esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Training and Testing of ANN and WANN Models  
Several ANN structures are employed for meteorological and hydrological prediction, among which the Feed Forward Neural Networks are the most popular [64].  
In this study, a feed-forward multilayer perceptron (MLP)-based ANN model with a three-layer network (input, hidden and output) is employed for the estimation and prediction of daily SSL. For the network-training method, the back-propagation (BP)-based Levenberg-Marquardt (LM) learning algorithm with the hyperbolic tangent sigmoid (tansig) activation function in the hidden layer and a linear function (pureln) in the output layer was applied to configure the MLP model. Further, the model performance can be enhanced by an appropriate selection of the number of neurons within a hidden layer. By selecting too few neurons in a hidden layer, the learning process may be affected, whereas more neurons in a hidden layer might decrease efficacy in terms of computational time or may cause a network-overfitting problem. To date, no clear rule has been suggested in the literature for the selection of the optimum number of neurons in the hidden layer. However, [PERSON], et al. [41] proposed that the hidden layer neurons should range from \(\sqrt{2n}+m\) to the value \(2n+1\), where n and m represent the number of input and output nodes, respectively. In this study, the number of neurons in the hidden layer is increased from 3 to \(2n+1\) for both the ANN and the WANN models to avoid under- and over-fitting.  
Furthermore, the coupled WANN model is very close to the ANN model. Before applying the ANN model, the input time series data are pre-processed through the WT analysis tool. The WT analysis serves to decompose the original time series data into subseries data of different time scales using the DWT approach. The DWT has different types of mother wavelets, such as the Haar wavelet, Daubechies wavelet, Coiflet wavelet and biorthogonal wavelet to decompose the original time series data. In this study, the Daubechies level four (db4) mother wavelet is applied due to its better performance in the sediment transport processes [46]. After the decomposition of time series by WT, some resulted subseries need to be excluded if there is a poor correlation between the subseries and the observed data. Only the subseries data that have a significant correlation with the observed data are fed into an ANN model.
## 3 Materials and Methods  
### Concept of a Concentration Measurement System  
The concept of the system, in which the operator's electromagnetic brain signal is read and analyzed by a measurement module, is shown in Figure 1. After processing the data, a decision is made to forward the signal to the machine's control panel in order to generate a possible warning signal if the operator's minimum concentration threshold is exceeded [6].  
It was decided to use EEG signal because the electroencephalographic analysis makes it possible to determine the bioelectrical activity of the cerebral cortex [17], which, depending on the frequency, provides the following information:  
* Alpha waves (frequency range 8-13 Hz, amplitude 20-100 \(\upmu\)V)--these waves occur in the case of adults with complete relaxation and eyes closed. The waveform is sinusoidal in nature, with the largest amplitudes received above the visual cortex. The alpha rhythm is blocked during attention (especially visual attention) and mental effort.
* Beta waves (frequency range 14-35 Hz, amplitude up to 20 \(\upmu\)V)--these waves occur during states of activity, information processing, attention or arousal. They are received by the frontal-central region.  
Figure 1: Block diagram of the operator concentration measurement system model.  
* Theta waves (frequency range 4-7 Hz, up to 30 \(\upmu\)V)--they occur mainly in case of children. When it comes to adults, they appear in a state of deep meditation, sleep, trace, hyponsis, dreaming, intense emotion, etc.
* Delta waves (frequency range 0.5-4 Hz, amplitude 75-200 \(\upmu\)V)--very characteristic for deep sleep. They are collected from the entire surface of the skull.
* Gamma waves (frequency range 35-100 Hz)--these waves that occur in stressful situations during stage right, anxiety and so-called emergency situations.  
This work focuses on measuring the EEG signal using the biofeedback method, in which the measured signal is given back to the patient in the form of, i.e., a visualization or computer game. The feedback contains information about which brain waves are predominant at a given moment. These waves are used, for example, to control a virtual element when brain activity increases in the desired frequency band. The corresponding correlation of alpha, beta and theta wave activity allows the identification of the subject's level of concentration.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 3 Calibration Mission Impact over Systematic Errors
### Polarization Angle  
As previously explained, systematic errors in the measurement of the polarization angle produces a leakage from the E- to the B-mode signal. Additionally, a correlation between them is introduced which is reflected in non-vanishing TB and EB signals. Figures 2 and 3 show the systematic errors in the BB, TB and EB power spectra produced by a polarization angle rotation. These results have been obtained from the expected leakage for a given polarization angle mismatch, under the assumption that the angle error is common to all detectors.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Aperture** & **40 GHz** & **60 GHz** & **90 GHz** & **150 GHz** & **220 GHz** & **350 GHz** & **650 GHz** \\ \hline
0.3 m (LiteBIRD) & 24 m & 36 m & 54 m & 90 m & 132 m & 210 m & — \\
1.3 m (CORE) & 451 m & 676 m & 1014 m & 1690 m & 2479 m & 3943 m & 6760 m \\
1.5 m (PICO) & 600 m & 900 m & 1350 m & 2250 m & 3300 m & 5250 m & 9000 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Far Field Distance for different telescope apertures and frequency bands.  
