esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials
### Experiment Introduction  
In order to validate the effectiveness of the proposed method, several of the typically traditional methods on SNR estimation and a denoising method based on a convolutional neural network were selected to compare and analyze, including LSD [7], FFT-DC [12], ReSNR [13], SDC [11], Yu [20], and DnCNN [14]. A total of 6 unused images were selected as the experimental basic data (shown in Figure 1). Additionally, 6 different noise levels were added into each image. The noise variances were 0.0001, 0.005, 0.04, 0.1, 2 and 15, respectively. Additionally, the mean values of the noise were all 0. These methods were evaluated and compared on the accuracy of noise estimation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Satellite** & **Sensor** & **Size (Pixel)** & **Frame** & **GSD (m)** \\ \hline Tianzhi-1 & pan & \(2048\times 2560\) & 10 & 6 \\ SPOT4 & pan/multi & \(2048\times 2000\) & 10 & 10/20 \\ SPOT5 & pan/multi & \(2000\times 2000\) & 6 & 2.5/10 \\ SPOT6 & pan/multi & \(2500\times 2500\) & 6 & 1.5/6 \\ Pleiades & multi & \(3200\times 2500\) & 8 & 2 \\ UAV & hyper & \(2750\times 1030\) & 1 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: The basic information of plain images.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Original Data** & **Training Samples** & **Validating Samples** & **Testing Samples** \\ \hline Tianzhi-1 & 3840 & 2560 & 1280 \\ SPOT4 & 2976 & 1984 & 992 \\ SPOT5 & 2883 & 1922 & 961 \\ SPOT6 & 4563 & 3042 & 1521 \\ Pleiades & 5850 & 3180 & 1950 \\ UAV & 20,112 & 12,688 & 6704 \\ \hline \end{tabular}
\end{table}
Table 2: The information of the dataset, generated from the plain images.
## 4 Test Record and Replay Approach
### The Use of Record and Replay for GBPT Performance Assessment  
The record and replay approach for the assessment of the GBPT's performance can be considered as the combination of laboratory and field tests. It relies on the use of a SDR front-end such as the one previously described that will act as a data grabber. The data are stored on a memory mass, and it is quite clear that the amount of data to be recorded is much larger than the output rates of the observables usually provided by GNSS receivers.  
As previously remarked, testing the GNSS functionalities within a GBPT is not always straightforward, and in this section, we discuss how the record and replay approach can be helpful.  
Figure 6 shows the system architecture for GBPT performance testing by using the record and replay approach. The recording and replay systems are respectively depicted as green and blue boxes. The picture also highlights which operations are expected to be live-performed on-board the test vehicle (upper part of the picture) and the ones to be carried out in the lab at a later stage (lower part of the picture). In case the output of other sensors is of interest, they need to be collected as well, and it is important to grant the time tagging of the samples by a common stable clock.  
Figure 6: Record and replay system architecture for GBPT performance testing.  
During the data collection process, the GNSS signal is split between the front-end and the RTMeS. The former records the raw samples of the GNSS signal (green box in Figure 6), whereas the latter estimates the ground truth. Starting from the samples, the re-played GNSS signal is fed into the GBPT under test (blue box in Figure 6) and the outputs of interest are saved. Output positioning data are compared to the reference trajectory, and eventually used to compute the errors for the GBPT performance assessment.
