esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Prototype
### Connection with ThingSpeak  
Once the prototype is working, it is important to select the payload format, which represents the way data are received and displayed on the network. By default, Cayenne LPP (low-power payload) will be selected; however, in this case, this has been changed to a custom format in order to program the decoder function so that the data are presented in the correct format, compatible with the platform we intend to use for data visualization, the ThingSpeak platform [66].  
Figure 8: Overview of the LoRa node and gateway devices and the application in the TTN: (**a**) gateway registration status information; (**b**) registration of the “apptestemp” application on the TTN; (**c**) registration of the LoRa node device in the application; (**d**) application registration information.  
In the received packet, we need to decode the parameter \"bytes\" that comes in the Cayenne LPP format and present the fields in JavaScript Object Notation (JSON) format [64], with the positions bytes(2) and bytes(3) representing the temperature times 10, which is necessary to proceed with the correction. The bytes(6) position represents the humidity as a double value, so it is also necessary to correct this value. To this end, we implemented the JavaScript function shown in Figure 9a, because the ThingSpeak platform works with predefined composites (field1, field2, etc.).  
Figure 9: (a) Decoder function compatible with ThingSpeak; (b) received message (payload); (c) TTN integration with ThingSpeak infrastructure.  
For example, when temperature and humidity are detected and sent on the LoRa network to the LoRaWAN gateway with payload 01 67 00 FB 01 68 72, as shown in Figure 9b, these are encoded as follows [67]:  
1. Device with temperature sensor: (Hex)--01 67 00 FB. The data channel is one (01), the type is temperature (67) and the value is 00 FB \(\Rightarrow\) 251 \(\Rightarrow\) 25.1 \({}^{\circ}\)C.
2. Device with humidity sensor: (Hex)--01 68 72. The data channel is one (01), the type is humidity (68) and the value is 72 \(\Rightarrow\) 114 \(\Rightarrow\) 57%.  
To register a data analysis application on the ThingSpeak platform, a registration is required, which is free in its basic version. After creating the channel, we selected the fields that we wanted to display and defined the metadata, field1--temperature and field2--humidity, according to what was defined in the decoder function (bytes, port). It is possible to have up to eight fields in a channel and GPS coordinates. The channel ID and channel write Application Program Interface (API) key are required to register the channel in the TTN (Figure 9c) and allow data communication. The channel also allows for the configuration of other parameters, as well as exporting the data in XML and JSON format (Figure 10).
# A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping
## 4 Conclusions  
The primary goal of this work is to offer a practical approach to bidirectional LSTM-RNN and GRU for eukaryotic DNA splice site identification and estimation. Bidirectional LSTM-RNN and GRU excels in processing massive quantities of sequential data, such as the complete genome. This strategy accelerates the training of the model. The findings indicate that just 70 epochs are required to obtain a 96.1 percent accuracy level, illustrating the model's speed. Bidirectional LSTM-RNN and GRU delivers the most accurate results. This model can predict more same exons for various eukaryotic genomes, which is an incredible accomplishment and will benefit comparative genomics research.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Genome Annotation**} \\ \hline  & Exons & Introns & Exons & Introns \\ Number & 4618 & 681 & 4231 & 681 \\ Average Length & 1370 & 89 & 1716 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Exons and introns are predicted by the model using genomic annotation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Our Proposed** & **LSTM-RNN** & **Bidirectional** & **Deep Belief** \\
**Comparison** & **Method** & **and GRU**[30] & **LSTM**[31] & **Networks**[32] \\ \hline Acc. & 0.961 & 0.953 & 0.843 & 0.892 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The suggested model’s performance was evaluated and compared to the state-of-the-art.  
Some of the shortcomings in our research should be rectified. First, the LSTM-RNN and GRU is not as successful as LSTM-RNN GRU Model, and additional research is needed for non-site region, genes without the intron. Future research will explore these limitations, such as How the model deals with the longer or shorter introns and the overlapping between predicted and genome annotation results.  
