esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Research
### Wearables and Next GNSS Chip Generation  
Wearables have the potential to expand the usage of data gathered in smart devices, bringing the health-related sensor dimension. Android smartwatches are based on a slightly different operating system (OS) than smartphones, called WearOS (formerly Android Wear). This OS is based on the same Android API levels, which allows for the common development of Android applications in both smartphones and smartwatches. WearOS 3.0 (Android 11) is the first version based on a higher version than Android 9, which should provide access to raw GNSS measurements. However, to the best of the authors' knowledge, no paper until today has yet investigated the GNSS measurement quality of any Android smartwatch. Nevertheless, medical-oriented studies have shown the potential of wearables for medical research [27; 28], as well as Android devices for contact tracing [29]. More specifically, in [30], the usage of indoor/outdoor positioning to retrieve the mobility patterns showed interesting results in recovering mental health assessment for patients. Thus, more accurate positioning and motion estimation could enable further medical research, especially if combined with the health sensors of smartwatches.  
In 2019, Broadcom announced the chipset's second generation (BCM4776), a follow-up chipset to the previously mentioned BCM4775 included in the Xiaomi Mi8. According to Broadcom, the new chipset comes with potentially 60% additional L5 signals with the reception of BeiDou-L5 signals [31]. Since 2021, new Android devices have integrated this chipset into their design, such as the Google Pixel 6 and 7, and even the recently released Google Pixel Watch. The release of the new Broadcom chipset could potentially mark the beginning of the second generation of GNSS measurements in Android smart devices.
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 6 Results and Discussion
### Catalogue Size  
In Figures 4 and 5, we compare the three forwarding strategies and show the impact of the cache size to the catalogue size ratio. In general, performance diminishes as this ratio reduces. We observe that with low exploration, NDNFS-RLRNN can exploit in-network caching better than ASF for all the considered scenarios. With respect to the cache hit rate, it provides a performance improvement compared with ASF of between 23-30%, 20-32%, and 22-30% under the LCE policy, _Betw_ policy, and _ProbCache_ policy, respectively. Also, NDNFS-RLRNN provides an improvement under the _Betw_ policy compared with the NRR strategy of between 3-16%. It achieves these improvements with less overhead as shown in Figure 5c; and as expected, the overhead NRR incurs is significantly higher than the other two forwarding strategies.  
Figure 3: Evaluation of the probe parameter (p) under different on-path caching policies. (a) Cache hit rate; (b) Network load per request. Each CR is equipped with a cache size of 1% of the content catalogue. Content popularity is modelled according to the Mandelbrot-Zipf (MZipf) distribution with the settings: \(\alpha=1.0\) and \(p=5.0\). The results reported are the average of 20 randomized simulation runs.  
The network overhead result as shown in Figure 5 is a measure of the cost to network incurred by the forwarding strategies. The probing of interfaces introduces additional costs because it increases the number of Interests processed in the network and, as a result, the number of Data packets and Interest NACKs. As a result, NRR incurs significantly higher overhead compared with the other forwarding strategies because it tries to flood the network with every request. The ASF is similar, in terms of probing, to the NRR except that the probing is periodical and restricted to a subset of the interfaces at each CR. This explains why NRR generates more than 2.5 times the overhead for ASF in all the considered scenarios. Moreover, NDNFS-RLRNN incurs the least overhead among the considered forwarding strategies primarily because of how it tries to take advantage of the probing process. Also, the low probing parameter value of 0.3 and the use of _probe_ Interests to control the network exploration are contributing factors.  
Finally, the relatively high deviation from the mean values, compared to the other results, can be explained by the randomization of the content distribution at the CRs.  
Figure 4: Cache hit rate performance of NDNFS-RLRNN (\(p=0.3\)), ASF and NRR for different cache size to catalogue size ratios, under: (**a**) LCE caching policy, (**b**) _Betw_ caching policy and (**c**) _ProbCache_ caching policy. The cache size of each CR is fixed at 10 data objects while we vary the catalogue size rom 1000 to 5000 data objects. Content popularity is modelled according to the Mandelbrot-Zipf (MZipf) distribution with the settings: \(p=5.0,\alpha\in[0.6,1.2]\). The results reported are the average of 20 randomized simulation runs.