## 3 Results
### Extreme Values of the Dependent Variables  
Table 19 succinctly delineates the primary descriptive statistics for each variable and dimension. The observed maximum and minimum values suggest that the scenarios featuring lighter hues, specifically orange, and intermittently green or violet, consistently produce the most elevated values. Conversely, the lowest values are invariably associated with one of the daylight scenarios within the control group.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Independent** & & & & & & & & & & & & & \\
**Samples** & & & & & & & & & & & & & \\
**\(t\)-Test and** & & & & & & & & & & & & & \\
**Mann–Whitney** & & & & & & & & & & & & & \\ \hline  & **Test** & **Statistic** & **df** & \(p\) & **Effect** & **Statistic** & **df** & \(p\) & **Effect** & **Statistic** & **df** & \(p\) & **Effect** \\ \hline Day 1A & Student & \(-0.582\) & 62 & \(0.563\) & \(-0.155\) & \(-1.101\) & 62 & \(0.563\) & \(-0.293\) & \(0.598\) & 62 & \(0.552\) & \(0.159\) & 62 & \(0.543\) & \(0.000\) \\ Natural light & M-W & 404.000 & 62 & \(0.371\) & \(-0.255\) & \(-0.688\) & 62 & \(0.494\) & \(-0.111\) & \(0.509\) & 62 & \(0.542\) & \(0.543\) & \(0.000\) \\ \hline Day 2A & Student & \(-0.901\) & 62 & \(0.371\) & \(-0.255\) & \(-0.688\) & 62 & \(0.494\) & \(-0.195\) & \(-1.502\) & \(-1.502\) & 60 & \(0.138\) & \(-0.427\) \\ Orange light & M-W & 360.000 & 63 & \(0.548\) & \(-0.099\) & 359.000 & 62 & \(0.538\) & \(-0.101\) & 284.000 & 60 & \(0.103\) & \(-0.258\) \\ \hline Day 3A & Student & \(-0.604\) & 63 & \(0.548\) & \(-0.162\) & 1.294 & 63 & \(0.200\) & \(0.348\) & \(-0.118\) & 61 & \(0.906\) & \(-0.032\) \\ Green light & M-W & 419.500 & 63 & \(0.666\) & \(-0.068\) & 538.000 & 63 & \(0.207\) & \(0.196\) & \(403.000\) & 61 & \(0.906\) & \(-0.068\) & \(-0.063\) \\ \hline Day 4A & Student & \(-0.165\) & 60 & \(0.870\) & \(-0.046\) & \(-0.302\) & 60 & \(0.764\) & \(-0.084\) & \(-0.215\) & 60 & \(0.831\) & \(-0.061\) \\ Purple light & M-W & 389.000 & 60 & \(0.919\) & \(-0.018\) & 367.000 & 60 & \(0.653\) & \(-0.073\) & 360.000 & 58 & \(0.760\) & \(-0.048\) \\ \hline Day 1B & Student & \(-2.397\) & 37 & \(0.022\) & \(-0.768\) & \(-1.662\) & 128.000 & 37 & \(0.105\) & \(-0.532\) & \(-1.251\) & 60 & \(0.192\) & \(-0.401\) \\ Natural light & M-W & 127.000 & 37 & \(0.076\) & \(-0.332\) & 128.000 & 37 & \(0.083\) & \(-0.326\) & 144.000 & 37 & \(0.192\) & \(-0.242\) \\ \hline Day 2B & Student & \(0.584\) & 37 & \(0.563\) & \(0.187\) & 0.944 & 37 & \(0.351\) & \(0.302\) & \(-0.262\) & 60 & \(0.794\) & \(-0.084\) \\ Green light & M-W & 201.000 & 37 & \(0.765\) & 0.058 & 226.000 & 37 & \(0.315\) & \(0.189\) & \(173.000\) & 37 & \(0.640\) & \(-0.089\) \\ \hline Day 3B & Student & \(0.445\) & 37 & \(0.659\) & 0.143 & 0.536 & 37 & \(0.595\) & \(0.172\) & \(0.052\) & 60 & \(0.052\) & 37 & \(0.959\) & \(0.017\) \\ Purple light & M-W & 237.000 & 37 & \(0.168\) & 0.247 & 230.000 & 37 & \(0.245\) & \(0.211\) & 198.500 & 37 & \(0.950\) & \(0.018\) & \(0.045\) \\ \hline Day 4B & Student & \(1.292\) & 37 & \(0.204\) & 0.414 & 1.504 & 37 & \(0.141\) & \(0.482\) & 1.088 & 37 & \(0.284\)
## 3 System Model and Problem Statements
### Problem Statements  
In this paper, the main goal is to propose an approach to improve the network energy efficiency and reduce the delay in transmission while maximizing the network lifetime. This goal can be characterized by the following indicators as explained below:  
(1) The energy utilization (denoted as \(K_{UTI}\)) is defined as the ratio of the energy used by the entire network to the initial total energy. The improvement of network energy utilization refers to increasing the effective utilization of energy, that is, maximizing the ratio of energy consumption of the entire network to the initial total energy. The energy utilization can be calculated by the equation below.  
