development of cracks, the stiffness of the test beam gradually decreased.  
For the unreinforced contrast beam under continued loading, the reinforcement entered the yield stage and the load across the middle deflection curve appears at the second turn; then, as the load changed slowly, the deflection increased rapidly, the concrete in the pressure area was crushed, and the specimen was finally destroyed.  
For the reinforcement beam A1, the load could still grow rapidly after entering the yield stage, until the epoxy mortar was pulled off and the load dropped to 77% of the peak load. After the pull failure of the reinforced layer, the specimen showed a similar damage process to that of the unreinforced beam A0. The load increased slowly, the deflection increased rapidly, the concrete in the pressure area was crushed, the load dropped to 55% of the peak load, and the specimen was finally destroyed.  
For the reinforcement beam B1, after the reinforcement entered the yield stage, the load increased slowly, the epoxy mortar was pulled off, and the load dropped sharply to 81% of the peak load. After the pull failure of the reinforced layer, the specimen showed a similar damage process to that of the unreinforced beam B0. The load changed slowly,  
Figure 11: The concrete protective layer is detached from the longitudinal steel bar interface.  
Figure 12: Load–mid-span deflection curve of beams.  
the deflection increased rapidly, the concrete in the pressure area was crushed, the load dropped to 68% of the peak load, and the specimen was finally destroyed.  
Compared with the unreinforced beams A0 and B0, the yield load, peak load, and structural stiffness of the reinforced beams A1 and B1 were significantly increased because the thin layer reinforced by the epoxy mortar effectively participates in the structural force before the tensile break.  
The test values of the cracking load \(P_{\alpha r}\), longitudinal rib yield load \(P_{y}\), peak load \(P_{m}\), ultimate load \(P_{u}\), and corresponding mid-span deflection \(f_{\alpha r},f_{y},f_{m},\) and \(f_{u}\) of each specimen are summarized in Table 3. The limit point is the point at which the load drops to 85% \(P_{m}\); \(P_{m,0}\) represents the peak load of the unreinforced contrast beams A0 and B0; and \(\mu\) is the displacement ductility coefficient. Failure form A represents the typical bending failure modes, and failure form B represents the tensile failure of the reinforcement layer.
# Scale-Free Random SAT Instances
## 2 Generation of Scale-Free Graphs
In the last case, we would ensure that after folding, node \(i\) has a degree close to \(k_{i}\), since in the Erdos-Renyi model, node degrees follow a binomial distribution (a Poisson distribution \(P(k)=e^{-z}\frac{k^{t}}{k! }\) in the infinite limit, where \(z\) is the average degree, \(z=1\) in our case). The previous method has two problems. First, the resulting graph (after the unfold-fold process) will have an average node degree equal to:  
\[\mathrm{E}[k]=\sum_{k=1}^{\infty}k\,P(k)=\sum_{k=1}^{\infty}k\,\frac{k^{- \gamma}}{\xi(\gamma)}=\frac{\zeta(\gamma-1)}{\xi(\gamma)} \tag{1}\]  
If we want to obtain a graph with a distinct average degree, we have to modify the probability \(P(k)\) for small values of \(k\) and ensure that \(P(k)\) follows a power-law distributiononly asymptotically for big values of \(k\). In other words, we only require \(P(k)\) to follow a _heavy-tail_ distribution. Second, a great fraction of generated graphs will contain self-loops or multiple edges between the same pair of nodes after folding. This means that a great fraction of graphs will be rejected, which makes the method inefficient. However, the model can be useful to translate some properties of the Erdos-Renyi model to scale-free graphs via the unfolding-folding process and the _configuration model_[28; 29]. The unfolding-folding procedure was described by [PERSON] et al. [30]. They, instead of assigning a random degree to each node, describe a model where, given two parameters \(\alpha\) and \(\gamma\) (In the original paper, authors use the name \(\beta\) instead of \(\gamma\). ), we choose a random graph (with uniform probability, and allowing self-cycles) among all graphs satisfying that the number of nodes with degree \(x\) is \(e^{\alpha}/x^{\gamma}\). When \(\gamma>2\), the average node degree in this model is also \(\frac{\xi(\gamma-1)}{\xi(\gamma)}\). Alternatively, instead of fixing the degree of every node, we can fix the _expected degree_ of every node \(\mathrm{E}[k_{i}]=w_{i}\). In order to construct a graph where nodes have this expected degree \(\mathrm{E}[k_{i}]\sim w_{i}\), we only need to generate edge \(i\leftrightarrow j\) with probability \(P(i\leftrightarrow j)\sim w_{i}w_{j}\). If we want to generate a scale-free graph where \(P(k)\sim k^{-\delta}\), for sparse graphs, it suffices to fix \(w_{i}=i^{1/(\delta-1)}\)[31; 32] (see also Theorem 1). Our scale-free formula generation method is based on this static scale-free graph generation model with fixed expected node degrees. Basically, nodes are replaced by variables. Then, instead of edges, we generate hyper-edges. By negating every variable connected by a hyper-edge with probability \(1/2\), we obtain clauses.
## 3 Post-Rolling Online Ultrasonic Thickness Measurement Platform  
### Principle of Ultrasonic Thickness Measurement  
Ultrasonic testing can be categorized into several types based on different criteria [21]. According to the detection principle, ultrasonic testing can be divided into the transmission method, pulse-echo method, Lamb wave method, and resonance interference method [22]. According to the waveform, ultrasonic testing can be divided into longitudinal wave method, transverse wave method, surface wave method, and plate wave method [23]. According to the number of probes, ultrasonic testing can be divided into single-probe method, dual-probe method, and multi-probe method [24]. According to the contact method between the probe and the tested object, ultrasonic testing can be divided into direct contact method and immersion method [25]. Considering the type of workpiece being tested, this experiment adopts the single-probe immersion method using longitudinal wave pulse-echo detection.  
In the workpiece, ultrasonic waves primarily propagate in the form of longitudinal and transverse waves. Detection is performed by analyzing the time difference and waveform difference between the transmitted wave peak and the echo wave peak [26]:  
\[\left\{\begin{array}{c}v_{L}=\sqrt{\frac{E(1-\sigma)}{\rho(1+\sigma)(1-2 \sigma)}}\\ v_{S}=\sqrt{\frac{E}{2\rho(1+\rho)}}\end{array}\right. \tag{6}\]  
where \(E\) is the [PERSON]'s modulus, \(\sigma\) is the [PERSON]'s ratio, \(\rho\) is the mass density, \(v_{L}\) is the longitudinal wave velocity, and \(v_{S}\) is the transverse wave velocity.  
The pulse-echo method uses an excitation source transmitter to send a signal to the ultrasonic transducer, which then emits ultrasonic waves [27; 28]. When the emitted ultrasonic waves reach the outer wall of the tested object, they reflect to form a first echo. After entering the interior of the object, they reflect upon encountering the inner wall, and the ultrasonic transducer receives the twice-reflected waves to form a second echo, as shown in Figure 3.  
In the waveform display in Figure 3, Wave I represents the first echo received by the ultrasonic transducer, and Wave II represents the second echo received by the ultrasonic transducer. Based on the assumption that the medium within the tested object is uniform, the propagation time of the ultrasonic wave is directly proportional to the thickness of the tested object. Therefore, the formula for the thickness of the tested object is  
\[H=\frac{1}{2}(t\times v) \tag{7}\]  
where \(H\) is the thickness of the tested object, \(t\) is the time difference between the first and second echoes, and \(v\) is the propagation speed of the ultrasonic wave in the tested object.