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Basic Principle of NOMA
#### 2.2.1 Superposition Coding (SC)  
Multiple users' signals are mapped in the power domain and at the transmitter utilising SC in NOMA. Capacity on a scalar Gaussian broadcast channel is achieved by combining user information into a single-signal source by using a non-orthogonal SC technique [36].  
Consider a situation in which \(K=\{\)user-N, user-F\(\}\) represents the total number of users where user-N denotes the near user and user-F represents the far-user. Complex-valued symbols, \(x_{\text{N}}\) and \(x_{\text{F}}\) for user-N and user-F, respectively, are mapped into their appropriate M-ary modulation constellation with total transmitted power \(P_{s}\) and the power allocation coefficients \(\alpha_{\text{N}}\) and \(\alpha_{\text{F}}\) for user-N and user-F, respectively. For this example, both users will utilise quadrature phase-shift keying (QPSK), as shown in Figure 4. In SC, user-F's (higher transmit power) constellation is superimposed on user-N's (less power) constellation to produce a mapped constellation, as shown in Figure 3(c).
## 4 Discussion
Germ cell-specific _Gja1_ knockout mice have been developed to assess the importance of this connexin in spermatogenesis [8]. Surprisingly, inactivation of _Gja1_ in germ cells has no effect on spermatogenesis and the animals are fertile, suggesting that the critical function of Gja1 is probably related to the differentiation of Sertoli cells and interactions between these cells, rather than interactions with developing germ cells. However, the reduced expression of _Gja1_ in germ cells has been associated with increased risks of testicular cancer development. Indeed, this tumor suppressor gene is downregulated during development of testicular seminoma [12]. Importantly, the lack of infertility phenotype in germ cell-specific _Gja1_ knockout mice suggests compensation by the expression of other connexins, such as Gjb2 (Cx25) or Gjc1 (Cx45) [8]. Indeed, _Gjc1_ is expressed in peritubular, Sertoli and germ cells [52] and its protein levels are increased in testes of germ cell-specific _Gja1_ knockout mice [8]. Additionally, the transfer of molecules through gap junctions is selective and unidirectional from Sertoli cells to spermatozoa [53; 54; 55]. Since spermatogenesis is compromised in Sertoli cell-specific _Gja1_ knockout mice, whereas it is preserved in germ cell-specific _Gja1_ knockout mice [4; 5; 8; 56], it is most likely that the connexons of spermatogonia are heterotypic in nature [56; 57]. TGCT are the most common cancer in young men. Indeed, more than 90% of all testicular cancers are germ cell tumors. This type of cancer develops from transformed gonocytes or undifferentiated spermatogonia. Seminomas generally have a good prognosis, while non-seminomatous tumors are much more likely to present with metastases and mixed germ cell tumors composition. Interestingly, _GJA1_ has been proposed as a potential diagnostic and prognostic marker for TGCT [58]. Although _GJA1_ is expressed in seminoma, its protein localization is aberrantly detected in the Golgi apparatus and has a molecular weight of 70 kDa [10], suggesting that its protein maturation is being compromised. Overall, connexins have been considered tumor-suppressor genes, and pharmacological up-regulation of their expression has therapeutic implication in cancer treatments [59]. In this study, we show that Jun and Fosl2 cooperate to activate _Gja1_ expression in GC-1 spermatogonial cells. In addition, the expression of these AP-1 members is correlated to that of _GJA1_ in TGCT RNA-Seq data. Importantly, most of these samples have low levels of expression of _GJA1_. Hence, this gene could be a potential biological marker linked to the severity of TGCT development. However, the causal relationship between the loss of AP-1 members' cooperation and the reduced expression of _GJA1_ in TGCT cannot be made solely based on the correlations. Such regulatory mechanism of _GJA1_ expression in TGCT will require further investigation.
## 2 Method
### Oscillation of the Airfoil  
The movement of the airfoil model is described as Equation (1) in a sinusoidal mode, as shown in Figure 2.  
\[\alpha_{t}=\alpha_{0}+\alpha_{amp}\sin(\omega t), \tag{1}\]  
where,  
\(\alpha_{t}\) is the angle of attack at flow time \(t\),  
\(\alpha_{0}\) is the mean angle of attack,  
\(\alpha_{amp}\) is the pitch oscillation amplitude,  
\(\omega\) is the oscillation angular frequency.  