\[max(K_{UTI})=max\left(\frac{\sum_{i=1}^{n}\xi_{i}}{\sum_{i=1}^{n}E_{ini}}\right) \tag{3}\]  
(2) The total delay (denoted as \(\mathcal{Y}_{TOT}\)) in the network is related to the waiting time before the transmission of each hop and the time that the sender takes to transmit the packet to the sink. Therefore, the total delay can be divided into two parts: the delay for detection and the delay for forwarding data. The target of reducing the delay can be expressed as:  
\[min\left(\mathcal{Y}_{TOT}^{k}\right)=min\left(\sum\mathcal{Y}_{DET}+ \mathcal{Y}_{i,j}\right) \tag{4}\]  
where \(\mathcal{Y}_{DET}\) represents the delay for detection and \(\mathcal{Y}_{i,j}\) represents the delay in forwarding data from node \(i\) to node \(j\), node \(j\) is the forwarding node of node \(i\), and nodes \(i\) and \(j\) are in the transmission path from the node \(k\) to the sink.  
(3) The network lifetime (denoted as \(\mathbb{L}\)) is defined as the time when the first node in the network dies out due to energy exhaustion. It means that the maximization of network lifetime is closely related to the maximum of energy consumption. The goal of maximizing the network lifetime can be expressed as follows.  
\[max(\mathbb{L})=min(max(\mathbb{E}_{T}^{x}+\mathbb{E}_{R}^{x}+\mathbb{E}_{LPL} ^{x}+\mathbb{E}_{SEN}^{x})) \tag{5}\]  
In conclusion, the goal of the approach proposed in this paper can be expressed as:  
\[\left\{\begin{array}{c}max(K_{UTI})=max\left(\frac{\sum_{i=1}^{n}\xi_{i}}{ \sum_{i=1}^{n}E_{ini}}\right)\\ min\left(\mathcal{Y}_{TOT}^{k}\right)=min\left(\sum\mathcal{Y}_{DET}+ \mathcal{Y}_{i,j}\right)\\ max(\mathbb{L})=min\left(max\left(\mathbb{E}_{T}^{x}+\mathbb{E}_{R}^{x}+ \mathbb{E}_{LPL}^{x}+\mathbb{E}_{SEN}^{x}\right)\right)\end{array}\right. \tag{6}\]
## 3 Challenges
### Sensing Techniques
#### 3.12.1 Signal Processing Techniques  
(a) Matched filter detection: The matched filter detection technique requires a demodulation of the PU's information signal at PHY and MAC layers, such as the modulation type and order, pulse shaping, packet format, operating frequency, bandwidth, _etc._ CR wireless sensors receive that information from the PU's pilots, preambles, synchronization words or spreading codes _etc._ The advantage of the matched filter method is that it takes a short time and requires fewer samples of the received signal. This decreases the received signal SNR and number of signal samples required. However, matched filter detection techniques consume considerable power and require high computational complexity and perfect knowledge of the target users.  
(b) Energy detection: Energy detection detects the signal based on the sensed energy. This does not require prior knowledge of the PU's signals. This is a very popular technique because of its simplicity. The main disadvantage of this technique is its lower accuracy. Energy detection cannot discriminate the PUs' signal from the SUs' signal. This technique cannot be used to detect spread spectrum signals and has poor performance under a low SNR.  
