esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
Experiments have been performed to demonstrate the validity of the proposed OSNR monitoring technique for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16 QAM systems. The experimental configuration is shown in Figure 7. At the transmitter, 28 Gbaud RZ-QPSK and 14 Gbaud NRZ-16 QAM optical carrier signals are generated using an external cavity laser (ECL) and an I/Q modulator driven by multilevel electrical signals. ECL has a central wavelength of 1550 nm and its line width is 150 kHz. A polarization beam splitter (PBS) divides continuous laser generated by continuous wave laser into two groups of vertically polarized optical carriers. The data of pseudo-random binary sequence (PRBS) generator are transformed into corresponding modulator driving signals by level generator and modulation driver, and then modulated to two vertically polarized optical signals by IQ modulator. Finally, these two groups of orthogonal polarized optical signals are synthesized by polarization beam combiner (PBC) to obtain polarization multiplexing optical signals. Then, the two signals are amplified by erbium-doped fiber amplifier (EDFA) and transmitted through the optical fiber recirculation loop. The loop consists of a span of 100 km standard single mode fiber (SSMF), EDFA, a variable optical attenuator (VOA) placed before the EDFA and a 5 nm bandwidth optical bandpass filter (OBPF) for channel power equalization. The gain of EDFA is 20 dB and the noise figure (NF) is 4 dB. Variable amounts of CD and differential group delay (DGD) are induced by the fiber and polarization mode dispersion (PMD) emulator, respectively. The OSNR is adjusted in the range of 10 and 30 dB in steps of 2 dB, the CD is introduced in the range between 0 and 3400 ps/nm in steps of 200 ps/nm, and the DGD is introduced in the range of 0 and 20 ps in steps of 2 ps. At the output end of the loop, the real value of OSNR is measured by optical spectrum analyzer (OSA). The output optical signal is filtered by 0.4 nm bandwidth OBPF, and then detected by a coherent receiver. The line width of the local oscillator (LO) is 100 kHz, and the frequency offset is 1 GHz. The coherent detected signal is sampled by a real-time oscilloscope with a 50G sampling rate, and then input to the DSP module for offline processing.  
A total of 200,000 amplitude samples for the whole experiment are obtained through offline processing. These samples are treated with 100 bin numbers to obtain 200 sets of AHs data, and thus the complete dataset of AHs is obtained. The equivalent sparse dataset of AHs is screened from the original dataset by the IBPSO algorithm. The OSNR monitoring system based on DNN and PSO adopts original and sparse datasets for training, respectively. The training set and test set are randomly selected as 80% and 20% of the AHs data and 10-fold cross-validation is also applied to validate the accuracy of the model and the results are not biased by the random train and test split.
## 3 Promotion of Double-Duplex Invasion by Modified PNAs
### PNA Containing Cationic Guanine (G\({}^{+}\)-PNA)  
In contrast to modified PNAs with chiral backbones or additional functional molecules, cationic-guanine (G\({}^{+}\)) PNA (G\({}^{+}\)-PNA; Figure 9a), in which a nucleobase is modified with a strategy different from pcRNAs, has been developed and reported to show improved invasion efficiency [75]. G\({}^{+}\) is positively charged by methylation of the \(N^{7}\)-nitrogen atom in a guanine base, resulting in quaternion. Consequently, G\({}^{+}\)-PNA/DNA duplexes are stabilized by electrostatic interaction, and G\({}^{+}\)-PNA/G\({}^{+}\)-PNA duplexes are destabilized by electrostatic repulsion (Figure 9b). The DNA-binding affinity of G\({}^{+}\)-PNAs was evaluated by \(T_{\text{m}}\) measurements with complementary DNAs, and an increase in \(T_{\text{m}}\) of up to 11.2 \({}^{\circ}\)C (\(\Delta T_{\text{m}}=\) 11.2 \({}^{\circ}\)C) was observed by simply replacing a single guanine nucleobase in the PNA with a G\({}^{+}\) nucleobase. On the other hand, the \(T_{\text{m}}\) values of a duplex between G\({}^{+}\)-PNAs were reduced by \(-\)4.8 to \(-\)8.3 \({}^{\circ}\)C by introducing G\({}^{+}\). These results are equivalent to or better than pcPNA (stabilization of pcPNA/DNA; +5 to +6 \({}^{\circ}\)C, destabilization of pcPNA/pcPNA; \(-\)4 to \(-\)6 \({}^{\circ}\)C). The relationship between changes in salt concentration and \(T_{\text{m}}\) value suggests that the electrostatic interaction plays an important role in these G\({}^{+}\)-PNAs.  