The angular frequency \(\omega\) is related to the reduced frequency \(k\), which is an important parameter used to describe the 'degree of unsteadiness', as defined by Equation (2).  
\[k=\ \frac{\omega c}{2U_{\infty}} \tag{2}\]  
where \(U_{\infty}\) is the free-stream velocity and \(c\) is the chord length of the airfoil. It can be obtained from dimensionless Navier-Stokes (N-S) equations. [PERSON] has the empirical view that when \(0\leq k\leq 0.5\), the flow can be considered as quasi-steady flow, which means for some cases that the unsteady effects may be neglected completely [1]. Therefore, \(k=0.026\), \(0.502\) and \(0.106\) are set to be computed to find out whether the previous empirical view applies to the pitching motion of the DU 91-W2-250 airfoil. They correspond to the oscillating period T = 0.6 s, 0.3 s and 0.15 s, respectively.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 4 Comparison of Deep Learning Results between Signal-Processed Data and No Signal-Processed Data
### Deep Learning Results Using the LeNet  
When learning the signal processing results, 34 out of 1152 test data could not be accurately determined. Moreover, when learning the raw data results, 308 out of 1152 test data could not be accurately determined. Through this result, the acuity was calculated as shown in Equation (9). The learning time was about 3249 s.  
\[\text{Accuracy}\ =\frac{Number\ of\ correct\ predictions}{Total\ number\ of\ predictions} \tag{9}\]  
As a result of using the LeNet model, signal-processed data had an accuracy of about 97%, whereas raw data without signal processing had an accuracy of about 73.26%. In the case of raw data without signal processing, the reason for the low accuracy was that the flat signal was buried due to the overlap of the raw data (Time-domain) with the noise signal, and thus, the size of the flat was small and the anomaly could not be accurately diagnosed.  
In addition to the accuracy, the results of the true positive rate were compared and analyzed through ROC. The ROC curve is frequently used in binary classification models, through which the AUC (Area Under the Curve) value is obtained, and the closer this value is to 1, the higher the reliability is regarded [30]. The result is shown in Figure 29.  
Figure 28: Pre-process diagram using a signal processing method.  
Figure 29: Comparison of AUC value of STFT and raw data (Time-domain).  
Finally, the deep learning results of the preprocessed data through signal processing and the raw data that were not preprocessed are shown in Table 7. It was confirmed that the result of deep learning with preprocessed data using the signal processing method was about 24% higher in Accuracy and 0.21 in AUC than the result of deep learning based on the Raw data (Time-domain) approach.
## 3 Contacts  
The type of contact describes how each layer is meshed to the other. There exist many ways to define the contact between all the layers; however, this paper focuses its study on the three following types: tie (penalty), bonded (rough), and mesh merged. A penalty method provides a simple way to bond surfaces together permanently with easy mesh transitioning. It is used by default for the surface-to-surface formulation, and it significantly reduces solver cost and CPU time for models with a massive fraction of nodes involved in contact. The rough friction model is typically used in conjunction with the no separation contact pressure-overclosure relationship for motions normal to the surfaces. This type of surface interaction is called \"rough\" friction, and all relative sliding motion between two contacting surfaces is prevented due to an infinite coefficient of friction. However, the merged technique is described as a shared mesh for both individual parts, as shown in Figure 4, avoiding collision or \"close interaction\" between the slave and master surfaces [31].  
Figure 5 presents the von Mises and S33 stress under three different types of contact. The primary aim of this analysis is to understand the stress propagation behaviour between the bottom centre node of the LEP and the top centre node of the filler, and to assess how each part is numerically connected. The materials of the two parts in contact, LEP and filler, have been modified to have similar properties to assess the effect of changing the contact type independently of material properties. It ensures that the results are not affected by the different materials' abilities to absorb the impact. Specifically, both LEP and filler are defined with the gelcoat properties mentioned in Table 2. As described in the Abaqus guide on constraints and connection [32], tie (penalty) and bonded (rough) share two individual local nodes between their two contact surfaces. In contrast, when the two bodies are mesh merged, they share a single local node at their touching surfaces.  
