esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FTN System Model Design
\(g(d-d^{\prime})\) is the channel equalization, the FTN signal passes through the multipath channel through the shaping filter and then convolves with the matched filter after the FTN system actively introduces the ISI coefficients and the convolution sum of the channel. That is, the ISI tap coefficients are aliased in the nonlinear interference generated by the fading channel. \(g(d-d^{\prime})\) can be expanded to  
\[g\big{(}d-d^{\prime}\big{)}=\int_{-\infty}^{+\infty}p\big{(}d^{\prime}-d\tau T \big{)}p^{*}\Big{(}d^{\prime}-dN_{f}\Big{)}\cdot\sum_{l=-L_{B}}^{L_{F}}h_{l} \Big{[}dN_{f}-\big{(}l+d^{\prime}\big{)}N_{f}\Big{]} \tag{6}\]  
Figure 1: Block diagram of the transmission model of FTN signal detection communication system based on the DMD-JD algorithm. where \(L_{F}\) and \(L_{B}\) represent the number of forward and backward multipaths, respectively, and \(L_{D}=~{}L_{F}+~{}L_{B}~{}+~{}1\) represents the delay spread of the frequency selective fading channel. \(h_{l}\) represents the influence of the channel gain of the \(l\)th channel on the current received signal. That is, after the FTN signal passes through the multipath channel, the non-linear noise interference that it receives can be approximately regarded as the non-linear ISI interference and passes through a non-linear multipath channel. The aliasing of two non-linear interferences will pose a major challenge to the existing receiver algorithms. In particular, when \(L_{F}~{}=~{}L_{B}=~{}0\), the multipath channel is equivalent to the AWGN channel, and the interference will degenerate into only the ISI introduced by the convolution of the shaping filter and the matched filter. The noise elimination in the system is divided into two modules. First, the ideal channel equalization is used to offset most of the multipath channel interference. The filtered signal is then subjected to data-driven FTN signal detection to eliminate the ISI introduced at the transmitting end and the nonlinear aliasing after the ISI passes through the multipath channel. The description of the signal detection module will be described in detail in the next section, and the specific simulation results will be explained in detail in Section 5.2.
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 2 Methods
### Network Architecture
#### 2.1.2. Warp Path  
The warp path is formed by stacking the baseline \(B\) several times in parallel, while maintaining parameter sharing, as shown in Figure 2. This structure enables the model to receive and process each slice of the image sequence simultaneously. The encoder part extracts a set of spatial features and serves them side by side like warp threads. The decoder part restores binary vessel segmentation masks from connectivity-aware spatial features augmented by the weft path. These are explained in the next section.  
Figure 1: Structure of the U-Net baseline \(B\). The downstream path is an encoder part which extracts convolutional features and the upstream path is a decoder part which restores segmentation masks. The network depth is fixed to 5 and skip connections connect the encoder and decoder on each level. Each level of the encoder is implemented by two basic layer sets (BLS) composed of a 3 \(\times\) 3 convolutional layer, a rectified linear unit, and a batch normalization layer, and a single 2 \(\times\) 2 max-pooling layer follows. The decoder has two BLS and a deconvolutional layer per level. The last layer, which is activated by the softmax function, produces probabilities regarding whether each pixel is a blood vessel or not.  
Figure 2: Structure of Spider U-Net. The warp path, which is formed by stacking the 2D U-Net baseline (\(B\)) \(T\) times in parallel, extracts spatial features of each 2D image and infers each segmentation mask. The weft path is placed between the encoder and decoder of the warp path and bridges information path between consecutive images (red arrows inside the weft path). Therefore, the model can learn additional inter-slice connectivity from the weft path and produces deliberate 2D segmentation masks from the spatial features augmented by the weft path. The final 3D modeling is constructed by simple trilinear interpolation.  
The number of baselines \(B\) composing the warp path is denoted as \(T\). This determines the input capacity, i.e., the size of the image sequence the model can process at one time. \(T\) can be defined freely by the network designer. However, setting \(T\) too high can be inefficient, because the context of two slices becomes weaker as the distance between two slices increases. We conducted pilot studies under various settings and found that a \(T\) larger than five did not give a notable improvement in the model performance.
