esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods  
### Datasets and Data Processing  
We research evaluated HPNet against a benchmark dataset called RBP-24, which contains 24 sub-datasets for 21 RBPs. In this dataset, 23 sub-datasets were derived from doRiNA [34], and 1 sub-dataset was derived from crosslinking immunoprecipitation (HITS-CLIP) experiments [35]. The doRiNA experiments employed photoactivated ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) to determine binding sites. For each sub-dataset, positive samples were determined using CLIP experiments. Each positive sample had a viewpoint region of 12-75 nucleotides, the binding region determined in biological experiments. Multiple nucleotides were extended to both sides, with the viewpoint as the center, to provide more associated information from the context of the viewpoint. Negative samples were obtained by modifying positive samples when there was no supporting evidence for binding sites. In the original RBP-24 dataset, each sub-dataset consists of training and test sets. In this research, 20% of the training set was randomly selected as the validation set and 80% as the training set. Except for ALKBH5 and C7 ORF85, each sub-dataset contained 500 positive and 500 negative samples for the test dataset. The details for the original RBP-24 dataset are shown in Supplementary Table S1.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.6 CryptoNight  
CryptoNight is designed to support efficient mining on CPUs while intentionally being less effective on GPUs and FPGAs. The CryptoNight algorithm also boasts resistance against ASICs, and it is used to mine those coins that implement the CryptoNote protocol, with Monero being a notable example. Since the CryptoNight algorithm is composed of a loop structure where memory reads and writes occur repeatedly, it is very sensitive to the latency of the memory, meaning its performance is strictly a function of the memory. This memory-centric characteristic is crucial, and the outcome of these memory-intensive operations has been determined to be used in a later step by the hash function, and this process will produce the output, which is the potential block solution. A crucial design decision within the algorithm was to guarantee that the working data size aligns with the shared cache memory per core in a contemporary CPU. This category of memory exhibits a markedly reduced latency in comparison to conventional system DRAM or a GPU's VRAM, so this design choice imparts a substantial efficiency advantage when running CryptoNight on a CPU as opposed to a GPU [66].  
The following steps are involved in the CryptoNight algorithm: In the initialization phase, with parameters \(B=1600\) and \(C=512\), the hash input is initialized using the Keccak function (SHA-3). The last parameters of Keccak (bytes 0 to 31) are interpreted as an AES-256 key and expanded into 10 round keys. During the block extraction phase, the bytes between 64 and 191 are extracted as eight blocks of 16 bytes each to be later encrypted. Finally, the required data are assigned to the scratchpad, having a size of almost 2 MB. In the scratchpad operations stage, an XOR operation is performed among the first 63 bytes of Keccak with the goal of initializing \(A\) and \(B\) constraints, both of length 32 bytes. The variables and their processing are used to enforce a continuous read-write loop 524,288 times in the scratchpad to ensure that the algorithm is tailored for the latency of the memory. The last hash sequence of the data previously obtained is calculated in this final hash calculation phase [67].
# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 2 Combining Upwind Sprint and GA for CPSL
### Adapting GA for CPSL
#### 2.2.1 Behavior Selection Scheme  
As depicted in Figure 2, the plume-finding behavior comprises the D-behaviors of all SNs, whereas the pollution source searching behavior comprises U-behaviors and C-behaviors. In other words, all SNs conduct the D-behavior when the plume-finding/re-finding behavior is activated, whereas U-behavior and C-behavior are candidate elementary behaviors for the SNs when pollution source searching behavior is activated.  