Invasion experiments under physiological conditions showed that the introduction of a single G\({}^{+}\) in the PNAs dramatically increased the invasion efficiency, which was about 20 times higher than that of unmodified pcRNAs. Furthermore, when the number of introduced G\({}^{+}\) was increased to three, the invasion efficiency was further improved, showing 24-fold higher invasion efficiency than that of unmodified pcRNAs. On the other hand, when invasion was performed with the target dsDNA containing the single-base mutation, the invasion complex almost disappeared, indicating the high sequence selectivity of G\({}^{+}\)-PNA. G\({}^{+}\) is expected to be one of the new pcPNA options because it can be introduced into GC base pairs, unlike pseudo-complementary nucleobases D and U\({}_{s}\), which can only apply to AT base pairs. In addition, compared to D and U\({}_{s}\), G\({}^{+}\) has the advantage that its monomer can be easily synthesized from commercially available Fmoc-G(Bhoc)-COOH PNA monomers in a single-step methylation reaction (the yield was 98%). Interestingly, although methylation to nucleobases in DNA promotes depurination and generally reduces the stability of the DNA itself [76], no such destabilization was observed with G\({}^{+}\)-PNA, indicating that this nucleobase modification was allowed by using the PNA backbone.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model
### Multiple Preprocessing
#### 3.2.3 Blood-Vessel and Noise Removal  
The flowchart of blood-vessel and noise removal is shown in Figure 12, while Figure 13 shows the result of each process. First, the Gaussian filter was used on the value channel (Figure 13a) to remove some small amount of noise. The result after Gaussian filter is Figure 13b. The size of the Gaussian kernel in this paper was \(17\times 17\), and the deviations in the horizontal and vertical directions were both 1. Then, blood vessels were removed with morphological closing and opening (Figure 13c). The used structure element in the morphological process is an ellipse with the size of \(15\times 15\), and the iteration times were 3. Here, the product of size and iteration times should be larger than the thickness of the blood vessels. As shown in Figure 14, some high-frequency noise is introduced after morphological processing. Lastly, a low-pass filter was used to remove this high-frequency noise (Figure 13d). The mask used in the low-pass filter was a \(40\times 40\) square in the center.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **Average** & **Variance** \\ \hline Red & 4.9824 & 5.4550 \\ Blue & 4.4111 & 11.6290 \\ Green & 4.6481 & 5.9764 \\ Hue & 1.8755 & 10.1563 \\ Saturation & 2.1173 & 3.0322 \\
**Value** & **5.0408** & 5.4443 \\ Lightness & 4.6617 & 5.1113 \\ A (green/magenta) & 2.0801 & 2.5126 \\ B (blue/yellow) & 2.6658 & 4.4554 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average and variance of CNR values of each channel.  
Figure 12: Flowchart of blood-vessel and noise removal ((**a–d**) correspond to Figure 13).  
Figure 13: The result of each process in blood-vessel and noise removal.  
However, as shown in Figure 15, the process of blood-vessel and noise removal generates some bright noise that connects with the OD area, resulting in a low contrast of the OD boundary and undersegmentation. This problem is solved with a more suitable initial value of the level set function. This bright noise is ignored under the constraint of shape-based term in the HLSM.
## 4 Case Study: NSS Data from a UK University
### Phase 2: Teaching Intervention and Evaluation
#### 4.2.1 Discussion on Issues Identified
While the official feedback response time cannot be shortened and the existing assessment arrangements cannot be changed, we recommend that, for major assignments, several checkpoints should be introduced to monitor students' progress and provide informal feedback and/or formative exercises as sub-assignment tasks; for minor assignments, informal/oral feedback should be provided and prior to the next assignment there should be discussion in class on common mistakes (this requires tutors to go quickly through all submissions). For all assignments, the marking criteria should be clearly explained and linked to the task, which will help the students to understand better the feedback that is provided. I9 and I10 relate to the experience of the support that students receive from tutors out of class. In this department, the official response to a student email must be within three working days, and there are designated office contact hours for students. In the follow-up investigation, these policies were clear to all staff members, and there was no evidence of staff violating any policies. While this initial finding contradicts the issues identified, further exploration suggested that the issues could be due to inconsistencies in the level of support provided by tutors. For example, some staff tended to reply to student emails within minutes, and sometimes even outside of office hours, while others tended to respond only during office hours. When a student popped in during non-contact hours, some staff tended to put aside any task that they were working on, while others followed the policy and booked a later appointment with the student. Such varied behaviors gave a false impression to students that every staff member should at all times respond or offer help (i.e., the students were unclear about the policies and took as the norm the instant response), a finding that was later confirmed by the student representatives. To address those issues, it is suggested that, at the beginning of a module, all tutors should explicitly clarify: (a) what to expect when emailing a tutor; (b) how a tutor's office hours work; and (c) the best way of contacting them.