Regarding the curve's behaviour, in each case, the tied and bonded corresponding curves are acting similarly. However, a notable gap exists between the peak values of the bottom LEP and top filler, with a difference of approximately 10 MPa on the S33 graph. This discrepancy appears independent of the material properties and indicates that the transition between LEP and filler is not smooth or completely accurate when using tie or bonded contacts. In contrast, the merged contact curves lie between the four curves described by tied and bonded contact types, suggesting more accurate results. Numerically,  
Figure 4: Slave and master surfaces schematics for mesh friction formulation and mesh types of contacts.  
the merged curve shows a S33 peak stress difference of 5 MPa with the bottom LEP node and 2 MPa with the top filler node. This shows a better transition between LEP and the filler, thanks to using the mesh merged interaction method and therefore, sharing single local nodes between the two surfaces.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 4 Results and Discussion
Furthermore, the performance of the MIMO detectors is evaluated considering the classification metrics previously defined in Section 2. Table 7 shows the results of this evaluation. The best performance is achieved when the OH labeling approach is selected in both MIMO configurations, with 0.97 and 0.95 F1-scores, respectively. This method also has the highest precision and recall values. Nevertheless, the results shown in Table 7 indicate that the performance metrics are directly related to the number of classes \(N_{c}\), which is also supported by the fact that the worst results are obtained when using the DSE strategy, where \(N_{c}\) is the lowest among all labeling methods. However, for DSE, the classification metrics outcomes are around 0.9, showing a labeling strategy with good performance. The OHA strategy produces classification performance values above symbol encoding and the OH strategy. Figure 2: NMSE loss curves vs the number of epochs during the training phase for the MIMO \(2\times 2\) (**left**) and \(4\times 4\) (**right**) DL-based detection configurations. The dashed lines indicate the NMSE values for the test or validation subsets, while the solid lines are the NMSE values for the training subset. Figure 4: BER performance of the detectors for the analyzed MIMO detection schemes with \(N_{t}=4\) and \(N_{r}=4\) antennas. The optimal ML detector and DL–based detectors with the OH, the OHA, and the DSE labeling strategies are applied. Figure 3: BER performance of the detectors for the analyzed MIMO detection schemes with \(N_{t}=2\) and \(N_{r}=2\) antennas. The optimal ML detector and DL–based detectors with the OH, the OHA, and the DSE of symbol labeling strategies are applied.
# Analysis of Nucleation and Glass Formation by Chip Calorimetry
## 3 Results and Discussions
### Glass Formation
#### 3.2.1 New Polyamorphous Material-D-Mannitol
On the other hand, if the GN sample is annealed with a combination of annealing temperature that can trigger the GN/SCL-1 to GX transition and annealing time that is longer than the delay time of the transition, GX can be formed in the annealed sample and the glass transition of GX can appear in the following heating trace. An example of that protocol is shown in Figure 7c. After annealing at \(T_{a}=309\) K for \(t_{a}=16\) s, the sample is then reheated to 340 K to obtain the supercooled liquid state and immediately quenched. The following heating trace in Figure 7d clearly shows three endothermic transitions before crystallization as \(T_{g}\) of GN at 298.3 K, \(T_{g}\) of GX at 327.3 K, and the transition from SCL-2 to SCL-1 at 340.1 K. It is also worth noting that the mixture of SCL-1 and SCL-2 has a larger specific heat capacity than that for pure SCL-1, which means that pure SCL-2 has a larger specific heat value compared to pure SCL-1 [4]. By choosing the Flash-DSC heating trace of the unannealed GN sample as a baseline that is subtracted from each of the annealing traces, the enthalpy changes under each annealing condition can then be calculated by integrating the remaining curve from Ta to 458 K. By normalizing the enthalpy change with the corresponding sample weight, a three-dimensional plot of the enthalpy change with \(T_{a}\) and \(\ln(t_{a})\) can be constructed as shown in Figure 7e. There are three plateaus in the 3-D profile, which correspond to unannealed GN/SCL-1 (\(\Delta H=0\) J/g red), GX/SCL-2(\(\Delta H=-50\) to \(-65\) J/g, yellow and orange), and \(\alpha\) crystalline (\(\Delta H=-160\) J/g, blue), respectively. Figure 7f is obtained by projecting the three-dimensional surface onto the \(T\)-\(\ln(t)\) plane. In this projection, the boundaries between adjacent plateaus are actually the Time-Temperature-Transition (TTT) curve. The transition boundary between the two amorphous states has a C shape, which is a main feature of thermally activated phase transitions. As a consequence, the time length to reach the boundary at a certain temperature is the delay time \(\tau\) for the reaction. It can also be seen that, although the difference is not very large, the height of the plateau corresponding to GX formed at lower annealing temperature (below 305 K) is greater than that formed at higher annealing temperature [4].