For behavior selection, two time-cycle thresholds are defined: \(lld_{i}\) (where \(i\) = 1, 2,..., _n_) and \(gld\). \(lld_{i}\) is the time-cycle duration since the \(i\)-th SN obtained an above-threshold concentration measurement until the present. \(gld\) is the time-cycle duration since the last above-threshold concentration measurement of the \(MSN\) until the present. Thus, the value of \(gld\) can be calculated as  
\[gld=\min_{i\in[1,n]}lld_{i}. \tag{3}\]  
Moreover, \(th_{l}\), \(th_{a}\), and \(th_{f}\) are three predefined time-cycle thresholds that satisfy \(th_{l}<th_{a}<th_{f}\), where \(th_{l}\) is a small number. \(lld_{i}<th_{l}\) means the \(i\)-th SNs are still moving inside of the plume profile. At the \(k\)-th cycle time, the activated compositional behavior and the elementary behavior activated for the \(i\)-th SNs are denoted as \(B\) and \(b_{i}\), respectively. The behavior selection scheme of the uGA, which corresponds with line 3 in Algorithm 2, can be represented as follows.  
\[B=\left\{\begin{array}{ll}\text{Plume Finding}&\text{if }th_{a}\leq gld<th_{f} \text{ or plume not detected}\\ \text{Source Searching}&\text{if }gld<th_{a}\end{array}\right. \tag{4}\]  
\[b_{i}=\left\{\begin{array}{ll}\text{D}-\text{behavior}&\text{if }th_{a}\leq gld<th_{f} \text{ or plume not detected}\\ \text{U}-\text{behavior}&\text{if }gld<th_{l}\text{ and }lld_{i}<th_{l}\\ \text{C}-\text{behavior}&\text{if }gld<th_{a}\text{ and }th_{l})lld_{i}\\ \end{array}\right. \tag{5}\]  
According to Equations (4) and (5), the circumstance of the MSN is classified as \(gld<th_{l}\), \(th_{l}<gld<th_{a}\), and \(th_{a}\leq gld<th_{m}\) and \(gld\) equals \(th_{m}\):1. \(gld<th_{l}\): At least one SN is currently keeping in contact with the plume. In this circumstance, as a cooperation mechanism, the newly detected plume information can be exploited by the SNs.
2. \(th_{l}<gld<th_{a}\): All \(n\) SNs have lost contact with the plume. However, the previously detected information is still time-efficient and can be exploited to recontact the plume.
3. \(th_{a}\leq gld<th_{m}\): All \(n\) SNs have lost contact with the plume for a long time. It is necessary to explore the uncovered search space for recontacting the plume.
4. \(gld\) equals \(th_{m}\): The CPSL process is considered as failed.
# Adaptive Aggregated Attention Network for Pulmonary
## 2 Related Works
Their proposed attention mechanism was effective in feature refinement, but the spatial attention module could be further improved. Multi-scale, multi-resolution, and residual connection problems are not considered in their spatial attention network, which are very important for classifying lung nodules. To extract more features of nodules, researchers tended to differentiate nodules with the fusion of deep learning features, raw nodule features, and hand-crafted nodule features. [PERSON] et al. [18] fused gray level cooccurrence matrix (GLCM) features, Fourier shape features, and deep learning features and utilized AdaBoosted backpropagation neural network to differentiate nodules. Moreover, they fused the classification results of different classifiers. They tested their architecture on the LIDC-IDRI dataset, and the results proved that their fusion strategies were effective. [PERSON] et al. [19] fused deep features and hand-crafted features which contained morphological, color, or textual features of the nodules. Moreover, they utilized cascaded classifiers to differentiate the hybrid features. [PERSON] et al. [20] fused radiological quantitative image features and 3D deep features for patient-level nodule classification. [PERSON] et al. [21] fused local binary pattern-based texture features, histograms of oriented gradient-based shape features, and deep features which were extracted by 3D DPN. Finally, they utilized GBM to differentiate benign and malignant nodules. For the above hybrid features methods on lung cancer diagnosis, it was proven that combining manual features with deep learning features can further improve the accuracy of pulmonary nodules recognition [18; 19; 20; 21], but the extraction of manual features still has the same problems as the traditional methods. To utilized the multi-attributes features and multi-scale features, [PERSON] et al. [22] proposed a multi-stream network for nodule classification, and they constructed a new loss function to overcome the imbalance of different attributes. However, the setting of the weight parameters between different tasks is crucial and unreasonable weight parameter settings will cause the network to fail to converge during multi-task training. However, the setting of the weight parameters between different tasks is manually set in their work, which causes the model to be unstable, and it is very difficult to debug the parameters. Therefore, to tackle these challenges, 3D DPN is utilized as our trunk network, which is effective in avoiding overfitting. Moreover, a multi-scale spatial attention network is proposed to capture multi-scale spatial attention features. The residual attention network is used to capture multi-resolution spatial attention features, and the squeeze-and-excitation attention network is used to capture the fine-grained features among channels automatically. Finally, we combine the deep features with the raw image pixels to differentiate malignant and benign nodules.
