esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Wind-Induced Soil Erosion
#### 4.1.2 Effect of Concentration  
For both of the tested soil types, the experimental results showed that the wind-induced soil loss decreases significantly with an increasing biopolymer concentration until they reach a plateau concentration, beyond which only marginal changes occur (Figures 4 and 5). The two-way ANOVA also reveals that the concentration has a significant effect on wind-induced soil loss (Table 3). These trends are generally consistent with the existing literature (e.g., [12; 13; 23; 24] and Table A6), which also reported that the soil loss decreases with an increasing biopolymer concentration until it reaches a plateau. Only one exception was reported by [PERSON] et al. [22], who observed that the dust control effectiveness of an Acacia gum treatment decreased when the concentration was increased from 2.0 to 3.0 wt%. This decrease can likely be attributed to an increase in biopolymer viscosity that prevented proper coating of soil particles, leading to the formation of a crust with a lower wind erosion resistance.  
It should be noted that the determined plateau concentrations only represent the experimental setup and methodology used in this study. Thus, they do not necessarily constitute the optimum concentration for field tests in which environmental factors such as biodegradation and precipitation play a decisive role. However, the determined plateau concentrations allow the comparison of the effectiveness of different biopolymer types with each other. While for medium-grained sand, XG (0.05 wt%) and CS (0.13 wt%) exhibited very low soil losses at low tested concentrations, CMC (0.50 wt%), FBPC (0.75 wt%), and WP (0.50 wt%) had to be applied at significantly higher concentrations to achieve similar performances. Thus, for future considerations, an application with XG and CS will likely be more efficient than the other biopolymers tested were.
## 5 Calculation of the Loss Contributions
### Losses Due to Eddy Currents  
The heat produced by the normal conducting materials present in the cable due to eddy currents are computed by multiplying the term \(q_{ed}\), corresponding to the eddy current losses per unit of cable length and per unit of current, by \(L_{line}\) and by the phase current [20]. This loss contribution is typically lower than the others.
## 3 Second-Order System Performance  
Between the second-order system performances, the most important are the level crossing rate and average fade duration. The LCR and AFD characterize the aspects of the  
\begin{table}
\begin{tabular}{c c c c} \hline
**ABEP-BDPSK** & \(w_{i}\) = \(-10\)**dB** & \(w_{i}\) = 0 dB** & \(w_{i}\) = 10 dB \\ \hline \(\kappa_{x}=1\), \(m=1\) & 8 & 8 & 6 \\ \hline \(\kappa_{x}=1.5\), \(m=1\) & 9 & 8 & 7 \\ \hline \(\kappa_{x}=2\), \(m=1\) & 10 & 8 & 6 \\ \hline \(\kappa_{x}=2.5\), \(m=1\) & 11 & 9 & 7 \\ \hline \(\kappa_{x}=3\), \(m=1\) & 12 & 10 & 8 \\ \hline \(\kappa_{x}=4\), \(m=1\) & 14 & 12 & 8 \\ \hline \(\kappa_{x}=1\), \(m=1.5\) & 9 & 8 & 6 \\ \hline \(\kappa_{x}=1\), \(m=2\) & 10 & 9 & 7 \\ \hline \(\kappa_{x}=1\), \(m=2.5\) & 11 & 10 & 8 \\ \hline \(\kappa_{x}=1\), \(m=3\) & 13 & 11 & 9 \\ \hline \(\kappa_{x}=1\), \(m=4\) & 14 & 14 & 10 \\ \hline \end{tabular}
\end{table}
Table 7: The number of additions in expression (26) to achieve accuracy at the 5 th significant decimal for MGF-based ABEP for BDPSK. The fading parameters \(\kappa_{x}\) and \(m\) are variable, the CCI parameters are: \(\kappa_{y}=1\), \(\mu=1\), powers \(\Omega=\)1, s = 1, and number of branches \(L=2\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**ABEP-BDPSK** & \(w_{i}\) = \(-10\)**dB** & \(w_{i}\) = 0 dB** & \(w_{i}\) = 10 dB \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=2\) & 8 & 8 & 6 \\ \hline \(\kappa_{y}=1.5\), \(\mu=1\), \(L=2\) & 9 & 9 & 7 \\ \hline \(\kappa_{y}=2\), \(\mu=1\), \(L=2\) & 11 & 10 & 9 \\ \hline \(\kappa_{y}=3\), \(\mu=1\), \(L=2\) & 13 & 13 & 11 \\ \hline \(\kappa_{y}=1\), \(\mu=1.5\), \(L=2\) & 10 & 9 & 8 \\ \hline \(\kappa_{y}=1\), \(\mu=2\), \(L=2\) & 10 & 10 & 9 \\ \hline \(\kappa_{y}=1\), \(\mu=3\), \(L=2\) & 13 & 12 & 11 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=3\) & 8 & 7 & 6 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=4\) & 8 & 7 & 6 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=5\) & 8 & 7 & 5 \\ \hline \end{tabular}
\end{table}
Table 8: The number of additions have to be summed in (26) to achieve precision at the 5 th significant digit for MGF-based ABEP for BDPSK; the CCI parameters \(\kappa_{y}\) and \(\mu\), and number of branches \(L\), are variable; constants are fading parameters \(\kappa_{x}=1\) and \(m=1\), and powers: \(\Omega=1\) and s = 1.  