## 2 Materials and Methods
### Implementation of SDKU-Net  
In the proposed network, the kernel size k is initialized as 2 for the convolutional operation, which is utilized both in the encoder and decoder phases. The Adam optimizer is configured with AMSGrad and a learning rate of 0.001. In contrast, the Nadam optimizer is initialized with a learning rate of 0.0001, oscillating down to 0.000085 when a decrease in IoU validation is observed, and reverting to its initial value upon metric stabilization or improvement. The training process spans 75 epochs, with a patience counter of 10 epochs. Notably, this patience mechanism is activated exclusively when Nadam is employed in the optimizer switching, enabling fine-tuning during the later training stages. All experiments were conducted on a workstation running the TensorFlow-GPU 2.10 package, equipped with an NVIDIA GeForce RTX 4060 GPU, on Windows 11. This configuration ensures efficient training, leveraging the GPU's advanced parallel processing capabilities.
###### Abstract  
Carbon dioxide (CO\({}_{2}\)) outgassing from river surface waters is an important component of the global carbon cycle currently not well constrained. To test the hypothesis that riverine partial pressure of CO\({}_{2}\) (\(p\)CO\({}_{2}\)) and CO\({}_{2}\) outgassing rates differ between daylight and darkness, we conducted in-situ \(p\)CO\({}_{2}\) and ambient water measurements over four 24-h periods in the spring and summer of 2018 in the Lower Mississippi River under varying flow regimes. We hypothesized that diel \(p\)CO\({}_{2}\) variation will correlate inversely with solar radiation due to light-induced photosynthesis. Despite differing ambient conditions between seasons, we found a consistent diel cycle of riverine \(p\)CO\({}_{2}\), with highest values before sunset and lowest values during peak daylight. Recorded \(p\)CO\({}_{2}\) measurements varied by 206-607 atm in spring and 344-377 atm in summer, with significantly lower records during daylight in summer. CO\({}_{2}\) outgassing was significantly lower during daylight in both seasons, with diel variation ranging between 1.5-4.4 mmol m\({}^{-2}\) h\({}^{-1}\) in spring and 1.9-2.1 mmol m\({}^{-2}\) h\({}^{-1}\) in summer. Daily outgassing rates calculated incorporating diel variation resulted in significantly greater rates (26.2 \(\pm\) std. 12.7 mmol m\({}^{-2}\) d\({}^{-1}\)) than calculations using a single daily \(p\)CO\({}_{2}\) value. This study suggests a likely substantial underestimation of carbon outgassed from higher order rivers that make up a majority of the global river water surface. The findings highlight the need for high temporal resolution data and further research on diel CO\({}_{2}\) outgassing in different climate regions to constrain uncertainties in riverine flux estimation.  
aquatic carbon cycle; freshwater CO\({}_{2}\); diel river metabolism; river carbon accounting; Mississippi River +
Footnote †: journal: water
## 1 Introduction
In this work, a multiresidual method has been validated for the analysis of pyrethroids (phenothrin, permethrin, cyfluthrin, cypermethrin, deltamethrin, fenvalerate), organochlorine pesticides (aldrin, dieldrin, endrin, \(\alpha\)-HCH, \(\beta\)-HCH, \(\gamma\)-HCH, \(\alpha\)-endosulfan, \(\beta\)-endosulfan, endosulfan sulphate, cis-chloradane, trans- chlordane, heptachlor, heptachlor exo-epoxide, heptachlor endo-epoxide, p,p' DDD, p,p' DDE, p,p' DDT, quinozene, tenzene) and organophosphorus compounds (azinphos-ethyl, chlorfenvinphos, chlorpyrifos, chlorpyrifos-methyl, diazinon, fenthion, malathion, parathion-ethyl, pirimiphos-methyl, profenofos, pyrazophos, triazophos) through the evaluation of linearity, selectivity, detection, and quantification limits, recovery, precision, and measurement uncertainty. Then, the evaluation of the contamination grade by OCPs, OPPs, and PYRs in more than 200 samples of cereals and legumes, coming from Italy and foreign countries and collected in the years 2018-2019 has been carried out. Considering the high number of samples and their widespread origin, the obtained data can be considered a first preliminary surveillance study that can contribute to the knowledge of food pesticide contamination.