Blue curves represent the spurious signal produced by 30, 10, 3 and 1 arc-minutes of polarization rotation in both figures. Primordial B-mode (solid black lines), gravitational lensing B-mode (dashed black) and the EE (solid green) signals are also plotted for reference in Figure 2. It is important to note that 30 arc-minutes (0.5\({}^{\circ}\)) of rotation is approximately the current error level set by the systematic uncertainty of celestial calibration sources as Tau-A. The calibration sources of L2-CalSat would allow the reduction of any polar angle error below the 1 arc-minute blue curve. This error is sufficiently low for current and future experiments, both in the ground (like the CMB Stage IV [31]) and from space (like LiteBIRD) that aim at a sensitivity in r of around 0.001. Notice that if the correction is not made with enough accuracy, this will bias the detection of the B-mode, mostly at the recombination peak.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Case No**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Channels’**} & \multirow{2}{*}{**Hybrid**} & \multirow{2}{*}{**Satellite Channel**} & \multirow{2}{*}{**Average Rejection**} \\  & & **Expected SNR** & & & **Usage** & **Rate** \\  & & **[dB]** & & **[Mbps]** & **[\% of Time]** & **[\%]** \\ \hline
1 & baseline & 0 & 1.03 & 0.03 & 0 \\
2 & baseline & 3 & 2.84 & 2.04 & 0 \\
3 & baseline & 6 & 5.41 & 11.9 & 0 \\
4 & baseline & 9 & 9.17 & 22.73 & 0.07 \\
5 & baseline & 12 & 17.83 & 13.85 & 0.16 \\
6 & baseline & 15 & 22.10 & 0.23 & 0.60 \\
7 & LSTM & 0 & 1.63 & 0.01 & 0 \\
8 & LSTM & 3 & 3.77 & 1.47 & 0 \\
9 & LSTM & 6 & 6.56 & 12.47 & 0 \\
10 & LSTM & 9 & 11.02 & 21.6 & 0.01 \\
11 & LSTM & 12 & 20.12 & 7.58 & 0.06 \\
12 & LSTM & 15 & 22.10 & 0.85 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results. value of about 2 Mbps. The decrease in the rejected frame percentage is also notable, and although it is low in both cases, it significantly decreases for the LSTM implementation. One more interesting characteristic that can be observed for test cases 4 and 10 is the distribution of the used MODCODs. The occurrences of different MODCODs for channel 1 (CH1), channel 2 (CH2) and the hybrid communication are given for test case 4 of the outdated information (OI) and test case 10 for the LSTM neural network (NN) in Figure 9.  
A similar trend as shown in Figure 8 can be observed in that the overall used MODCODs are changed by LSTM but not drastically in comparison to the outdated information. In general, the same MODCODs can be observed in both cases, most likely influenced by the shape of the simulated SNR values for both channels. The distribution, however, is shifted, with LSTM ensuring that higher MODCODs are more frequently used. For both  
Figure 8: The rejected frames and information bitrate over time for test cases 4 and 10. Figure 9: The rejected frames and information bitrate over time for cases 4 and 10.  
individual channels, the main increase between the algorithms can be seen in the frequency of the MODCODs indexed at 15 and 16 (16 APSK 26/45, 16 APSK 3/5), while the lowering of occurrences of lower MODCODs is most prominent for the MODCOD with index 14 being 16 APSK 8/15. For the hybrid communication, we can see that no lower MODCODs are prominent in the distribution, and the main shift is lowering the 16 APSK 8/15 occurrences in favor of 16 APSK 26/45 and 16 APSK 3/5. This shows that the improvements LSTM can make are not distributed evenly across all MODCODs, but rather in a given range, which stands in line with the previous conclusions that for extremely high SNR values and hence MODCOD, the LSTM neural network simply does not offer much in comparison to outdated information.