# Learning Hierarchical Representations for Explainable Chemical Reaction Prediction
## 3 Method
### Learning Strategy and Objectives
#### 3.2.2 Reaction Classification and Yield Prediction  
For the reaction classification task, we apply an ensemble learning way with three kinds of classifiers. The first one directly classifies the transformer-based feature, \(\mathbf{x}^{t}_{global}\), with a Softmax operation optimized according to the cross-entropy loss, \(L_{CLS\_global}\). The second one learns a Softmax classifier with the temperature \(\tau\) at the coarse level and generates a multinomial distribution, \(\mathbf{y}_{coarse}\in\mathbf{R}^{C_{coarse}}\), and the value of \(m\)-th class is calculated by  
\[\mathbf{y}_{coarse}[m]=\text{Softmax}(h_{CLS\_coarse}(\mathbf{c})[m],\tau), \tag{8}\]  
where \(h_{CLS\_coarse}(\cdot)\) denotes a two-layer non-linear fully connected network and \(C_{coarse}\) represents the number of coarse-level classes. The classifier is optimized according to the cross-entropy loss, \(L_{CLS\_coarse}\). The third one learns \(C_{coarse}\) classifiers at the fine level, each of which is sensitive to the fine-level classes belonging to a specific coarse-level class. These classifiers output prediction matrix \(\mathbf{Y}_{fine}\in\mathbf{R}^{C_{fine}\times C_{coarse}}\), and the probability of \(n\)-th fine class of the final prediction \(\mathbf{y}_{fine}\) is  
\[\mathbf{y}_{fine}[n]=\text{Softmax}((\mathbf{y}_{fine}\cdot\mathbf{y}_{coarse})[n]). \tag{9}\]  
Note that during the pre-training procedure, we replace the \(\mathbf{y}_{coarse}\) by the one-hot ground-truth coarse-level labels in the first epoch for steady optimization, and then set \(\tau\) in Equation (9) to 1 in the following training epochs. The total loss of reaction classification is formulated as  
\[L_{RC}=\lambda_{1}\Big{(}L_{CLS\_coarse}+L_{CLS\_fine}\Big{)}+\lambda_{2}L_{CLS \_global}+L_{CL\_coarse}+L_{CL\_fine}, \tag{10}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are hyperparameters. At the inference procedure, we select the category index with the largest value as the final result.  
For fine tuning on the reaction classification dataset without coarse-level annotations, we remove the component of \(L_{CLS\_coarse}\) in the loss function and gradually decrease the value of \(\tau\) in Equation (9).  
For the yield prediction, we adapt the model pre-trained by the reaction classification task and fine tune the adapted model for yield prediction. The fine-tuning process is just to concatenate the transformer-based feature \(\mathbf{x}^{t}_{global}\), the coarse-level encoding feature \(c\), and the fine-level encoding feature \(f\), after the 12-normalization. Then a regression model is used to learn the yield given the concatenated feature with the mean squared error (MSE) loss function.
## 6 Simulation Results and Discussion  
The efficacy of the proposed approach was verified and then tested on standard IEEE 33-bus, 69-bus, and 85-bus test networks. The proposed I-GWO algorithm was coded in MATLAB 9.11 and executed on an Intel Core i7-8700K (3.7 GHz, 32 GB RAM) with Microsoft Windows 11 installed. As discussed earlier, the backward/forward sweep technique was employed for load flow calculations. The parameters utilized in the simulation for the three test systems can be found in Table 2.  
Two scenarios were considered for each tested system for the optimal allocation and sizing of DSTATCOMs:  
* Scenario (1): This scenario did not consider the presence of renewable generations and assumed a fixed load demand.
* Scenario (2): This scenario considered the presence of renewable energies located at predefined positions, also considering uncertain generation and load uncertainty.