## 2 Broadband System
### Wireless Broadband System
#### 2.2.5 Terrestrial Wireless Broadband  
Wireless broadband systems offer effective ways of delivering wireless connections between the users' devices and communication networks. Services such as high-speed data, telephony, and other multimedia applications can be effectively supported and delivered  
Figure 6: A typical FSO system.  
to unspecified areas within subscribers' locations such as homes and offices [5,19]. In this subsection, we present several terrestrial wireless broadband technologies that are generally employed for delivering wireless connectivity.
## 3 Results and Discussion  
Development of a Mathematical Model to Establish a Relationship between Amott-USBM Wettability and NMR T2 LM  
From the Amott-USBM test conducted, samples primarily showed water-wet wettability states. However, using the Amott-USBM scale used in this study, samples 9, 10, and 15 showed moderately water-wet conditions. Samples 8 and 62 indicated water-wet and strongly water-wet conditions, respectively (Table 5). This scale was used to analyse the various heterogeneous wettability states in existence. From the cross-plot of the NMR T\({}_{2}\)LM log data and the Amott-USBM experimental data of the core samples, it was observed that there is some form of correlation between them. However, with the understanding of wettability in a reservoir, there can never be only hydrocarbon in the pore space as there will always be irreducible water. The relationship should not be less than or equal to 0 for the Amott-USBM index with this assumption. This is because an oil-wet system is classified in the negative (\(-1\) to \(0\)), and water-wet is positive (0 to 1). Therefore, an exponential function is used, making it impossible for the equation to return negative wettability index values (see Figure 6). This yielded a correlation coefficient of 0.95, and consequently, a new formula to quantitatively predict wettability from NMR T\({}_{2}\)LM is achieved as:  
\[\hat{\text{w}}=\pm 0.0528e^{0.0081(\log\text{T})\times\text{T})} \tag{11}\]  
where \(\hat{\text{w}}\) refers to the Amott-USBM wettability index and T represents the average NMR T\({}_{2}\)LM against water saturation distribution within the reservoir interval. Since NMR response in the field could be affected by other complicating conditions such as mud filtrates and their degree of saturation, the applicability of the proposed formula will have some limitations. Hence, the NMR log affected by field conditions needs to be adjusted to cater for their effects. The unavailability of sufficient core wettability and NMR log data provides a major uncertainty. However, from the cross-plot, high T\({}_{2}\)LM representing the gas phase correlates to high Amott-USBM value, representing water-wet to strong water-wet conditions. This analysis validates the plot, and more data will help improve the accuracy of the proposed mathematical formula.
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 2 Experimental Setup
### Thrust Measurement Data Acquisition  
In order to ensure reproducible thrust measurements, the following measurement procedure was introduced:  
1. auto zeroing calibration;
2. thermal drift measurement; (duration \(\sim\)20 s)
3. activation of the thruster; (settling time \(\sim\)50 s)
4. thrust measurement in the steady state of the balance;
5. switching off of the thruster.  
The signal evolution during each procedure step is shown in Figure 5. After the automatic calibration in step 1, which eliminates the accumulated thermal drift, the thermal drift of the system at the specific measurement was recorded in step 2 in order to be used in the post processing of the thrust measurement. The response of the thrust balance to the activation of the thruster took approximately 50 s, after which the system could be considered in steady state. Hence, the actual thrust measurement was evaluated after post-processing the raw thrust measurement data.  
