esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Discussion
### Performance Evaluation of AISRT in the Different Mediums
In Figure 7b,c, the recognition of landmarks 1, 2 and 3 would be not difficult in the water pipe, in contrast, in Figure 7d the identification of the landmarks 1 and 2 is impossible when the PSNR reached almost 8.5381. The worst case is that we could not identify or judge any target landmark in Figure 7e because the structure of landmarks was missing in the image. In the empty pipe, the situation was better, however, the landmarks 1 and 2 could also not be found in Figure 6e. Although landmark 3 could be seen, the detail part of the image was completely missing with PSNR = 5.8742. Besides, we used another important parameter SSIM [44; 45; 46]. It could basically reveal the structure deterioration of image. Usually, the higher the SSIM value, the closer the image structure, brightness, and contrast of the received image is to the original image. Figure 7: Analog image transmission with visible light in the water pipe. It can be observed from Figures 6 and 7 that the structure and brightness of the images was apparently degraded in two transmission mediums. Especially, SSIM in the water pipe dropped obviously to 0.53095 at the second elbow in Figure 9b. In the situation of Figure 6b,c and Figure 7b,c, the image's SSIM in the two transmission mediums could remain at nearly 0.71-0.80, whereas in the situation of Figures 6e and 7e both SSIM decreased rapidly to approximately 0.50. In this case, structure deterioration occurred, and the image quality degraded significantly making it impossible to judge from the detailed structure and brightness of the image in the pipe [47]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Land Mark**} & **Pipe** & \multirow{2}{*}{**Pipe Image**} & \multirow{2}{*}{**Actual Size**} & \multirow{2}{*}{**L \(\times\) W (cm)**} & \multirow{2}{*}{**\(\theta\) (rad)**} & \multirow{2}{*}{**Color**} \\ \hline  & & & & & & \\ \cline{2-6} \cline{
## 4 Discussion  
Linkers play a critical role in determining the biological degradation efficacy of PROTACs. In this study, we employed both transfer learning and reinforcement learning approaches to design linkers. Linkers were designed by leveraging data augmentation and selecting different pre-training datasets. Reinforcement learning was used to optimize linker properties through its unique reward iteration mechanism. A transformer-based GPT model was primarily used in this study. As shown in Figure 7, compared to a range of existing generative models, GPT achieved the best evaluation results, consistently ranking at the top of all curves. This suggests that our approach outperforms mainstream generative models, particularly in terms of the effectiveness and innovation of the generated linkers. However, RNN [57,58] and AAE have certain advantages in the diversity and structural similarity of the generated linkers. This enabled us to design more effective, unique, and novel linkers, thereby enriching the chemical space of linkers and providing a more diverse candidate pool for novel linker design.  
Figure 6: Docking results of PROTAC molecules with linkers generated using ProLinker-Generator in the BTK system. The stick-like structures composed of cyan, blue and red represent the listed PROTAC molecules. The gray region represents the protein (PDB ID: 6W8I), the green region highlights residues within 5 Å of the ligand, and the stick-like structure outside the green area corresponds to the PROTAC molecule.  
As shown in Figure 8, we present several examples of linkers generated using the method developed in this study. PROTAC linkers are primarily based on PEG chains, and the linkers displayed on the left side of the figure are all PEG-based designs generated by our model. The linkers on the right side contain maleimide groups, which are suitable for stable conjugation. Our approach provides structural diversity for PROTAC linker design.  
Figure 8: Examples of generated linkers: PEG-based linkers (**left**) and maleimide-based linkers (**right**).  
Figure 7: Performance of state-of-the-art molecular generation models and our models in the generation of PROTAC linkers.