Conceptualization, P.J.C. and O.N.U.; methodology, software, P.J.C. and O.N.U.; validation, O.N.U. and P.J.C.; formal analysis, P.J.C.; investigation, O.N.U.; resources, P.J.C.; data curation, P.J.C.; writing--original draft preparation, P.J.C.; writing--review and editing, P.J.C. and O.N.U.; visualization, P.J.C.; supervision, P.J.C.; project administration, P.J.C.; funding acquisition, P.J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData available at [[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)) (accessed on 21 April 2022).  
We thank all my supervisors for their expertise and assistance throughout all aspects of our study and for their help in writing the manuscript.  
The authors declare no conflict of interest.
## 3 Network Background
### Ultra-Dense 5G/6G Cellular Networks
#### 3.3.2 Fundamental Feature of the UDN  
To understand the distinct characteristics of UDNs compared to traditional networks, several key aspects must be highlighted.  
* High Density of Small Cells: UDNs consist of many small cells with low power and small coverage areas, often located within meters or tens of meters from users [38]. This creates a unique wireless coverage environment where users are in close proximity to multiple small cells [40].
* Idle-Mode Capability: Due to the high concentration of small cells, many remain inactive at a given time. To mitigate interference and improve energy efficiency, idle-mode mechanisms are implemented, allowing inactive small cells to be turned off [41; 42].
* Severe Interference Challenges: The close proximity of small cells in the UDN environment results in significant interference between neighboring cells. Thus, strict interference management techniques are required to minimize disruptions and maintain network performance [43; 44].
* Advanced Frequency Reuse Techniques: Unlike traditional networks where frequency reuse occurs at the cell cluster level, UDNs require a paradigm shift in spectrum reuse strategies. In CDMA and OFDMA systems, the frequency reuse factor is one, but UDNs demand more innovative frequency reuse approaches [40].
* Complex Backhauling Requirements: Providing low-latency, high-speed backhaul for each small cell in a UDN is challenging. The backhaul capacity may become a bottleneck, limiting the air interface capacity of small cells [26; 30; 44].
* High Probability of Line-of-Sight (LOS) Transmission: Due to the short distances between base stations (BSs) and users in UDNs, LOS transmission is more common. Consequently, new propagation models are required, incorporating Rican fading models to account for the dominant LOS component in the received signals [11; 28].  
These characteristics highlight the unique design and operational challenges of UDNs, necessitating advanced interference management, frequency reuse, and backhauling solutions to ensure efficient and scalable network performance.
## Instrumentation
### JSATS Signal Conditioner  
The four hydrophones in a JSATS cabled system are connected by cables to a four-channel JSATS signal conditioner (Precision Acoustic Systems, Seattle, WA, USA). The JSATS signal conditioner amplifies and conditions the analog signals prior to signal detection analysis. Detailed specifications of the JSATS signal conditioner are as follows:  
1. A power supply with +6 and -6 V is required.
2. Four independent receiver channels (with common power supply).
3. Input self-noise must be less than 3 nV/SqrtHz (for 90-ohm source).
4. MS3102A14S-6S hydrophone signal-input/pre-amp power connector on the rear panel (signal-/\(\sim\)6 V \(>\) B&C, signal+/\(\sim\)6 V \(>\) E&F, signal common \(>\) A&D).
5. 3-wire (2008 model) or 6-wire (2010 model) common-mode choke with a common-mode impedance of more than 35 K-ohm from 342 to 492 kHz for all inputs.
6. Differential input signal impedance at 416.667 kHz of 85 to 95 ohm.
7. Individual gain control inputs for each channel (female BNC on front panel) with a scale factor of 20 dB/V, an input resistance \(>\) 100 K-ohm, and a common mode range of -2.0 to +0.5 V. The control voltage is typically 0 to 4 V for an 80-dB gain control range.
8. Maximum receiver gain at 0 V is less than 16 dB when the gain-control voltage is at 0 V.
9. Three-pole Bessel filter or better with a 150-kHz bandwidth centered at 416.667 kHz and matched to within 3% in both amplitude and phase.