Figure 5: Raw data of a typical thrust evaluation throughout 5 steps of the thrust measurement procedure (blue); corrected evolution of the thrust over the measurement interval without the thermal drift (red).  
In order to prevent thermal damage of the thruster and the mounting system, a cool down phase was included between repetitions of single thrust measurements. This was necessary due to the rapid anode heat up, which is especially the case at higher pulse rates and energies.  
The average thrust per pulse \(\bar{T}\) is calculated as the mean value of thrust over the measurement time when the steady-state is achieved. It can be expressed as:  
\[\bar{T}=\frac{\int_{0}^{t_{meas}}T(t)\,dt}{t_{meas}} \tag{1}\]  
with \(t_{meas}\) the total measurement time during the steady state of the thrust balance and \(T(t)\) the actual measured thrust.
# Interface Characterization within a Nuclear Fuel Plate
## Materials and Methods
### Test Procedures
#### 3.3.1 Cladding-Cladding Test Steps  
The test procedure for the cladding to cladding characterization is a series of process steps. The process steps are designed to ensure repeatability and enable direct comparison of the measurements between fuel plate specimens. The process steps are slightly different for the cladding to cladding characterization since the interface strength is significantly higher than fuel-cladding bonds. These steps have been closely followed.  
1. Perform cladding to cladding velocity measurements at each test location. Record and use the appropriate average cladding velocity value for the corresponding plate in the fuel-cladding tests.
2. For the given test specimen, use test locations as shown in Figure 4 and Table 1. The lower left corner is used as a fiduciary mark.
3. Perform ultrasonic C-scan (UT) prescan at each location to confirm the interface is intact at the start of testing.
4. Test location #1 with laser amp delay at maximum power (40 \(\upmu\)s \(\approx\) 2.25 J) to determine if interface debonds.  
1. If interface debonds, lower energy by steps of 0.25 J, using current amp-energy curve, to locate debond energy level.
2. If interface does not debond, test other locations at maximum power. Add two additional shocks to the last location and repeatedly shoot at maximum power to debond.
5. Perform standard LST testing on the following matrix locations with the specimen in standard orientation, see Table 1.  
Figure 4: Example of clad-clad plate with locations for shock testing indicated.  
The LST locations have been chosen to be toward the center of the plate to reduce edge effects. The spacing between the LST locations are spread out to keep the locations pristine. Typically, LST is not able to debond cladding-cladding interfaces.
## 2 Materials and Methods
### Model Implementation  
In this work, the MOD17, TL-LUE, MTL-LUE, TG, MTG, BEPS, and BTL models were all run at the spatial resolution of 30 m. As for the time resolution, MOD17, TL-LUE, MTL-LUE, BEPS, and BTL models were run at the daily step, whereas the time step of TG and MTG was 8-day. The main input data of these seven models are shown in Table 2. In this work, the NEE measurements are not enough to build reliable models for tower-based GPP estimation [76; 80]. Due to the unavailability of tower-based GPP, this work calibrated the model parameters of MOD17, TL-LUE, MTL-LUE, TG, MTG, BEPS, and BTL using reference values from previous studies (as shown in Supplementary Table S1). [PERSON] et al. (2016) [35] optimized the sensitive parameters of the TL-LUE and MOD17 models at global tower sites, which could provide reliable values of model parameters. Moreover, MTL-LUE has the same unknown parameters as the TL-LUE model. The different canopy albedo (i.e., \(\alpha\)) values across vegetation types were set according to [PERSON] et al. (2015) [37], and all other parameter values for MOD17, TL-LUE, and MTL-LUE in different vegetation types were obtained from [PERSON] et al. (2016) [35]. [PERSON] and [PERSON] [23] optimized the sensitive parameters of TG (\(m_{TG}\)) and MTG (\(m_{MTG}\)) at 16 mountainous sites, and [PERSON] et al. (2017) [81] calibrated the minimum, optimum, and maximum LST for vegetation photosynthesis at 155 tower sites. In this work, the parameter values of TG and MTG across vegetation types were obtained from [PERSON] and [PERSON] [23] and [PERSON] et al. (2017) [81]. [PERSON] et al. (2018) [82] adopted BEPS to simulate carbon and water fluxes over China during 2003-2012, and their GPP estimates matched well with tower-based GPP at 38 sites. In this work, the key parameter values of BEPS across vegetation types were mainly obtained from [PERSON] et al. (2018) [82] and [PERSON] et al. (2019) [83]. In the BTL model, the biophysical parameters were set to the same values in BEPS, and the values of the hydrological parameters were set according to [PERSON] et al. (2009) ([PERSON] et al., 2009).