## 4 Devised Model for Received Power
Rearranging Equation (13) to express \(I_{0}\) in terms of other quantities and substituting it into Equation (14), we obtain Equation (16) for the adapted received power at \(\alpha\). This model considers the symmetry of the intensity distribution about the LED's center axis. Thus, it simplifies a 3D distribution to just the plane-angle measure \(\alpha\), where an Rx at any cartesian coordinate within the annulus of interest and whose arctan argument to the center axis of Tx is approximately \(\alpha\) will demonstrate the same received power when revolved around the center axis. Using this model, we will study the geometrical implications for optimizing the received power for a PD array in the next section. Figure 2:1  
Figure 2:2
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 6 Challenges in FNDN Security
### Data Security  
Data packet is signed but not encrypted which means that data confidentiality is not guaranteed. For now, we suppose that the requested data is freely available; however if the data is encrypted, then key management and access to that data should be provided to the consumer based on the access rights. Another problem is the content store where the content could be cached in the encrypted form, but the meta information about the content would still be in a cleartext. Therefore, more investigation is needed to handle access to the encrypted data in NDN. This issue is more serious in FNDN due to the mobility and intermittency among UAVs.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 4 Experimental Validation and Result Analysis
### System Computational Complexity Analysis  
Using the data from the second experiment in Section 4.1, an image of the number of satellites is plotted in Figure 11.  
The average number of visible stars of the three-constellation combination system is 18, and the selection ratio is set to 70%, and a total of 13 satellites need to be selected. The statistics of the system computation of four different satellite selection algorithms are shown in Table 5.  
As can be seen from Table 5, in an epoch, compared with the traditional GDOP algorithm, the number of matrix multiplications and matrix inversions are reduced by 81.89%, and compared with the position error-aided GDOP algorithm, the number of matrix multiplications is reduced by 46.01% and the number of matrix inversions is reduced by 44.10%, and compared with fast genetic GDOP algorithm, the number of matrix multiplications is reduced by 44.34%, and the number of matrix inversions is reduced by 44.76%. The satellite selection algorithm designed in this paper significantly reduces the system operations compared to other algorithms.  
Figure 11: Number of satellites before and after the selection algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Satellite Selection Algorithm**} & \multicolumn{2}{c}{**Arithmetic Category**} \\ \cline{2-3}  & **Matrix Multiplication (Times)** & **Matrix Inversion (Times)** \\ \hline Traditional GDOP & 113,696 & 56,848 \\ \hline Algorithms in this paper & 20,592 & 10,296 \\ \hline Position error-aided GDOP & 38,145 & 18,418 \\ \hline Fast genetic GDOP & 37,002 & 18,640 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Complexity of different satellite selection algorithms.
## 1 Introduction
The first incision is made at the 4 th or 5 th phosphodiesterase bond on the 3' side of the lesion; this is followed by the second incision at the 8 th phosphodiesterase bond on the 5' side of the lesion [23]. Excision of the damage-containing ssDNA segment by the UvrD-helicase, repair synthesis by DNA polymerase I and ligation by DNA ligase [17] complete NER and lead to restoration of the original sequence. The above sequence of steps can be found in both the GGR and TCR modes of NER, however, the latter mode includes several unique steps. Transcription-coupled repair is initiated when a lesion on the DNA strand that is being transcribed causes RNA polymerase to stall. In this scenario, the stalled RNA polymerase occludes the lesion, preventing its repair. In order to remove the DNA lesion and resume transcription, the stalled RNA polymerase needs to be displaced. To accomplish this, the Mfd (mutation frequency decline) transcription-repair coupling factor is recruited to the site [24]. Mfd removes the stalled RNA polymerase from DNA using ATP-dependent forward translocation [25] and recruits UvrA to the damaged DNA [26; 27]. Recently, an alternative pathway for TCR has been proposed that leverages UvrD, NusA, together with guanosine 3', 5'--bis(diphosphate) (ppGpp), to stimulate back-tracking of RNA polymerase [28; 29]. Both branches of TCR culminate in the binding of UvrA and UvrB to damaged DNA, after which the sequence of steps as outlined above for GGR commences. Notably, although molecular mechanisms of NER are conserved from bacteria to humans, they are implemented by unrelated proteins [30].