## 6 Performance Evaluation
### Comparative Analysis
#### 6.3.4 Service Reliability  
The service reliabilities of the proposed and existing methods, which include performance, accessibility, and continuity, are also compared. Efficient task scheduling and load balancing improve the service reliability of our proposed method. In our framework, resource utilization is enhanced by task clustering and ranking, whereas load balancing is improved via VM clustering and allocation, which reduce VM migration. The response time of our proposed work is accordingly reduced, and service reliability is increased. The service reliability of a task is presented as a plot between task size and trust level. Cloud computes the trust value of each user-given task in cloud environment on the basis of which service is provided to the upcoming user task.  
Figure 8 shows that our proposed work exhibits greater service reliability than TTSA.  
Figure 8: Comparison of service reliability.  
Figure 7: Comparison of resource utilization.
## 3 Results
### Methodology and Sources of Error
Pioneer land cover (KIA: 0.22, AUC: 0.31) was probably confused with several other categories due to their ephemeral nature, as described above. Of the four categories with an AUC value between 0.6 and 0.7, two correspond to land cover types that grow below the forest canopy, such as _Coffea_ (KIA: 1.927, AUC: 0.676) and _Rubus_ (KIA:0.417, AUC: 0.603), so it makes sense that their AUC values are not as high, as their spectral signatures can become confused with other vegetation types. The other two categories with an AUC between 0.6 and 0.7 are built environments and transitory crops, which, as mentioned earlier, both become confused with bare ground. Our results suggest that our classification map likely represents general patterns (large patches) relatively well, especially for categories that had a clear spectral signature and, thus, had a high correlation coefficient as well, such as _Cedrela_, _Pennisetum_, and _Syzygium_. However, the landscape's heterogeneity means that the map likely becomes more inaccurate as one approaches smaller scales. The fact that the two largest islands, Santa Cruz and San Cristobal, had the highest \(R^{2}\) values (0.83 and 0.7, respectively) might be due to these islands having larger farms with a more regular and well-delimited configuration of their crops than smaller farms from Isabela or Floreana. While human digitation using high-resolution UAV images would usually have more accurate results than using segments mapped by a computer, the fact that we are using satellite images with a 3 m spatial resolution means that we are within the margin of error for most GPS devices, including our drone. This might cause misalignment with the high-resolution images that we used to obtain the geometry for the reference polygons, thus lowering our accuracy results. Furthermore, given that we used the most homogenous land cover patches visible within our UAV images to help us pick areas to train the algorithm, remaining unused areas of our images were also the most mixed and harder to categorize, which might have affected our accuracy scores as well.
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 5 Simulation Results and Discussion
### Verification of Disturbance Rejection  
To verify the disturbance rejection performance of the proposed control method, a single-rod hydraulic cylinder model was built, and the constant external load force increased from 1000 N to 3000 N at 2 s. The response results of the DPV are shown in Figure 15 when the reference position is a step input from 7 mm to 3.5 mm. Evidently, according to Equations (1)-(4), the outlet pressure of the main valve fluctuates at 2 s and rises slightly under the action of external load force, and the greater the external load force, the larger the oscillation amplitude, and the more apparent the fluctuation, the flow force produces oscillations and decreases slightly. Thus, the displacement of the main spool oscillates and increases slightly. The p5 of the control chamber pressure decreases and p4 increases to stabilize the main spool at the desired position. Therefore, the main spool reaches a stable state after a small amplitude oscillation for 0.09 s. The main spool oscillates again at 3.107 s due to the full extension of the load hydraulic cylinder and reaches a stable state after 0.035 s. Therefore, the proposed control method could suppress the oscillation of the main spool of the DPV under different load forces. The above proves that the proposed control method has good disturbance rejection performance.  
Figure 14: Ramp response of the proportional valve: (**a**) ramp response of the proportional valve main spool; (**b**) displacement tracking error; (**c**) control signal of HSVB; (**d**) pressure of the two control chambers of the main.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Decoder
#### 2.2.3 Global-Local Information Module  
As shown in Figure 4, the GLIM consists of a global path, a local path, a batch normalization layer, a depth-wise separable convolution, and a convolution operation. In the following section, we elaborate the specific process of the GLIM.  