dynamic temporal behavior of envelope fluctuations. Knowing these magnitudes can help us better understand and combat the disturbing effects of fading.  
The LCR is the number of crossing the specified level in a positive (or negative) direction. The AFD shows the average time that the signal envelope spends below that specified threshold level.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 1 Introduction
It outperforms ViT-based models by achieving 5% to 10% higher DSC scores across various datasets, along with a notable reduction in parameter count. Extending previous work, VM-UNet [12] employs an asymmetric encoder-decoder architecture and integrates Visual State Space blocks to enhance contextual understanding. It is the first medical image segmentation model fully constructed on an the SSM framework. VM-UNet demonstrates strong performance across multiple datasets, achieving a 3.49% improvement over U-Net and showing further gains compared to ViT-based models. Inspired by VM-UNet, we propose a hybrid architecture that fuses CNNs with Mamba networks to exploit their complementary strengths in medical image segmentation. The model comprises four main components: an encoder, a decoder, a bottleneck module, and an enhanced skip connection. In the encoder, CNNs are embedded within Visual State Space blocks to form a foundational unit termed the Hybrid CNN-Mamba (HCM) block, which captures both fine-grained local features and global semantic context. A patch merging mechanism is employed for down-sampling, enhancing representational capacity while optimizing computational efficiency. The decoder follows a similar architecture, utilizing HCM blocks alongside patch expansion layers to gradually recover spatial information and refine boundary predictions. The bottleneck comprises two HCM blocks, which enrich feature representations and support the learning of complex image characteristics. For the skip connection, we enhance the traditional design by introducing attention mechanisms to mitigate the loss of local spatial information and facilitate global multi-scale feature interactions. We evaluate our model on three widely used publicly available datasets for medical image segmentation: the International Skin Imaging Collaboration 2018 Challenge (ISIC 2018) [13], the Synapse multi-organ segmentation dataset [14], and the Automatic Cardiac Diagnosis Challenge (ACDC) dataset [15]. Results show that our model consistently delivers high segmentation accuracy with strong generalization across diverse medical imaging scenarios, underscoring its effectiveness and practical value. The main contributions of this work are summarized as follows:  
* We propose HCMUNet, a novel U-shaped architecture that integrates a hybrid CNN-Mamba network within a dual-branch encoder-decoder framework. This design facilitates efficient extraction of both local and global features while maintaining low computational complexity. * We introduce Skip Connection Dual Attention (SCDA), which enhances conventional skip connections by incorporating both channel and spatial attention. This mechanism strengthens cross-dimensional feature fusion and improves the recovery of spatial information lost during down-sampling. * We validate HCMUNet on three benchmark datasets: ISIC 2018, Synapse, and ACDC. The experimental results indicate that HCMUNet achieves high segmentation performance and exhibits strong generalization capability across diverse medical image segmentation tasks.
## Code Synchronization Algorithm Based on Segment Correlation in Spread Spectrum Communication  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2,*  
1 School of Electrical Engineering, Heilongjiang University, Harbin 150800, China;  
E-Mails: [EMAIL_ADDRESS] (A.L.); [EMAIL_ADDRESS] (R.Q.);  
[EMAIL_ADDRESS] (F.Z.)  
2 Department of Information and Communication System, Hohai University,  
Changzhou 213022, China  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (Z.Y.);  
[EMAIL_ADDRESS] (G.H.); Tel.: +86-451-8660-8320 (Z.Y.); +86-519-8519-1838 (G.H.).  