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 4 Performance Analysis
### DLSTM Network Training Results  
The DLSTM network was trained by the LLR sequences and the one-hot encoding of first error bit. The simulation parameters for \(P(64,32)\) are shown in Table 2. As described in Section 2, a basic LSTM unit consists of three gates and one cell state. Each gate and cell state is a fully connection layer with \(N\) (hidden size) neurons. Thus, a layer of LSTM actually has four fully connection layers. Equation (9) can also be written as  
\[\text{gate}(\mathbf{x}_{t})=\sigma(\mathbf{W}\cdot[\mathbf{h}_{t-1},\mathbf{x }_{t}]+\mathbf{b})\enspace. \tag{16}\]  
Therefore, one layer of the basic LSTM network has \(4(h_{s}(h_{s}+x_{d})+h_{s})\) parameters, where \(h_{s}\) is the size of the hidden layer and \(x_{d}\) is the dimension of the input data. As shown in Table 2, a layer of basic LSTM has \(4\times[64\times(64+64)+64]=33,024\) parameters. Thus, a layer of DLSTM has \(2\times 33,024=66,048\) parameters. Dense denotes the fully connection layer. The \(N\)(hidden size)-dimensional vector output from the DLSTM network is used as the input to the dense layer, so the dense layer has \((h\_s\cdot K+K)\) parameters, where \(K\) is the number of neurons in the dense layer.  
All the data are trained once for one epoch. In each epoch, the network calculates the accuracy and loss of the training set and validation set. After 30 epochs, the accuracy and loss changes for different codewords with \(E_{b}/N_{0}=1.0\) dB are depicted in Figure 11. It can be seen that, before five epochs, the accuracy of the validation set is higher than that of the training set. As the number of epoch increases, the accuracy of the training set starts to increase and outperform that of the validation set. Finally, the accuracy of both the validation set and training set converge to a stable value. It is observed that the DLSTM network converges and reaches a stable state after about 20 epochs.  
In this paper, a flipping set of size \(T_{1}\) is constructed for one-bit flipping. Figure 12 shows that, when the number of flips is smaller than the size of the CS, our flipping set has a higher accuracy in identifying the first error bit compared to the CS in [8]. Although CS  
Figure 10: Structure of the DLSTM-based robustness algorithm.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 2 Theoretical Background  
### Spatial Variability in the Ground  
As a natural component, the ground can exhibit variability even in layers that appear homogeneous. This spatial variability arises from various factors such as stress history, characteristics of the samples used, and deposition conditions, serving as a fundamental reason for the uncertainty in ground conditions [1,2]. Variability in the ground does not possess random characteristics, rather it tends to be determined by its spatial location. Expressing spatial variability in the ground with simple statistical figures like mean or variance is insufficient [21]. To understand the correlation of ground properties, two main factors have been primarily used, the \(CV\)[21] and \(CL\)[22], to delineate the spatial range where the correlation of ground characteristics is expressed. The \(CV\) mentioned above is a metric that numerically represents the degree of variation in observed data values within a specific area or space. The \(CV\) is used to assess the uniformity or heterogeneity in spatial distribution patterns, measuring how diverse data values are distributed across space. Moreover, the \(CL\) in spatial variability can describe the correlation between data points distributed in space. This indicates how data values measured at a specific location are correlated with data values at surrounding locations, revealing how similar or related they are spatially.