Figure 3: Illustration of the traditional WMSA and the situation after adding SPT: (**a**) standard WMSA and (**b**) WMSA after adding SPT.  
While global contextual information is important for segmenting complex urban scenes, local context is also crucial to retaining rich spatial details. As shown in Figure 4, for the local branch, we used two groups of parallel convolutions with the kernel sizes of 1 and 3 for local information extraction and, finally, performed an add operation.  
The global branch uses a window partition operation and the proposed WMFSA to capture the global information and combines it with the position transformation operation to obtain the final global information. The details about the window partition operation can be found in Figure 5. First, we expanded the number of input channels to three times using a 1 \(\times\) 1 convolution; then, the 1D sequence could be obtained by a window partition operation; finally, we merged all the heads to obtain the three pivotal vectors (Q, K, and V) after performing attention computation on each head. The window size and the number of heads were 8, and the channel dimension, C, was 64. For more details about the WMSA, refer to the Swin Transformer [22]. However, conducting self-attention computation in non-overlapping windows is effective, but the lack of connection between different windows destroys the spatial continuity of input features and weakens the model's ability to model global relationships. The Swin Transformer uses the shifted window operation to improve the information interaction between different windows. Although effective, it introduces a great computational cost and limits the model's real-time scene application ability. In this study, we designed the SPT module to replace the shifted window operation to achieve efficient information interaction between windows while only introducing a small computational overhead. This module can be easily integrated into the WMFSA, and more details can be found in Section 2.2.2.  
Finally, the global context information generated by the global module was further fused with the local information of the local module. Meanwhile, the final global-local information was obtained after a depth-wise separable convolution, batch normalization, and 1 \(\times\) 1 convolution.  
Figure 4: Overall structure of the proposed GLIM.
## 3 CSES Optimization Framework with Dual-Time-Scale Coordination
### Dual-Time-Scale Coordination  
The CSES framework adopts a dual-time-scale approach that combines day-ahead scheduling with intraday adjustments, as shown in Figure 2.  
The day-ahead scheduling establishes reference points to ensure that the CSES SOC constraints are met throughout the operational horizon. As actual RES and load patterns are realized, intraday adjustments are made to the pre-established reference points while ensuring that SOC constraints are met. Notably, the SOC reference points obtained from the day-ahead stage are linearly interpolated to obtain time-scale-matched, application-ready SOC trajectories in intraday. These trajectories are then distributed to individual subnetworks for distributed optimal scheduling. These adjustments enable the system to respond to real-time variations in RES while maintaining the overall operational integrity of the CSES.
# RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering
## 4 Experiments
### Baselines  
Here we present some existing state-of-the-art signed network embedding methods. The baselines are as follows:  
* A: A partitioning algorithm based on the direct relation of the symmetric adjacency matrix denoted as \(A^{*}=\frac{1}{2}\big{(}A+(A)^{T}\big{)}\).
* SNS [24] and DNS [24]: SNS and DNS are two spectral methods based on the random walk normalized Laplacian, both extended from the unsigned random walk normalized graph Laplacian [39].
* L: the Laplacian matrix L\({}^{*}\), using a signed network adjacency matrix.
* L\({}_{\text{sym}}\): This is the symmetric normalized version of L.
* BNC [23] and BRC [23]: direct extensions of the signed Laplacian to the k-way clustering problem. BNC and BRC denote two algorithms for optimizing balanced ratio cuts and balanced normalized cut objectives, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **N** & **E** & **E\({}^{+}\)** & **E\({}^{-}\)** \\ \hline Sampson & 25 & 166 & 148 & 18 \\ Rainfall & 306 & 93,636 & 64,408 & 29,228 \\ S\&P1500 & 1193 & 1,423,249 & 1,069,319 & 353,930 \\ PPI & 3058 & 11,860 & 7996 & 3864 \\ Epinions & 41,178 & 609,489 & 508,449 & 101,040 \\ Slashdot & 40,825 & 448,240 & 339,415 & 108,825 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of six real-world networks. E\({}^{+}\) and E\({}^{-}\) denote positive and negative links, respectively.  