10. Each input channel has a BNC connector for signal outputs on the front panel.
11. Each receiver channel must be equalized within 0.5 dB over a 60-dB gain-control range.
12. A power entry module on the front panel with fuse holder and on/off switch (115 V AC) is required.
13. The instrument case must be a standard 17-in. rack mount case with front rack-mount handles.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 3 Proposed Method
### Thresholding  
In data compression, thresholding is a process where samples of a digital discrete time signal with an absolute value lower than a specified threshold are set to zero. This threshold might align with the lowest absolute quantization level, particularly when quantization is also applied. In the compression method introduced in this work, the threshold is determined as a percentage of the maximum absolute value found among the signal samples. This threshold is then applied to both the loadings and transform coefficients obtained from the PCA procedure. Different thresholds are applied to each due to the anticipated substantial differences in the numerical value ranges of the elements within each matrix.
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
## 3 Discussion
### Summary  
The PKD protocols have higher rates than the QKD protocols because eavesdroppers have no access to the channel and only 'collective attacks' are allowed. That means if there are no multi-photon emissions (i.e., not using the WCP source) no privacy amplification is required. PPM PKD suffers from multi-photon emissions so requires privacy amplification. In general the relaxed security assumptions increase key rates and allow for key generation at larger distances. Comparing between different hardware methods of PKD (or between different hardware methods of QKD) is less conclusive, since the 'pulse' rates of different implementations of the hardware will vary.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Registration and Verification Phase  
To ensure secure data communication and prevent unauthorized access to the system, all users \(U_{i}\) who need to join the system must register. Prior to registration, a key pair is generated, where \(pk\) represents the public key, and \(sk\) represents the private key. Additionally, users prepare their real identity \(ID_{R}\) for registration. The HMC generates a \(uid\) and proof.
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 4 The Three-Stage Stackelberg Game
### Analysis of Stackelberg Equilibrium
In addition, PER leads to a loss of sample diversity, which can be corrected by introducing the importance sampling weight (ISW) [42], i.e.,  
\[w_{i}=(B\cdot P(i))^{-\beta}, \tag{19}\]  
where \(B\) is the size of the replay memory buffer and \(\beta\) controls the level of compensation. When performing the gradient descent step, we use the weighted TD-error \(w_{i}\delta_{i}\) instead of \(\delta_{i}\). Algorithm 1 shows how D3 QN is applied to solve our IPL problem. In each training round, the InP first selects an action based on the greedy strategy (lines 3-4), then executes the pricing scheme and computes rewards based on feedback from the NPL and the RAL (line 5). After that, transition \((s_{t}^{InP},\hat{a}_{t}^{InP},R_{t}^{InP},s_{t+1}^{InP})\) is stored in the replay memory buffer (line 7). Lastly, a mini-batch is sequentially selected from the buffer using PER (lines 8-14). Specifically, we first select a transition based on the replay probability (line 9). Then, we compute the ISW and TD-error and then update its transition priority \(p_{i}\) (line 10-12). Finally, the gradient descent method is employed to update the network parameters (line 13). Moreover, the target network should be updated every fixed number of rounds (line 15). ```
1:Initialize the evaluation network with random weights \(\mathbf{\theta}\) and target network with \(\hat{\mathbf{\theta}}=\mathbf{\theta}\); replay memory buffer \(\mathcal{D}\). 2:for\(t=1\) to \(T\)do
3: Select a random action \(\hat{a}_{t}^{InP}\) with probability \(e\). 4: Otherwise \(\hat{a}_{t}^{InP}=\arg\max_{\hat{a}_{t}^{InP}}\hat{Q}(\hat{s}_{t}^{InP},\hat{ \mathbf{\theta}})\). 5: Send \(\hat{a}_{t}^{InP}\) to the NPL and RAL and calculate the reward \(R_{t}^{InP}\) according to the feedback. 6: Obtain the state \(s_{i+1}^{InP}\) of the next round based on the number of users the tenants expect to serve. 7: Store the transition \((s_{t}^{InP},\hat{a}_{t}^{InP},R_{t}^{InP},s_{t+1}^{InP})\) in \(\mathcal{D}\). 8:for\(i=1\) to \(I\)do
