esa-sceva/satcom-chunk-collection huggingface dataset:
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 3 Results
### ERBB4 Was Significantly Upregulated in Independent Samples with NTRK3 Fusion  
Utilizing RT-PCR, we selected five RNA samples from papillary _BRAF_ V600E negative thyroid cancer FFPE blocks containing the most frequent _ETV6_-_NTRK3_ fusion and 10 RNA samples without _ETV6_-_NTRK3_ fusion. The _ERBB4_ expression measured by qRT-PCR was significantly higher in samples with the _NTRK3_ rearrangement (_p_-value = 0.004 in Mann-Whitney two-sided test) (Figure 8).  
Figure 5: The comparison of the normalized gene expression value distributions for the genes associated with nervous system development in normal thyroid gland tissues (first boxes), thyroid cancer without NTRK gene fusion (second boxes), with _NTRK1_ (third boxes) and _NTRK3_ (fourth boxes) gene fusions.  
Figure 6: The potential signal transduction and gene regulatory network acting in the thyroid tumors, with _NTRK1_ and _NTRK3_ gene fusions activating nervous system development genes. The revealed activated key master-regulator proteins ErbB84, Cool1, Noggin, and Dkk-1 (pink nodes on the top) through the intracellular signal transduction network (green nodes) activate the transcription factors (blue nodes) that, in turn, upregulate the genes of nervous system development and axon growth (light blue nodes in the bottom of the figure). The dotted lines indicate a transcription-translation link from some of these genes (_ERBB4_, _NOG_, _DKK1_, _ARHGEF7_) to the proteins encoded by these genes (positive feedback loops). The red shadows around key master regulators correspond to the expression fold changes in their genes in the tumors carrying _NTRK1_- (right half of the shadow) and _NTRK3_- (left half of the shadow) fusions, respectively.  
Figure 7: STRING network analysis of genes suggested in this study as participants in signal transduction from TrkA and TrkC activated by gene fusion formation.
## 3 Materials and Methods
### Data Processing  
Data quality in the MIMIC-III database is variable and requires cleaning. After trawling through the MIMIC database and downloading the records with both ABP and PPG waveforms present, the data was processed. Specifically, the data was filtered, and better quality segments were retained.  
The ABP and PPG signals were first filtered. A Hampel filter was applied to remove outlying points [14]. This replaces any outlying points in a window of the data with the window's median [14]. Both signals were then passed through a 6 th order, zero-phase, lowpass Butterworth filter, with a cutoff frequency of 25 Hz [8].  
The recorded waveforms contain poor quality segments, including flat signals, segments with values that are out of range (such as negative pressures), and distorted waveforms. These could be due to problems with sensor connections, sensor saturation, or errors in recording. Out-of-range values were evaluated as those with SBPs less than 80 mmHg or greater than 180 mmHg or DBPs less than 60 mmHg or greater than 130 mmHg, respectively [22]. Flat signals were detected by determining whether the gradient was approximately zero for more than 1.5 s.  
The integrity of the ABP waveform was verified in two ways. First, we confirmed that the signal's cardiac period could be detected using the WFDB library's beat detection tool [21], which returns the indices of the diastolic points. Then, a moving maximum window was applied with a window size of 1.92 s. This value was chosen empirically and led to a sequence containing 0 s for the regions of the signal that had irregular patterns and 1 s where the signal was satisfactory.  