* SPONGE [13]: introduces a principle and theoretically sound spectral method for k-way clustering in signed graphs and proposes a regularized spectral clustering algorithm based on solving the generalized feature problem.
* SPONGE\({}_{\text{sym}}\)[13]: a variant of SPONGE that uses the minimum of k generalized eigenvectors to generate embeddings, called the symmetric Laplacian operator of SPONGE.
* SSSNET [33]: redefines the balance path in balance path and asks \"is the enemy of the enemy?\" A neutral position is taken, and it then proposes an end-to-end GNN framework for semi-supervised signature network clustering.
# Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
## 5 Physical Experiment Verification
Figure 7: Multi-robot research platform. \(\tau_{2}=0.063s\), \(\tau_{3}=0.068s\), \(\tau_{4}=0.082s\). Because this experiment is being performed in a laboratory environment, it is assumed that the communication noise is white noise with a maximum amplitude of 2. According to Formula (26) and the moving speed of omnidirectional robot, set \(k_{1}=1\), \(k_{2}=1.4\). Four omnidirectional robots were placed in arbitrary positions, (0.83,2.20,0), (0.35,1.74,0), (0.67,0.88,0), (0.56,0.48,0), respectively. In the practicality experiment, the robot cannot converge to one point, so Formula (2) is changed to:  
\[\lim_{t\rightarrow+\infty}[x_{i}(t)-x_{j}(t)-F_{p}]=0 \tag{35}\]  
where \(F_{p}\) is the formation parameters and \(p=1,2,\,\ldots,\,n\). Since the system only installs a UWB ranging sensor to provide positioning for the robot, the robot will perform pose determination before the experiment starts, and the robot's body coordinate system will be consistent with the global coordinate system. The experimental results are shown in Figure 9. The experimental video address can be found in reference [19]. The experimental data collected by the UWB positioning system are shown in Figure 10.  
The experiments show that the multi-robot system can eventually achieve consensus and form a formation in a variety of time-delay and noise environments, which verifies the effectiveness of the proposed algorithm. Figure 9: Experiment process screenshots. (**a**) T = 0 s; (**b**) T = 15 s; (**c**) T = 25 s; (**d**) T = 30 s.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Technical Vulnerabilities in HE
#### 8.2.3 Missing Best Practice Technical Security Controls  
[PERSON] and [PERSON] [4] found that the Turkish universities scored high on the maturity modeling, but they also found various degrees of technical compliance within the control domains. The previously mentioned password security policies scored the worst, together with vulnerability management. Only one University had intrusion detection systems in place at the time. [PERSON] et al. [52] found that the Jordanian University was lacking in several technical controls. FireEye also comments on this issue for US universities [5], writing that they lack threat intelligence regarding events and log data. Missing network monitoring prevents efficient incident detection and response, as it will be very challenging to detect and respond without appropriate tools. Additionally, FireEye comments that, for those who have implemented network monitoring, the university network traffic is so diverse that they will be flooded with low-level alerts, preventing efficient response. The UNIT report also addressed that some institutions had implement mitigation method to limit the damage of a cyber-attack by conducting backups [6]. Considering the statistics in the threat event Section (Section 6), some findings point to missing baseline security measures: A portion of the incidents reported being caused by malware, hacking, and vulnerabilities are likely connected to missing security controls. For example, vulnerable systems should either be patched or hidden behind a firewall. Hacking attacks are also demanding on a hardened security system. More specifically, the Verizon report points to _Web application_-attacks and backdoors accounting for about 90% of all hacking attacks against HE in 2018 [63], indicating poor technical security in web applications. Furthermore, over 75% of the recorded incidents targeted servers. There are strong indications of varying technical security in HE.