9: Sample a transition \((s_{i}^{InP},\hat{a}_{i}^{InP},R_{t}^{InP},s_{i+1}^{InP})\) with probability \(P(i)\). 10: Compute the ISW according to (19). 11: Compute the TD-error according to (18). 12: Update transition priority \(p_{i}\). 13: Perform the gradient descent algorithm on the loss function with respect to \(\mathbf{\theta}\). 14:endfor
15: Set \(\hat{\mathbf{\theta}}=\mathbf{\theta}\) every \(\Omega\) rounds. 16:endfor
```  
**Algorithm 1** D3 QN Algorithm for the IPL
# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 6 Visual Simulation of the Virtual Reality System  
The MATLAB engine adopts a client/server based computing model. The client and the server can exist on the same computer, or can be distributed over two computers, sharing information over a network. The application uses a hybrid of Visual C++ programming and MATLAB programming, as the front-end client. The Visual C++ platform receives the data from the MATLAB engine and transmits the command and the data to the MATLAB engine. As a server, the MATLAB engine processes data at the background. In MATLAB, the following functions are used to call the MATLAB engine from the Visual C++ language: The engOpen and enclose functions are used to turn the engine on and turn off; the engGetVariable and the engPutVariable functions are used to get and send a MATLAB array from the engine; and the engEvalString function makes the engine execute the commands in MATLAB grammar.  
For AUV motion control, the virtual reality system can be used to verify the effects of the motion control algorithm. The motion control simulation uses MATLAB simulations as the main operational tool. It is not intuitive to estimate the effectiveness of the algorithms just from the calculation results or the simulation curve. Therefore, in this paper, we develop a virtual simulation system with synchronous operations and perform analog and digital simulations of the AUV motion. We use intuitive visual simulation to perform tests using the neuro-fuzzy control algorithm. The AUV's horizontal motion control is shown in Figure 11. We also design a three-dimensional motion controller in MATLAB. Then, as shown in Figure 12, the AUV virtual reality system vividly shows the motion state of the autonomous AUV. This achieves the goal of using virtual reality technology to verify the AUV motion control algorithm, not only saving the costs of voyage trails, but also providing a more intuitive and vivid interface for the design.  
Figure 11: Data communication between the VR system and MATLAB.
## 3 Results
### Post-Classification Change Detection  
Figure 7 shows sample results of post-classification change detection for the Guatem and Mato Grosso tiles. The change maps were derived by identifying all transitions from any of the forest classes 1 (primary forest), 11 (sparse woodland), and 12 (dense woodland) to any of the non-forest classes 3 (bare soil), 4 (crops), or 5 (grassland) using a purpose-built function from the PyEO library. Class 2 (plantation forest) was not included in the forest classes because rotation forestry was not a change of interest in the change detection step.  
Figure 6: Random forest classifications of the baseline Sentinel-2 composites and sample change detection image. (**a**) Tile 15 PXT Guatem, Sentinel-2 baseline composite from 2019; (**b**) Guatem, Sentinel-2, acquisition date 20220714; (**c**) Tile 21 LTD, Mato Grosso State, Brazil, Sentinel-2 baseline composite from 2019; (**d**) Mato Grosso, Sentinel-2 acquisition date 20220419. Black areas are masked-out clouds or cloud shadows based on the Sentinel-2 Scene Classification Layer (SCL file) and a dilation algorithm.  
Figure 7: Post-classification change detection between the random forest classifications of the baselineSentinel-2 composite 2019, and the sample change detection images from Figure 5. **(a)** Tile ID 15 PXT, Guatema; **(b)** Tile 21 LTD, Mato Grosso State, Brazil. White pixels show a change from one of the forest classes (1, 11, 12) to a non-forest class (3, 4, 5).