The integrity of the waveform was also checked using the Pearson correlation coefficient [23], ensuring that uncorrelated segments of PPG and ABP were discarded. The phase shift that exists between the PPG and ABP signals was removed by taking the cross correlation function of the signals, given by:  
\[g(\tau)=\sum_{t}ABP[t]PPG[t+\tau] \tag{2}\]  
where the value of \(\tau\) that corresponds to the maximum peak in \(g(\tau)\) is the time offset between the PPG and ABP signals [22]. After removing the phase shift, the Pearson correlation coefficient \(r\) was calculated for each ABP and PPG pairing. Data segments with \(r\leq 0.8\) were discarded. After data cleaning, 93,309 segments from 1669 patients remained, totalling 890 h of patient data.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 2 System Model
### Channel Modeling and Simulation
**  
**The expected SNR** value of a channel does not necessarily imply changes in the morphology of the signal, but it does heavily influence the possibility and speed of data transmission. Hence, it is important to observe this parameter in order to analyze its interactions and influence on spectral efficiency and the transmission error rate, in combination with the other two considered characteristics that change the morphology of the SNR signal for a given channel. In general, a higher expected SNR value of a channel allows for higher spectral efficiency to be obtained and is considered a favorable condition. In this paper, a set of expected SNR values is considered, {0 dB, 3 dB, 6 dB, 9 dB, 12 dB, 15 dB}, to cover both extremes, the very high SNR, the very low SNR and several steps in between. The second characteristic that is observed in this paper is **the level of Doppler frequency shift**. This influence of Doppler frequency shift is modeled through the \(f_{Dm}\) parameter of the simulation and essentially influences the SNR characteristic of the channel in terms of speed of change. In terms of the SNR dynamic, the higher the \(f_{Dm}\) value, the quicker the changes in the SNR occur over time and the weaker the correlation of future SNR values to the previous ones. This makes the SNR value more complex to predict and can significantly impact communication performance. A set of values for the \(f_{Dm}\) parameter was considered, [5 Hz, 25 Hz, 50 Hz, 75 Hz, 100 Hz], and these cover a range of movement influences that can be considered residual movement after considering the movement corrections based on the GPS signal. The third characteristic of a channel that is considered in this paper is **the level of shadowing**. Based on the channel simulation described in the previous section, three scenarios were considered through the parameters shown in Table 2.  
The level of shadowing modulates how often and to what extent the SNR values drop to very low values. This can add to the difficulty of predicting future SNR values, but more prominently, it creates intervals in which no transmission of data could occur. The lower level of shadowing represents more favorable conditions, but it is of interest to see to which extent channels with higher levels of shadowing can be used and how reliable they can be. In combination with various levels of expected SNR and Doppler frequency shift, the observed levels of shadowing create a space of channel characteristics that can cover a wide range of scenarios. The total number of different generated channels amounts to 90 (6 expected SNRs \(\times\) 5 different \(f_{Dm}\)s \(\times\) 3 different shadowing levels). For each channel, at least 100.000 consecutive samples were generated for the purposes of training and evaluation. Each generated sample has a true SNR value present in the channel and an estimated SNR value available as input to the decision-making algorithms.
## 2 Materials and Methods
### Capsule-Transformer
#### 2.3.1 Transformer  
The DE features \(x_{11},x_{12},x_{13},x_{14}\in R^{d1}\) for frontal multi-band EEG signals, \(x_{21},x_{22},x_{23}\), \(x_{24}\in R^{d2}\) for temporal multi-band EEG signals, \(x_{31},x_{32},x_{33},x_{34}\in R^{d3}\) for parietal multi-band EEG signals, and \(x_{31},x_{32},x_{33},x_{34}\in R^{44}\) for occipital multi-band EEG signals, respectively, are inputted into the fully connected layer to obtain \(f_{ij}\in R^{d}(1\leq i\leq 4,1\leq j\leq 4)\), \(f=concat(f_{11},f_{12},\dots,f_{44})\in R^{N\times d}\). Where \(d1\), \(d2\), \(d3\), and \(d4\) are the dimensions of the frequency band features within each brain region, and \(d\) is the dimension of the feature after dimensional alignment, totaling 16 frequency band features, i.e., \(N=16\). Then, the intrinsic contextual connections of features in different brain regions and frequency bands are learned based on the Transformer encoder structure in the following process.  
The length of the feature vectors is first compressed to between 0 and 1 by a layer normalization (LN) structure, and the input feature vectors are mapped to the QKV space: \(Q=W_{q}f\), \(K=W_{k}f\), and \(V=W_{v}f\), where \(Q,K,V\in R^{N\times d}\), respectively. Then the scaled dot product is used to calculate the similarity between the frequency band features within the current brain region, between the current brain region and the features of other brain regions, and these similarities are normalized to the attention weights. Finally, the frequency band features of each brain region are weighted and summed with the corresponding attentional weights to obtain the output of the self-attention mechanism, as shown in Equation (3):  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d}}\bigg{)}V \tag{3}\]  
To obtain richer contextual information in different brain regions and frequency bands, this paper uses the multi-head self-attention mechanism (MSA) to generate multiple QKV spaces, and then the attention outputs computed in each QKV space are spliced and linearly transformed thus obtaining richer feature representations, as shown in Equation (4):  
\[MSA(Q,K,V)=Concat(Attention_{1},\dots,Attention_{h})W^{o} \tag{4}\]  
where \(W^{o}\) is the learnable parameter matrix and \(h\) is the number of self-attention heads, the detailed structure of MSA is shown in Figure 2.  