Academic Editor: [PERSON]  
_Received: 30 June 2015 / Accepted: 23 September 2015 / Published: 9 October 2015_  
**Abstract:** Spread Spectrum (SPSP) Communication is the theoretical basis of Direct Sequence Spread Spectrum (DSSS) transceiver technology. Spreading code, modulation, demodulation, carrier synchronization and code synchronization in SPSP communications are the core parts of DSSS transceivers. This paper focuses on the code synchronization problem in SPSP communications. A novel code synchronization algorithm based on segment correlation is proposed. The proposed algorithm can effectively deal with the informational misjudgment caused by the unreasonable data acquisition times. This misjudgment may lead to an inability of DSSS receivers to restore transmitted signals. Simulation results show the feasibility of a DSSS transceiver design based on the proposed code synchronization algorithm. Finally, the communication functions of the DSSS transceiver based on the proposed code synchronization algorithm are implemented on Field Programmable Gate Array (FPGA).  
**Keywords:** direct sequence spread spectrum; spread spectrum; code synchronization; segment correlation; FPGA
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 3 Sensing Connectivity and K-Node (Edge) Sensing Connectivity
### K-Node (Edge) Sensing Connectivity
#### 3.3.3 The RSA for End-to-Sensing Light Paths  
In elastic optical networks, we need to address the problem of RSA for each end-to-sensing light path. The RSA for each end-to-sensing light path must satisfy spectrum contiguity and spectrum continuity requirements. The spectrum contiguity constraint requires all the Frequency Slots (FSs) assigned to an end-to-sensing light path to be spectrally neighboring. The spectrum continuity constraint requires that all the fiber links traversed by an end-to-sensing light path use the same set of FSs. We assumed that the transponder at each requesting node was tunable, so any end-to-sensing light path can use different sets of contiguous FSs. The modulation levels can affect the QoT and the transmission distance of the light path. The spectrum can also affect the QoT and can influence the resource efficiency of OSNs. In Figure 9a, the content is replicated and maintained in sensor E and sensor F in NSFNet. Three independent end-to-sensing light paths are established to design a three-node (edge) sensing-connected sensor network. The three end-to-sensing light paths are P\({}_{1}\): 1\(\rightarrow\)E, P\({}_{2}\): 1\(\rightarrow\)2\(\rightarrow\)F, and P\({}_{3}\): 1\(\rightarrow\)6\(\rightarrow\)5\(\rightarrow\)4\(\rightarrow\)F. The transponder at requesting node_1 was tunable, so the light paths P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) used different sets of contiguous FSs. As presented in Figure 9b, the light path P\({}_{1}\) with length 118 km used the modulation level BPSK, QPSK, and 8 QAM. Figure 9c,d compare the consumed spectrum between lowest Modulation Level-Routing Spectrum Allocation (ML-RSA) light path and distance-adaptive RSA. The lowest ML-RSA chose the lowest modulation level for any end-to-sensing light path. Distance-adaptive RSA chose the appropriate modulation level according to the length of the end-to-sensing light path. The comparison shows that the distance-adaptive RSA presented higher spectrum efficiency.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Constraint Condition
#### 3.3.2 Ground Resource Constraints
\[\sum_{1\leq k\leq n}x_{k}x_{c}^{k}<N_{cm} \tag{9}\]  
\(x_{c}^{k}\) represents whether the equipment assigned to task \(k\) crosses IFSM; \(N_{cm}\) denotes the maximum allowed number of crossings through the IFSM supported by the ground receiving system.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model
### ConvGRU  
Due to the large number of parameters in ConvLSTM, overfitting of the input data may occur during the training process. To address this issue, [PERSON] et al. [28] proposed the ConvGRU model. A ConvGRU introduces convolution operations to replace the original fully connected operations and extract local spatial features of the input data based on the one-dimensional GRU network. Compared with ConvLSTM, the gate mechanism of the ConvGRU model is simpler, with fewer parameters, higher computational efficiency, and faster network convergence speed. The operation of the ConvGRU model is illustrated in Equations (6)-(9):  
\[z_{t}=\sigma(W_{xz}*x_{t}+W_{hz}*h_{t-1}+b_{z}) \tag{6}\]  
\[r_{t}=\sigma(W_{xx}*x_{t}+W_{hr}*h_{t-1}+b_{r}) \tag{7}\]  
\[h^{\prime}_{t}=\tanh(W_{xih}*x_{t}+r_{t}\circ(W_{hh}*h_{t-1})+b_{h}) \tag{8}\]  
\[h_{t}=(1-z_{t})\circ h^{\prime}_{t}+z_{t}\circ h_{t-1} \tag{9}\]  
where \(z_{t}\), \(r_{t}\), and \(h^{\prime}_{t}\) represent the update gate, reset gate, and candidate gate at time \(t\), respectively;\(f\) is the activation function; and \(h_{t}\) is the output at time \(t\).
# Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events
## 8 Minimum Prediction Error Survival Time (MPET)  
Both mean and median survival time estimates can be biased when a small sample is used, especially in models that incorporate censoring [8]. [PERSON] et al. proposed a method to find the optimum prediction time with the minimum prediction error [9]. They suggested that if an observed survival time \(t\) falls in the interval \(\frac{p}{k}<t<kp\) where \(p\) is the predicted survival time and \(k>1\), then the prediction should be considered accurate. The probability of prediction error \(E_{k}\) conditional on the predicted time \(p\) is given by:  
\[P(E_{k}|p)=P(T<p/k)+P(T>kp)\]  
This probability can be expressed as:  
\[f_{T}(p/k)=k^{2}f(kp) \tag{24}\]  
The probability of prediction error \(P(E_{k}|p)\) achieves the minimum value.  
Now, let us calculate the minimum prediction error for the Weibull AFT model. Referring to Equation (13), we have:  
\[f_{T}(p/k)=\frac{1/\sigma}{\exp(\boldsymbol{x^{\prime}}\boldsymbol{\beta})}( \frac{p/k}{\exp(\boldsymbol{x^{\prime}}\boldsymbol{\beta})})^{\frac{1}{\sigma }-1}\exp\left[-(\frac{p/k}{\exp(\boldsymbol{x^{\prime}}\boldsymbol{\beta})})^{ \frac{1}{\sigma}}\right]\]  
Substituting the above equations into Equation (24) and canceling the common parts, we obtain:  
\[k^{1-\frac{1}{\sigma}}\exp\left[-(\frac{p/k}{\exp(\boldsymbol{x^{\prime}} \boldsymbol{\beta})})^{\frac{1}{\sigma}}\right]=k^{1+\frac{1}{\sigma}}\exp \left[-(\frac{kp}{\exp(\boldsymbol{x^{\prime}}\boldsymbol{\beta})})^{\frac{1 }{\sigma}}\right]\]  
We then take the natural logarithm of both sides:\[(1-\frac{1}{\sigma})\log(k)-(\frac{p/k}{\exp(\mathbf{x^{\prime}}\mathbf{\beta})})^{\frac{1} {\sigma}}=(1+\frac{1}{\sigma})\log(k)-(\frac{kp}{\exp(\mathbf{x^{\prime}}\mathbf{\beta}) })^{\frac{1}{\sigma}}\]  
Rearranging these terms, we can solve for \(p\) to calculate the minimum prediction error survival time:  
\[p=\left[\frac{\frac{2}{\sigma}\log(k)}{k^{\frac{1}{\sigma}}-k^{-\frac{1}{ \sigma}}}\right]^{\sigma}\exp(\mathbf{x^{\prime}}\mathbf{\beta}) \tag{25}\]  
Here, \(p\) presents the minimum prediction error survival time. To estimate its standard error, the Delta method can be employed, and bootstrap methods can also be used to obtain a confidence interval for the minimum prediction error survival time.  
This approach helps to minimize prediction errors and enhance the accuracy of survival time predictions in the Weibull AFT model, especially when dealing with censored data and small sample sizes.
# An Overview of CubeSat Missions and Applications
###### Abstract
Recent advances in autonomous control algorithms [16] and contactless manipulation techniques further position CubeSats as key enablers of future space sustainability, with the potential to serve as foundational elements of next-generation orbital infrastructures. The present work aims to provide a comprehensive overview of CubeSat success and the missions they undertake. The paper is structured as follows: Section 2 presents data on the rising number of CubeSats in orbit around the Earth, demonstrating their success in recent years, along with a discussion of the enabling factors that have led to this success and future prospects. Section 3 presents the use of CubeSats for Earth observation and Earth science missions. Section 4 discusses examples of telecommunication CubeSats, classified into radio communication and laser-optical communication categories. Section 5 presents examples of CubeSats used as astronomical observation platforms. Section 6 examines CubeSats used as platforms for the execution of chemical and biological experiments, while Section 7 explores examples of CubeSats serving as instruments for lunar, planetary, and asteroid exploration. Section 8 provides illustrative examples of past, present, and upcoming CubeSat missions serving as platforms for state-of-the-art technology demonstration. Finally, Section 9 discusses various observations made during the review and presents some conclusions that are derived from them. It should be noted that this work does not aim to provide exhaustive documentation of every CubeSat mission conducted to date for each category. Instead, it seeks to offer illustrative examples that define the scope of missions currently achievable with CubeSat platforms.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Test Results
#### 4.4.1 Top-x Accuracy Between Different Pieces  
Firstly, we tested the ELP-Net under optimal verification parameters and selected the highest accuracy as the baseline result. Figure 6 presents the top 1, top 3, top 5, top 10, top 15, and top 50 prediction accuracy for all pieces using ELP-Net. Overall, the results reveal that the soldier2 piece consistently achieves the highest accuracy across all top-x levels, followed by the armored vehicle2 pieces, with the tank1 pieces exhibiting the lowest accuracy. From an operational perspective, this pattern aligns with the actual complexity of the battlefield. Soldier pieces are more dynamic and likely engage in smaller areas, making them easier to predict accurately. In contrast, tank pieces operate over larger areas  
Figure 5: The training loss and accuracy trends for the three models.  