(c) Cyclostationary feature detection: In cyclostationary feature-detection techniques, modulated signals are coupled with sine wave carriers, pulse trains, repeated spreading, hopping sequences, or cyclic prefixes. The cyclostationary feature detection technique provides better performance, even in low SNR regions. This has good signal classification ability. However, it is more complex than energy detection and high speed sensing cannot be achieved. This cannot work if the target signal's characteristics are unknown.  
Figure 5: Classification of spectrum-sensing techniques.
# Protein Dynamics in F-like Bacterial Conjugation
## 4 Mating Pair Stabilization Proteins and the Dynamics of their Structural Ensemble
### TraU  
TraU has been generally categorized as having a role in DNA transfer as pilus synthesis is affected but not abolished by its absence, however conjugation does not proceed past the pilus retraction phase [14; 59; 81; 121]. Phage infection is inhibited in TraU mutants indicating that its function lies in the post-adsorption phase and therefore may play a role in Mpf or Mps [79; 121]. TraU is a 34 kDa protein with an N-terminal signal sequence that is processed to 33 kDa [121]. The position of the cleavage is disputed, as there exists a potential signal peptidase I cut site between Ala22-Asp23 and a signal peptidase II sequence Ser24-Ala25-Cys26. It localizes to the periplasm based on fractionation experiments; several segments of hydrophobic amino acids signify an interaction between TraU and membrane proteins in the T4 SS [121; 122]. There have been hypothesized interactions between TraU and TraN in the OM for Mps, and TraH in the periplasm for its effect on pilus retraction, all of which are proteins found only in F-like plasmids; no homologs are found in other T4 SS [14; 102; 121; 122]. This is further supported by the many potential redox sites in these three proteins; disulfide bond formation and cleavage may be catalyzed by TrbB and/or TraF [88]. There is currently no structural information regarding TraU, and its proposed complexes must be further studied to confirm putative functions.
## 7 Conclusions and Future Work  
This paper proposes a unified modeling and measurement method for the dynamic and static performance indicators of computing nodes, named DCRMM. The paper provides a detailed explanation of how to select the optimal computing nodes based on measurement results. Experimental results indicate that compared to computing node measurement methods that only consider dynamic or static indicators, DCRMM demonstrates superior node stability, node utilization, and node matching accuracy, enabling more effective utilization of node resources. Additionally, the paper discusses a computing power routing algorithm, RSCR, based on the SD-CFN architecture. This algorithm employs a Q-Learning mechanism to effectively compute paths between the computing power requester and the computing power provider. We validate the algorithm on the GEANT topology, and the experiments show that RSCR can ensure a significant reduction in packet loss while maintaining a delay close to that of the OSPF algorithm. Furthermore, it increases link utilization, allowing traffic to be distributed more widely across the entire network topology.  
Based on this, we hope that future work can explore the use of transfer learning algorithms for more experimentation on dynamic topologies. This would enable the algorithm to make new decisions quickly in situations where the topology is constantly changing.  
Conceptualization, X.G. and S.R.; formal analysis, software, writing, X.G. and S.R.; supervision, C.W.; investigation and methodology, J.W.; validation and visualization, X.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The code of this article is open source at [[https://github.com/ryan101/6/RSCR/](https://github.com/ryan101/6/RSCR/)]([https://github.com/ryan101/6/RSCR/](https://github.com/ryan101/6/RSCR/)) (will be available online in March 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 10: Average Throughput Comparison Chart.