Figure 1: The structure of the proposed Capsule–Transformer method for EEG emotion recognition.  
Based on the above steps, a single Transformer encoder is calculated as shown in Equations (5) and (6):  
\[f^{\prime}=f+MSA(LN(f)) \tag{5}\]  
\[f^{*}=f^{\prime}+MLP\big{(}LN\big{(}f^{\prime}\big{)}\big{)} \tag{6}\]  
where \(f\), \(f^{*}\) denote the input and output of the encoder, respectively.
## 5 Implications and Discussion
### The Impact of a Fault Zone, Elevation, and Topographic Slope on CZ Thickness  
The presence of fault zones may have an effect on the thickness of the CZ. The presence of faults can lead to one of two outcomes: (i) a change in bedrock height levels caused by fault geometry changes, and (ii) an increase in the number of altered regolith layer fragments [19; 22; 23].  
In this study, we have identified different fault zones in each profile by the combination of ERT, seismic tomography, and MASW models. The results in these models indicate that fault zones have a clear impact on the thickness of the CZ. For example, the thickness of the CZ in the La Torrecilla profile (Figure 7) coincides with the presence of a fault zone between F1 and F2, specifically the AMF shear deformation zone defined by [PERSON] et al. [49]. The CZ surrounding the fault zone (h2) is thicker than the area outside the fault zone (h1 and h3). The thickness of the CZ in the La Salud North is also affected by the AMF shear deformation zone [49]. The thickest CZ in the La Salud North profile (Figure 8) is very close to the F1 fault (h2). Meanwhile, the thickening of the CZ in the La Salud South profile (Figure 9) appears close to the F3 fault, which is correlated to the S-AMF deformation zone (h3). In the Carrascoy profile (Figure 10), the ACNF fault zone controls the thickness of the CZ, with the thickened layer located near the F1 fault zone (h3) [27; 49].  
The effect of elevation in the thickness of the CZ has been analyzed by [PERSON] et al., [81]. Their study reported that lower elevations correlated well with thicker CZ. In the La Salud South profile (Figure 9c), the lowest elevation on the SE (ca. 420-430 m) coincides with the thickest CZ (h3). The differences in elevation across La Torrecilla and La Salud North profiles are relatively small, and the thickness of the CZ is thus more controlled by the presence of the fault zone.  
Different geophysical studies have identified differences in the thickness of the CZ depending on the topographic slope and orientation [8; 25; 78; 82]. In our study area, especially in the Carrascoy profile (Figure 10b), the Vs model shows clear differences in CZ thickness in the topographic asymmetry area:the CZ thickness in h1 has a relatively lower topographic slope than in the h2 thickness (Figure 10c). This seems to indicate CZ thickness differences is depending on the orientation of the slope, where the north-facing CZ 1 (h1) is relatively thinner than the south-facing (h2). It could also depend on the lithology that the northern part is the Guadalentin Depression, which is a relatively thick basin.
# eHealth Integrity Model Based on Permissioned Blockchain +
## 3 Discussion
### Performance  
All blockchain client and authority nodes store a copy of the blockchain. The algorithms for adding, removing, and verifying are polynomial time algorithms. It might appear that the most time-consuming operations are those responsible for searching transactions with a specific covert ID. The blockchain has an ordered data structure. Therefore, it can be stored in a data format allowing for the fast searching for elements with a certain key. Transactions with a given covert ID are scattered unevenly across the blockchain. There are several solutions that can be used to speed up the retrieval of transactions with a given covert ID, starting from using an additional tree data structure with cover IDs as leaves, having pointers to the transactions, and ending at mapping block structures to relational database tables.  
The integrity-verification algorithm is the most complex algorithm in the model. The time complexity of the algorithm is \(O(n^{2})\). The algorithm was implemented using C#, and two datasets were generated. The first test dataset consisted of 100 million random transactions that belong to one million users (100 transactions per user). The second one consisted of 100 different files with size ranging from 1 KB to 1 MB that simulates different medical documents. Hashes from those documents were inserted into the random transactions from the first set. All data transactions were stored in a Dictionary data structure that is an implementation of a hash table. The time complexity for the search operation for that data structure is \(O(1)\).  