## 2 Materials and Methods
### Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)  
Total RNA from cells and prostate cancer tissues was extracted using ISOGEN (Nippon Gene, Toyama, Japan). cDNA was synthesized from total RNA using a High Capacity cDNA Reverse Transcription kit (Thermo Fisher Scientific, Waltham, MA, USA) with random hexamers. RT-qPCR was carried out on a fluorescent quantitative detection system (LineGene FQD-33A; Bio Flux, Tokyo, Japan). PCR was run in microtubes at a volume of 24 \(\upmu\)L, containing 1.0 \(\upmu\)L cDNA, 12.5 \(\upmu\)L SYBR Premix EX Taq (Takara Bio, Shiga, Japan), 8.5 \(\upmu\)L PCR-grade water, and 10 pmol of each pair of primers. The primers were used for the PCR as follows: 5\({}^{\prime}\)-CATGAGAAGTATGACAACAGCCT-3\({}^{\prime}\) (forward), 5\({}^{\prime}\)-AGTCCTTCCACGATACCAAAGT-3\({}^{\prime}\) (reverse) for glyceraldehyde 3-phosphate dehydrogenase (_GAPDH_; GenBank accession No. NM002046); 5\({}^{\prime}\)-AGACATGACCAGGTATGCCTAT-3\({}^{\prime}\) (forward), 5\({}^{\prime}\)-AGCCTATCTCTGTCCGATTA-3\({}^{\prime}\) (reverse) for multiple drug resistance 1 (_MDR1_; GenBank accession No. NM000927); 5\({}^{\prime}\)-TATTAGAGGTCCGATACAGGC-3\({}^{\prime}\) (forward), 5\({}^{\prime}\)-AGAGGGGATCATGAAAGGGTA-3\({}^{\prime}\) (reverse) of multidrug resistance associated protein 1 (_MRP-1/ABCC1_; GenBank accession No. 019900); 5\({}^{\prime}\)-AACCTGGTCTCAACGCCAT-3\({}^{\prime}\) (forward), 5\({}^{\prime}\)-GTCCGGTGTCCTTATTAC-3\({}^{\prime}\) (reverse) for breast cancer resistance protein (_BCRP/ABCG2_; GenBank accession no. NM004827). The reactions were performed for 10 s at 95 \({}^{\circ}\)C for preheating, then 5 s at 95 \({}^{\circ}\)C and 26 s at 60 \({}^{\circ}\)C for 40 or 45 cycles. The PCR products were subjected to subsequent agarose gel electrophoresis. The crossing point was defined as the cycle number at which the fit line in the log-linear portion of the plot intersected the threshold level. A standard curve for each gene and _GAPDH_ was generated from serial dilution of the mRNA of each gene. Finally, the relative copy number was calculated as the ratio of each gene to the _GAPDH_ copy number in each sample.
## 2 Basic Models and Theory
### Baseline Control System Model
#### 2.3.1 Generator-Torque Controller  
In the region of below-rated wind speed range, the generator torque must be varied as the square of the generator speed [36]:  
\[T_{g}=K_{g}w_{g}{}^{2} \tag{9}\]  
and  
\[K_{g}=\frac{1}{N_{g}}\frac{1}{3}\rho\pi R^{5}\frac{C_{\text{Pmax}}}{\left( \lambda_{\text{opt}}\right)^{3}} \tag{10}\]  
\[w_{g}=N_{g}\Omega \tag{11}\]  
where \(w_{g}\) is the generator speed, \(T_{g}\) is the generator torque, \(\rho\) is the air density, \(N_{g}\) is the gearbox ratio, \(R\) is the rotor radius, \(C_{\text{Pmax}}\) is the maximum power coefficient, and \(\lambda_{\text{opt}}\) is the optimum tip-speeds ratio (TSR).