###### Abstract  
Research has shown that Raman spectroscopy could be applied to monitor various components in mammalian cell culture in real time. In the process of application, it is necessary to ensure the performance of the Raman-based model. The variable selection strategy is an effective method that significantly influences the model performance and simplification. In this study, different variable selection strategies were evaluated, and the optimal variable selection strategy was determined for monitoring the CHO cell culture process. Firstly, a wide variety of spectral regions involving the Raman fingerprinting region and the C-H stretching region were investigated. Secondly, six different variable selection algorithms were meticulously assessed. Thirdly, the combination of different variable selection algorithms was used to improve model performance and simplify the model. Finally, the monitoring of cell culture processes was implemented. The findings underscored that commonly used spectral regions could improve the model performance but could not simplify the model well. Moving-window partial least square (MWPLS), genetic algorithm (GA), and random frog (RF) are more suitable for Raman modeling of the cell culture process, but they must be used after the spectral region selection. The combination of three variable selection algorithms (MWPLS-GA-RF) improved the model's performance by 16-70% by selecting 30-60 variables, effectively simplifying the model. For glucose, lactate, viable cell density, and ammonium ion, real-time monitoring was performed well. This study will be helpful for researchers to select suitable variable selection strategies for building models for the real-time monitoring of cell culture.  
Raman spectroscopy; mammalian cell culture; variable selection; model performance Article
## 4 A Survey on FPGA-Based Sensor Systems
### Safety and Security Applications for Sensor Nodes
Finally, [PERSON] _et al_. [88] proposed the implementation of the Skipjack encryption algorithm for WSNs on a Spartan-3E (XC3S500E) FPGA clocked at 20 MHz. The power consumption of the implementation in the FPGA required 98.9 mW and 0.32 \(\mu\)J per block. The authors compared their implementation with another implementation on a Microchip 16F84A microcontroller which consumes 82 mW and required 44.45 \(\mu\)J per block.
## 3 Experimental Verification  
To validate the effectiveness of the proposed method, the experiments were performed on a MFK-PK5M SpectraQuest Machinery Fault Simulator (SpectraQuest, VA, USA), as shown in Figure 6. ER16K bearings (RBC bearings, California, USA) were used and parameters of the bearing are given in Table 1. Experimental data of the bearing outer race fault and inner race fault were collected.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 3 Model and Method
### Assimilation Algorithm
We conduct experiments to determine the parameter and threshold values of the PF algorithm following [PERSON] et al. (2018): (1) The optimal number of SMAP observations, \(\mathrm{N_{SMAP,opt}}\), decides each assimilated window. Reasonable results are obtained for values ranging between three and seven SMAP observations. Less than three observations can result in degradation, since more SMAP samples are necessary to compensate the possible uncertainties of individual SMAP data. More than seven observations can also lead to degradation because of a long-time assimilated window. The optimal SMAP observations \(\mathrm{N_{SMAP,opt}}\) for each grid cell is obtained through a trial process in which the RMSE of simulated soil moisture (relative to the SMAP observations) is calculated and the \(\mathrm{N_{SMAP}}\) with the smallest RMSE is chosen as the optimal. (2) The number of the ensemble simulations. Since we get very similar results with the number of 100, 200, and 300, we finally select 100 to limit computation cost; (3) The value of \(\mathrm{P_{threshold}}\), which determine the rainfall category. Considering huge regional difference in precipitation magnitudes over China, we determine the best \(\mathrm{P_{threshold}}\) for each grid cell separately as we do for \(\mathrm{N_{SMAP}}\). Figure 2: Graphical representation of assimilation algorithm using SM data. The figures show an example for a grid point at 109.3\({}^{\circ}\)E, 22.1\({}^{\circ}\)N from June 12 to 24, 2017. (**a**) Original rainfall (blue bars) from GPM to be corrected and its associated SM data (blue lines) simulated by API model. (**b**) Including 100 simulations of artificially perturbed rainfall data (grey bars) and their associated SM data (grey lines). (**c**) As in (**b**) but including the best 10 simulations in yellow. (**d**) As in (**c**) but with the final output rainfall and SM data in red. The reference data of rainfall (from CMPA) and SM (from SMAP) are marked with green pentacles.