# Polycrystalline Formamidinium Lead Bromide X-ray Detectors
## 3 Results and Discussion
### Electrical Resistivity  
FAPbBr\({}_{3}\) X-ray detectors were fabricated by thermally-evaporating 90 nm thick gold contacts onto both sides of each pellet, and their charge carrier transport properties and X-ray sensitivity were investigated. The conductivity of the resulting'sandwich' device structure of [Au/FAPbBr\({}_{3}\)/Au] was measured in terms of dark current density as a function of applied bias voltage, and showed an ohmic response which was symmetric for either voltage polarity. Figure 5a shows the dark current density for pellets which were formed at 24 and 74 MPa, both with and without the annealing process. In general the current density for the 74 MPa pellet was higher than that of the 24 MPa pellet, indicating improved conductivity when the material is pressed at the higher pressure. Figure 5b shows the overall trend of dark current, measured at 5 V, across the full pressure range. Both figures also indicate the effect of pellet annealing, which generally shows a reduction of 2-3 times in dark current compared to the equivalent non-annealed pellet. This may indicate a reduction in active defects in the material after the annealing process.  
The resistivity of the pellets was calculated by applying a linear fit to the dark current at low bias voltage. As shown in Figure 5c the resistivity of the non-annealed devices was higher for the 12 MPa device, but dropped significantly for higher pressures, which is consistent with an increase in material density and improved conductivity. A similar trend was also observed for the annealed devices as the pressure was increased.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 2 High-Level Design of the PWPN
### LTE 230  
The LTE 230 system works in the 223-235 MHz frequency band, which can be used in telemetry, remote control, data transmission, and so on [16]. This section of the spectrum is allocated with 25 KHz bandwidth as a single frequency point, and the power grid system obtains a total of 40 private authorized frequency points in a discrete distribution, for a total of 1 MHz bandwidth. The distribution of the specific frequency points is shown in Figure 2. The structure is irregular and comb-shaped, and the distribution of frequency points is discrete. Among the 40 frequency points, there are 15 pairs of frequency points and 10 separate frequency points. The LTE 230 system is an exclusive LTE system launched by Potevio, Chengdu, China. The main characteristic of the discrete power 223-235 MHz frequency band is the use of carrier aggregation technology to integrate the discrete narrowband frequency resources of the 230 MHz frequency band so as to form broadband resources to meet the requirements of power applications. In addition, the system also uses some new technologies, such as OFDM, high-order modulation, adaptive coding and modulation (AMC), and so on. On the one hand, it improves the spectrum efficiency. On the other hand, it gives the system superior demodulation performance and improves the anti-interference ability of the system, so as to ensure coexistence with other wireless systems operated at 230 MHz. As described in the previous section, there are mainly two frequency bands, 230 MHz and 1800 MHz, which are suitable for the construction of power wireless networks in China. Jiangsu Electric Power Company has established a PWPN pilot construction using LTE 230 developed by Potevio and a PWPN pilot construction using LTE 1800 developed by Huawei, Shenzhen, China. The comparisons of these two systems are listed in Table 1.  
Figure 2: Illustration of the state grid licensed spectrum at 230 MHz. _Future Internet_**2019**, _11_, 221
## 2 System Model  
### mmWave Massive MIMO System Model  
Consider the Massive MIMO analog-digital hybrid precoding system model as shown in Figure 1.  
Assume there are \(N_{s}\) data streams in the system, and the base station Alice and the user [PERSON] are equipped with \(N_{t}\) and \(N_{r}\) antennas, respectively. To facilitate multi-stream communication, Alice has \(N_{RF}^{t}\) RF chains, satisfying \(N_{s}\leq N_{RF}^{t}\leq N_{t}\). Under this hardware architecture, a baseband precoder \(\mathbf{F}_{BB}\) of size \(N_{RF}^{t}\times N_{s}\) and an RF precoder \(\mathbf{F}_{RF}\) of size \(N_{t}\times N_{RF}^{t}\) are adopted. The RF precoder \(\mathbf{F}_{RF}\) is implemented using phase shifters, where the elements have equal magnitude. By adjusting \(\mathbf{F}_{BB}\), Alice normalizes the transmit power, ensuring \(\|\mathbf{F}_{RF}\mathbf{F}_{BB}\|_{F}^{2}=N_{s}\). On the receiving side, a baseband combiner \(\mathbf{W}_{BB}\) of size \(N_{RF}^{r}\times N_{s}\) and an RF combiner \(\mathbf{W}_{RF}\) of size \(N_{RF}^{r}\times N_{s}\) are used.  