with more significant movement restrictions, resulting in greater prediction uncertainty. Similarly, armored vehicle pieces exhibit intermediate accuracy, reflecting their moderate maneuverability and constrained operational zones.  
For decision makers, it is not essential to point to the exact hexagonal grid where the opponent pieces are located. Instead, it is more critical to identify the approximate range of hexagonal grids in which the opponent pieces are likely to be. Consequently, top 5, top 10, and top 15 prediction results are practically usable metrics as they strike a balance between accuracy and operational applicability in complex wargame scenarios.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 5 NAD+ Biosynthesis
### De Novo Pathway  
NAD+ can be synthesised de novo from tryptophan, which is an essential amino acid that must be incorporated daily through the diet. In humans, this pathway, also called the kynureine pathway, is present mainly in the liver and consists of several steps [9]. The limiting metabolite is quinolinic acid (QA). Its precursor, 2-amino-3-carboxymuccin acid semialdehyde (ACMS), can be oxidised to picolinic acid (PA) by the enzyme 2-amino-3-carboxymuccin acid semialdehyde decarboxylase (ACMSD) or enter a spontaneous cyclization, giving rise to QA. Compound QA is only formed when the ACMSD enzyme becomes saturated. Once formed, QA enters the Preiss-Handler pathway through the enzyme quinolinate phosphoribosyltransferase (QPRT) to continue NAD+ synthesis [3; 7]. Trp is also required for the synthesis of other essential molecules, such as serotonin and melatonin, and approximately only 1.66% of dietary Trp is used for NAD+ biosynthesis. This low efficiency may explain why the de novo pathway is not the main source of NAD+ in humans.
## 4 Discussion on the Results
### Compressor Status during Adiabatic Compression  
When the cooling of the compressor cylinders is stopped after Stage 1, the gas pressure will be determined using the formula:  
\[p_{\text{2 ad}}=p_{1}\cdot\left(\frac{p_{2}}{p_{1}}\right)^{\frac{\kappa}{p_{1 }}}, \tag{16}\]  
where \(p_{\text{2 ad}}\) is the gas pressure after Stage 1 (Pa), and \(\kappa\) is [PERSON]'s constant (for nitrogen and hydrogen \(\kappa=1.4\)) (1).  
The temperature of the compressor after Stage 1 in the adiabatic compression is defined by the formula:  
\[T_{\text{2 ad}}=T_{1}\cdot\left(\frac{p_{\text{2 ad}}}{p_{1}}\right)^{\frac{ \kappa-1}{\kappa}}, \tag{17}\]  
where \(T_{\text{2 ad}}\) is the gas temperature after Stage 1 (K).  
The pressure and temperature of the compressor after Stage 2 are determined analogically.  
The calculated values of the mechanical power that are required during adiabatic compression are shown in Figure 19.  
Using Formulas (2) and (3), it is possible to retroactively calculate what amount of the current (Figure 20) would flow through Stage 1 into the electric motor, if the mechanical power on the shaft of the electric motor was as it is shown in Figure 19.  
Figure 20 indicates that, in the case of a compressor cooling failure, there would be a stoppage of the electric motor power supply at almost all of the measurement times, i.e., an electric motor failure would occur.  
The adiabatic process is an extreme case of compression. It is apparent that, with decreasing rates of the cooling water flow, the required mechanic power of the electric motor shaft increases because the compressor approaches adiabatic compression. Therefore, to avoid a potential current protection switch-off, it is always necessary to ensure the determined minimum cooling water flow rate.  
Figure 19: Values of the required mechanical power at adiabatic compression.  
Another factor that may cause a compressor switch-off is an excessive increase in the outlet gas pressure. Higher pressure leads to an increase in the compression work, and also an increase in the current that is withdrawn by the electric motor.