The test was carried out on a test computer with an Intel Core i7-8750H @2.20 Ghz processor, 32 GB RAM, and an SSD drive. The purpose of the test was to measure how much time it would take for the integrity-verification algorithm to verify a set of 100 files (the second dataset) using transactions from the first dataset. Total execution time was 4718 ms, and almost all of that time was used for hashing documents. The time excluding hashing was only 64.3 ms, which is fast and suitable for practical purposes. The selection of 100 transactions from a 100 million transaction set took less than 0.01 ms, but such high speed required 16 GB of memory to store the transactions. In the test, RSA encryption with a 2048-bit key pair was used, and it took 64 ms to decrypt the 100 transactions. Other operations took 0.3 ms. The test results are an average after five repetitions.  
Additionally, the covert ID could be encrypted with other data in a transaction, so a blockchain could be publicly stored. However, in such case, a verifier during each verification would have to decrypt a complete blockchain, and then would have to select all transactions with a specific covert ID. This is possible, but would require enormous amount of computation when a blockchain contains millions of transactions, especially when using an asymmetric encryption algorithm.
## 2 Operation Principle  
Figure 1 shows the configuration of the proposed waveform generation scheme. In the proposed scheme, an optical carrier generated from a continuous wave (CW) laser was coupled into an commercial off-the-shelf DDMZM, which had two RF input ports and two independent DC biases. The sinusoidal RF signal was split into two channels by an RF power splitter, with one output port connected to the upper arm of the DDMZM and the other output port connected to the lower arm of the DDMZM via a phase shifter. The optical signal was modulated by the RF signal via the DDMZM to generate optical sidebands. Assuming the optical field of the carrier is expressed as \(E_{in}(t)=E_{in}\ exp(j\omega_{0}t)\) and \(V_{\pi}\) is the half-wave voltage of the DDMZM, the output optical fields of the upper and the lower arm are:  
\[\begin{split} E_{upper}(t)&=\frac{1}{\sqrt{2}}E_{in} (t)\exp[\frac{i\pi(V_{\text{DC1}}+V_{\text{RF}}\cos(\omega_{\text{RF}}t))}{V_ {\pi}}]\\ E_{lower}(t)&=\frac{1}{\sqrt{2}}E_{in}(t)\exp[ \frac{i\pi(V_{\text{DC2}}+V_{\text{RF}}\cos(\omega_{\text{RF}}t+\theta))}{V_ {\pi}}]\end{split} \tag{1}\]  
where \(V_{\text{RF}}\) and \(\omega_{\text{RF}}\) are the amplitude and frequency of the RF signal, and \(\theta\) is the phase difference of the RF signals applied to the two arms. \(V_{\text{DC1}}\) and \(V_{\text{DC2}}\) denote the two bias voltages applied to the upper and lower arms, respectively. Then, the optical field at output of DDMZM can be expressed as:  
\[E_{\text{out}}(t)=\frac{E_{in}(t)\exp\left(j\phi_{2}\right)}{\sqrt{2}}\Big{[}e^ {j\lambda\phi+j\beta\cos(\omega_{\text{RF}}t)}+e^{j\beta\cos(\omega_{\text{RF} }t+\theta)}\Big{]} \tag{2}\]where \(\phi_{1}=\pi V_{\rm{DC1}}/V_{\pi}\) and \(\phi_{2}=\pi V_{\rm{DC2}}/V_{\pi}\) are phase shift determined by the bias voltages, \(\Delta\phi=\left|\phi_{1}-\phi_{2}\right|=\left|\pi(V_{\rm{DC1}}-V_{\rm{DC2}})/V _{\pi}\right|\) represents the phase difference caused by the two bias voltages, and \(\beta=\pi V_{RF}/V_{\pi}\) is the modulation index of the DDMZM. When this optical signal was sent to a PD for intensity detection, the output current of the PD was given by:  
\[\begin{array}{ll}I_{out}(t)&=\frac{\Re|E_{\rm{in}}|^{2}}{2}\Big{[}2+2\cos \!\left[\Delta\phi+2\beta\sin\frac{\theta}{2}\cos(\omega_{RF}t+\frac{\theta}{ 2})\right]\Big{]}\\ &=\frac{\Re|E_{\rm{in}}|^{2}}{2}\Big{[}2+2\cos(\Delta\phi)\cos(2\beta\sin\frac {\theta}{2}\cos(\omega_{RF}t+\frac{\theta}{2}))-2\sin(\Delta\phi)\sin(2\beta \sin\frac{\theta}{2}\cos(\omega_{RF}t+\frac{\theta}{2}))\Big{]}\end{array} \tag{3}\]  
where \(\Re\) is the responsivity of the PD. It can be perceived from Equation (3) that the desired waveforms can be obtained by properly setting the coefficients of \(\beta\) and \(\theta\).