## 3 The PMT Readout and Trigger System  
The signals produced by the calorimeter's PMTs are digitized by a dedicated electronic board, called PMT&T, that mounts two CITIROC readout chips made by Weeroc that are managed by an A3 PE3000L FPGA by Microsemi (Chandler, AZ, USA) [16; 17].  
The PMT&T board configures CITIROCs parameters, starts and stops the acquisition, produces the trigger for the detector if certain logic conditions on PMT signals, called \"trigger masks\", are met, and sends the data to the DPCU.  
A schematic description of the board is shown in Figure 2b.  
Figure 2: (**a**) The electronic board used for PMT readout and trigger system, (**b**) schematic description of the trigger board.  
Since CITIROCs are designed for SiPMs, which have positive polarity, the PMT signals are collected from the last dynode and not from the anode, making it possible to avoid the use of inverters and lowering the board consumption. To match the input range of the CITIROCs, the dynode signals are also attenuated with \"pi pad\" attenuators.
## 2 Principle of Multiple Antenna Arrays based DM Transmission  
### System Model  
In this paper, we take a multiple-input multiple-output (MIMO) downlink system into account, as depicted in Figure 1. The system mainly comprises a base station (BS) with \(K\) (\(K\geq 2\)) non-collocated transmit antenna arrays, each of which is an \(N\)-element isotropic uniform linear array (ULA) with an interelement spacing of \(d\), one LU with multiple receive antennas, and one passive Eve wishing to intercept the confidential information sent from the BS to the LU.  
For the sake of analysis, path loss is normalized and neglected. The direction angle between the \(k\)th (\(k=1,2,\cdots,K\)) transmit antenna array and the LU is denoted by \(\theta_{dk}\). Following [PERSON] et al. [23] and [PERSON] et al. [24], the normalized steering vector for the \(k\)th transmit antenna array can be calculated by  
\[\mathbf{h}(\theta_{dk})=\frac{1}{\sqrt{N}}\left[\underbrace{\xi^{ \mathrm{j}2\pi\varphi_{dk}(1)}}_{h_{1}(\theta_{dk})},\cdots,\underbrace{\xi^{ \mathrm{j}2\pi\varphi_{dk}(n)}}_{h_{n}(\theta_{dk})},\cdots,\underbrace{\xi^ {\mathrm{j}2\pi\varphi_{dk}(N)}}_{h_{N}(\theta_{dk})}\right]^{H}, \tag{1}\]  
Figure 1: System model.  
where \(\varphi_{\theta_{\theta_{\theta_{\theta}}}}(n)\) is given by  
\[\varphi_{\theta_{\theta_{\theta}}}(n)=\frac{(n-1)d\cos\theta_{dk}}{\lambda_{k}}, \tag{2}\]  
where \(\lambda_{k}\) is the carrier wavelength at the frequency \(f_{ck}\) of interest for the \(k\)th transmit antenna array.  
Likewise, the direction angle between the \(k\)th (\(k=1,2,\cdots,K\)) transmit antenna array and the Eve is denoted by \(\theta_{ek}\).
## 3 Proposed Approach  
The proposed MSRL-Net architecture is illustrated in Figure 2, employing the U-Net structure as its foundation. The U-Net consists of an encoder, decoder, and skip connection components. Several modifications have been made to the original U-Net to create the MSRL-Net, including the implementation of the proposed MSpooling instead of max-pooling for multi-scale feature extraction. Additionally, the MRHDC structure has been integrated into the U-Net network to mitigate the influence of image resolution reduction on segmentation tasks. The coordinate attention (CA) [33] mechanism is introduced to capture cross-channel information across CT image dimensions, thereby enhancing lesion feature learning and yielding more accurate segmentation outcomes for hemorrhagic lesion boundaries.