## 4 Model to Minimize Cost Dynamically
### Local Computing in the IoT Device  
Each mobile device has a respective number of CPU cores (\(PL_{j}=\{p|_{j,1},p|_{j,2},\ldots,p|_{j,n}\}\)). The energy consumption is computed in Equation (4) based on a total number of CPU cycles (\(Cc_{i}\)), operating frequency (\(f_{local,j,k}\)), voltage supply (\(V_{local,j,k}\)), and on the effective commutative capacitance (\(C_{local,j,k}\)) of each core. Equation (4) computes the local dynamic energy consumed for the IoT device in each task.  
\[E_{i,local}=P_{i,local}*T_{i,local} \tag{4}\]  
where,  
\[T_{i,local}=\frac{C_{cT_{i}}}{f_{local,j,k}} \tag{5}\]  
\[P_{i,local}=C_{local,j,k}*V_{local,j,k}^{2}*f_{local,j,k} \tag{6}\]  
Considering battery level and latency as model constraints, a device \(D_{j}\) must decide whether it is more appropriate to process the task locally or remotely. As the battery level is a critical factor in the decision, the system will appreciate a policy that reduces energy consumption. The local cost of one task \(i\) is expressed in Equation (7).  
\[Cost_{i,local}=u_{localT}*T_{i,local,total}+u_{localE}*E_{i,local} \tag{7}\]  
The coefficients \(u_{localT}\in[0,1]\) and \(u_{localE}\in[0,1]\) are weightings, where \(u_{localT}+u_{localE}=1\). These variables represent a trade-off between execution time and energy consumption and minimize one of the costs, according to [PERSON] et al. [4]. The DVFS associated overhead rate is between 0.02% to 2% in the best- and worst-case scenarios, according to [16]. The cost overhead for this approach represents 2% in our model, built into these trade-off coefficients.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
###### Abstract  
With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links' channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link's transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks.  
Keywords:fifth generation; wireless backhaul routing; massive MIMO; utility +
Footnote †: journal: Accepted: 14 October 2020; Published: 17 October 2020
## 6 Experimental Scenarios and Results
### Throughput  
Throughput is generally used to determine how well SDN routers and controllers can handle traffic and how efficient they are at this task. This test measures the number of packets sent per second in the case of OSPF, while SDN measures the number of flows installed on the devices. Equation (1) presents a simple way to calculate the throughput.  
\[Throughput=maximum\_receiver\_bandwidth/round-trip\_time \tag{1}\]  
The iperf3 tool obtains the maximum receiver bandwidth, while a simple ping returns the round-trip time. Figure 15 shows the results of this test. As is evident, the results show a better performance in the case of conventional strategies. However, this advantage may be compromised in more extensive networks where the OSPF protocol needs to describe all router neighbors.
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Figure 3 shows DSC curve that indicates the energy consumption property of cellulose and CMSC samples. The DSC thermogram exhibits an early endothermic peak with an enthalpy change observed at 90-110 \({}^{\circ}\)C for all samples. This minor enthalpy change is attributed to desorption of moisture as water trapped in the polysaccharide structure is released. The DSC profile of cellulose B shows an endothermic peak at 250 \({}^{\circ}\)C due to non-cellulose constituents such as hemicellulose [13]. As the temperature increases further, the DSC profile for all cellulose samples shows an endothermic peak at around 350-360 \({}^{\circ}\)C. However, cellulose A showed the most prominent endothermic peak at about 355 \({}^{\circ}\)C. This could be due to the increase in compactness of the crystallinity structure and involves the breakdown of intramolecular interaction and the decomposition of the cellulose [49]. The major enthalpy change for CMSC (F) occurs at 310 \({}^{\circ}\)C exhibiting exothermic peak due to the degradation of cellulose with the carboxylate functional groups. The thermal scission of the carboxylate groups resulted in the evolution of CO2 from the corresponding cellulose backbone, and this could be a probable thermal mechanism for CMSC thermal transitions.  
Figure 2: TGA (Thermogravimetric Analysis) of commercial cellulose (**A**); commercial lignin (**B**), cellulose A (preparation 1) (**C**); cellulose B (preparation 2) (**D**); cellulose C (preparation 3) (**E**); cellulose D (preparation 4) (**F**); cellulose E (preparation 5) (**G**); and CMSC (**H**).
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
###### Abstract  
In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement.  