## 3 Results and Discussion
### Direct Impacts of Gasifier Uptake on Household Livelihoods
#### 3.2.2 Reduced Expenditure on Cooking Fuel  
The survey showed that 26% of households bought some or most of the firewood they used, with 85% of them buying headloads and 15% handcart loads. These households bought firewood 1 to 10 times per month. A headload of firewood cost 50 KES (0.5 USD) on average. There was a significant difference (\(p\) = 0.003) in the number of headloads of firewood used before and after gasifier by the 26% of households that bought firewood and a 38% decrease (400 KES (4 USD) to 250 KES (2.5 USD)) in the amount of money spent on fuel after the gasifier was introduced. The gasifier can use fuel materials from tree pruning and crop residues, which are accessible on-farm (Figure 6a), hence reducing the need to buy firewood, which could constrain the household budget [32].  
Before receiving the gasifier, 42% of the households cooked with charcoal which they obtained either through purchasing, making on their own farms, collecting from friends' farms or picking from the remains of three-stone open fires. Of the households that used charcoal, 71% bought it in various units of measure. For example, seven reported using half a bag per month, costing 500 KES (5 USD) on average. The gasifier produces charcoal (Figure 6b) and 98% of the households using the stove also harvested the charcoal. Eighty-five percent of these households stored the charcoal produced, as they were asked to in the research project, and had collected 0.5-13 kg of charcoal (average 5.1 kg per household) after approximately 2.5 months of gasifier use. This amount of charcoal was collected from gasifier stoves being used nine times a week [23]. On average, 63% and 37% of the households using the gasifier for fast-cooking and both slow and fast cooking foods collected 0.10 kg and 0.13 kg of charcoal, respectively, for each day they used the gasifier. With daily use of the gasifier, these households have the potential to increase their charcoal collection rate from 2 to 3 kg and from 2.1 to 3.9 kg of charcoal per month, respectively. Even though the households were requested to store  
\begin{table}
\begin{tabular}{c c} \hline
**Member(s) of the Household Involved in Collecting Firewood** & **Percent of Households** \\ \hline Female head only & 30.6 \\ Male head + female head & 16.3 \\ Female head + young female + young male & 22.5 \\ Female head + girls + boys & 16.3 \\ Male head only & 4.1 \\ Young male only & 2.0 \\ Male head + female head + girls & 2.0 \\ Female head + young female + boys + girls & 2.0 \\ Male head+ female head + young male + young female & 2.0 \\ Female head + young female + hired labour & 2.0 \\ \hline \end{tabular}
\end{table}
Table 1: Household members involved in firewood collection, according to the present survey.  
Figure 6: (a) Biomass from the farm ready for use in the gasifier and (b) the charcoal produced.  
the charcoal they produced for an upcoming biochar field participatory trial, 23.4% of the households were using it to cook and hence reducing money spent on charcoal.
# Digital Twin Framework for Road Infrastructure Management
## 1 Introduction
RQ3--How can a comprehensive DT framework that encompasses the common functionalities and components of existing road infrastructure DT models be defined? RQ4--How can the derived comprehensive DT framework be applied to the current road management practices? The remainder of this paper is structured as follows: Section 2 provides the methodology used in this study, and Section 3 presents background of the concept of DTs and the relevant standards in the context of the road infrastructure industry. Section 4 outlines the core contribution of this study--a DT framework (reference model) for road infrastructure management. This section begins with a detailed analysis of existing DT applications and reference models for road infrastructure management, including road infrastructure networks, bridges, tunnels, and other civil infrastructures and urban DTs. Based on this analysis, a set of DT layers is derived from existing initiatives. The proposed DT framework is then established by addressing both data-related aspects and functional requirements. Section 5 compares the proposed model with the ISO 23247-2 DT reference framework for manufacturing, highlighting key similarities and differences to enhance understanding and promote the applicability of the proposed model in the development of road infrastructure DTs. Section 6 provides the implementation scenario of the proposed road infrastructure DT framework with a case study. Finally, Section 7 discusses and concludes this paper by summarizing the key findings and discussing their implications, along with potential directions for future research.