In a fully digital communication system, the digital precoder is denoted as \(\mathbf{F}\). In a hybrid precoding system, where the number of RF chains is smaller than the number of transmit antennas, \(\mathbf{F}\) needs to be decomposed into an RF precoder \(\mathbf{F}_{RF}\) and a baseband precoder \(\mathbf{F}_{BB}\) using algorithms such as orthogonal matching pursuit (OMP). The relationship is expressed as  
\[\mathbf{F}\approx\mathbf{F}_{RF}\mathbf{F}_{BB}. \tag{1}\]
# Microbial Precipitation of Calcium Carbonate for Crack Healing and Stabilization of Sandy Soils
## 2 Materials and Methods
### Effect of MMT on the Stabilization of Sandy Soil Using MICP  
To investigate the effect of MMT in terms of improving the strength of sandy soils under MICP, artificial sandy soils were prepared by mixing standard Jumunjin sand (poorly graded sand, SP) and MMT (sand:MMT = 9:1, 8:2, or 7:3) (Figure 1B). According to USDA soil texture classification, the artificially prepared sandy soil samples were classified as loamy sand with 10% of MMT (i.e., sand:MMT = 9:1) and sandy loam with 20-30% of MMT (i.e., sand:MMT = 8:2 to 7:3). Changes in the soil texture can affect mechanical properties, such as soil strength and permeability, but this study focused on investigating whether MMT in sandy soils enhances the MICP effect and soil stabilization. The MMT used in the present study was bentonite from Samchun Chemicals (Seoul, Republic of Korea). X-ray diffraction analysis (XRD) revealed that the MMT had a d-spacing (001) for the main peak located at a 20 of 5.9\({}^{\circ}\) of 1.505 nm (Figure S2). The chemical properties of MMT were analyzed using X-ray fluorescence analysis (XRF), showing that the CaO content (1.5%) was higher than that of MgO (1.36%) and Na\({}_{2}\)O (0.97%) (Table S1). It was thus confirmed that the MMT used in the experiment was Ca-rich MMT (Ca-MMT). A Brunauer-Emmett-Teller (BET) analysis revealed that the surface area of MMT was 85.5152 m\({}^{2}\)/g.  
For the soil stabilization experiment, 450 g of sand and the three artificially prepared sandy soils (i.e., containing 10%, 20%, or 30% of MMT) were transferred to separate cylindrical containers (332 cm\({}^{3}\)) with a height of 6 cm. In order to induce MICP, 110 mL of the CS containing bacteria (1% \(v/v\)) was added to the soil and mixed by stirring with a stick until it was homogeneous. Previous studies have reported that this mixing method is more efficient than the pouring method when the soil particles are small [28]. Each container was placed in an incubator at 30 \({}^{\circ}\)C for one month to induce microbial growth and the precipitation of CaCO\({}_{3}\). After four weeks, soil stabilization was confirmed by the formation of a soil cake, after which the soil hardness and the amount and mineralogical properties of CaCO\({}_{3}\) were evaluated. The amount of CaCO\({}_{3}\) in the soil cake was measured using a leaching method with 0.5 M hydrochloric acid [29]. The CaCO\({}_{3}\) content (%) was determined using Equation (2):  
\[\text{\%CaCO}_{3}=\frac{m_{dry\ soil\ before}-m_{dry\ soil\ after}}{m_{ dry\ soil\ before}}\times 100\% \tag{2}\]  
where \(m_{dry\ soil\ before}\) is the initial soil weight after oven drying at 105 \({}^{\circ}\)C for 24 h and \(m_{dry\ soil\ after}\) is the soil weight after acid washing and drying until no more gas bubbles occur. To investigate the effect of the CS on the cementation of sandy soils via MICP, the same experiment was conducted without the bacterial cells and the results were compared with those from the MICP experiment.