Simultaneous Wireless Information and Power Transfer (SWIPT); Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA); iterative Multi-User Detection (iterative MUD); Signal to Interference and Noise Ratio (SINR); Power Allocation +
Footnote †: journal: Journal of Communications and Information Theory
###### Abstract  
Semiconductor optical amplifier (SOA) is considered an excellent candidate for power amplification at O-band due to its low cost and small footprint. In passive optical networks (PONs), SOA is popular as a booster and pre-amplifier to improve the link power budget. However, whether as a booster or pre-amplifier, SOA will induce different degrees of nonlinearity when the output power is high, which degrades the transmission performance of the system and leads to a limited receiver dynamic range. In this paper, we experimentally demonstrate the feasibility of using SOA in both transmitter and receiver sides for power budget improvement in 100 Gb/s/\(\lambda\) four-level pulsed amplitude modulation (PAM-4) time division multiplexed PON (TDM-PON) system at O-band. For compensating the linear and nonlinear impairments induced by transceivers and SOA, a look-up table (LUT) pre-compensation at the optical line terminal (OLT) side and a simple feed-forward equalizer (FFE) at the optical network unit (ONU) side are adopted for downstream transmission. For upstream transmission, a 2 nd-order Volterra nonlinear equalizer (VNLE) is utilized at the OLT side, and no pre-compensation is used at the transmitter of the ONU, which releases the digital signal processing (DSP) pressure of ONUs in a multi-user scenario. For the soft-decision FEC (SD-FEC) threshold (\(1\times 10^{-2}\)), the IEEE PR-30 power budget requirement is met, and >18 dB dynamic range is achieved in both 25 km downstream and upstream transmission.  
Keywords:optical fiber communication; semiconductor optical amplifier (SOA); passive optical networks (PON); digital signal processing (DSP); pulse amplitude modulation (PAM) +
Footnote †: journal: _micromachines_
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 2 Experimental Methods
### Experimental Materials  
The materials used in this study were carefully selected to simulate real-world cuttings generated during horizontal drilling operations. The following paragraphs detail the types of materials and their role in the experiment:  
(1) Shale cuttings.  
The cuttings used in the experiment were collected from actual shale formations drilled with a PDC bit. These shale fragments exhibited a flaky, cuboid morphology, with dimensions typically ranging between 3 and 8 mm in length, 3 and 5 mm in width, and 1 and 2 mm in height (Figure 6). Due to the combined effects of drilling tool abrasion, fluid flow, and particle collisions, the edges of the cuttings became blunted, and their shapes remained predominantly irregular and flaky. These flaky cuttings were chosen as they more accurately represent the types of cuttings found in horizontal shale wells, where migration dynamics differ from those of spherical particles.  
(2) Simulated cuttings.  
To compare the behavior of different particle shapes, standard glass spherical particles were used alongside the shale cuttings. These spherical particles were selected because their uniform shape allows for a comparative study of how particle geometry affects migration and deposition. Both the flaky shale cuttings and spherical particles had similar densities (2.7 g/cm\({}^{3}\)) to ensure that any observed differences in migration were primarily due to shape, and not weight or material composition.  
(3) Cuttings classification.  
The cuttings were divided into five different types based on their size and shape, as outlined in Table 1. Types 1 through 3 represent shale cuttings of varying dimensions, while Types 4 and 5 were spherical glass particles used as controls. The dimensions of the shale particles varied, with lengths ranging from 1 mm to 4 mm, and widths and heights adjusted proportionally.  
Figure 5: Cuttings separation system.  
(4) Purpose of shape comparison.  
By comparing the migration behavior of flaky shale cuttings to that of spherical glass particles, the experiment aimed to highlight how particle shape impacts the formation of cuttings beds and the overall efficiency of cuttings transport. Flaky cuttings, due to their irregular shape, are more prone to forming beds and require greater critical velocities for movement, while spherical particles tend to settle less readily and migrate more easily through the annulus.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 5 How the SecAIS Works
### SecAIS Source Authentication
#### 5.1.3 Retrieving the Original AIS Message  
All the conventional AIS devices that receive the two broadcasted ID8 AIS messages are able to decode them and retrieve the SecAIS application data. The sequence of actions of the conventional AIS device of the Receiver for doing so are as follows:  
1. Step 1: receives the encoded ID8 AIS message containing the PVT of the Transmitter;
2. Step 2: decodes the encoded ID8 AIS message containing the PVT of the Transmitter;
3. Step 3: receives the encoded ID8 AIS message containing the original signed message, the timestamp, and the variable cryptographic parameters of the signature (R, S);
4. Step 4: decodes the received ID8 AIS message.  
Screenshots depicting the various stages of the process are shown in Figures 8-10. The conventional AIS device of the Receiver is again simulated by the online AIS VDM/VDO decoder as it was on 6 October 2019; the browser used was Firefox(r) Quantum version 67.0.1 (64 bit).