## 4 Deployment of Telemedicine Services Stage
### Economic Sustainability  
EHAS was responsible for coordinating the consortium and obtaining the non-refundable funds needed to deploy and maintain the network in the short term. This funding came from international development agencies such as the Madrid City Council (Spain) or the Spanish Agency for International Development Cooperation (AECID).  
EHAS and PUCP worked together to develop a plan that would guarantee the economic sustainability of the network in the medium and long-term [6]. This plan identified the importance of involving the regional government of Loreto (GOREL) responsible for the public healthcare system in the region. The communication and telemedicine systems were expected to avoid unnecessary transfers of healthcare personnel and patients, and thus contribute to reducing fuel costs incurredby the GOREL. In return, the GOREL committed to allocating part of these savings to finance the maintenance of the telecommunications network in the medium and long term.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 3 Detection Model of Transmission Line Insulator Defects
### Depthwise Separable Convolution  
Depthwise separable convolution (DSC) is the core components of MobileNet [40]. It replaces the original 3 \(\times\) 3 standard convolution with 3 \(\times\) 3 depthwise convolution and 1 \(\times\) 1 pointwise convolution. The principle of depthwise convolution is to use a single convolution kernel for each input channel to extract features, while 1 \(\times\) 1 pointwise convolution is used for linear feature fusion of multiple outputs of depthwise convolution. DSC breaks the connection between the output channel and the convolution kernel, thus  
Figure 4: Structure of YOLOv4 algorithm.  
Figure 5: Difference between CSPDarknet53 and Darknet53 feature extraction: (**a**) Resblock, (**b**) Cross stage partial network.  
reducing the computational complexity of the model. Compared with standard convolution, the principle is shown in Figure 6.  
Suppose the input image size is \(D_{F}\times D_{F}\times M\), if the standard convolution is adopted, the parameter quantity is  
\[D_{K}\cdot D_{K}\cdot M\cdot N\cdot D_{F}\cdot D_{F} \tag{7}\]  
where \(D_{K}\) is the size of the convolution kernel, \(M\) is the number of input channels, and \(N\) is the number of output channels. If depthwise separable convolution is used, the parameter quantity of depthwise convolution and pointwise convolution is  
\[\left\{\begin{array}{l}C_{\text{DW}}=D_{K}\cdot D_{K}\cdot M\cdot D_{F} \cdot D_{F}\\ C_{\text{PW}}=M\cdot N\cdot D_{F}\cdot D_{F}\end{array}\right. \tag{8}\]  
where \(C_{DW}\) and \(C_{PW}\) are the corresponding parameter quantity of depthwise convolution and pointwise convolution. According to Equation (9), the parameter quantity of deep separable convolution is \(1/N+1/{D_{k}}^{2}\) of the standard convolution parameter quantity, and \(N{\gg}D_{K}^{2}\) in the general model structure. The convolution kernel \(D_{K}\times D_{K}\) commonly used in MobileNet is \(3\times 3\), so the parameter quantity of the DSC can be reduced to 1/8-1/9 of the standard convolution parameter quantity.  
\[\frac{D_{K}\cdot D_{K}\cdot M\cdot D_{F}\cdot D_{F}+M\cdot N\cdot D_{F}\cdot D _{F}}{D_{K}\cdot D_{K}\cdot M\cdot N\cdot D_{F}\cdot D_{F}}=\frac{1}{N}+\frac {1}{D_{K}^{2}} \tag{9}\]  
Since the practical deployment on mobile devices requires less parameter quantity and a high detection speed of the model, in MobileNet the number of channels in the model is multiplied by a hyperparameter, which is called width multiplier \(\alpha\). Then the parameter quantity of the convolution operation is  
\[D_{K}\cdot D_{K}\cdot\alpha M\cdot D_{F}\cdot D_{F}+\alpha M\cdot\alpha N\cdot D _{F}\cdot D_{F} \tag{10}\]  
The default value of \(\alpha\) in the standard MobileNet is 1.0, and the computation and parameter quantity of the model can be effectively reduced with a less \(\alpha\) value. The values of \(\alpha\) are usually set as 1.0, 0.75, 0.5, 0.25.
# Performance Analysis of Pile Group Installation in Saturated Clay
## 2 Numerical Modeling and Validation
### Single Pile Installation Model
The contact surface parameters have a significant effect on the pile bearing capacity. In this paper, the contact surface parameters were calibrated using the centrifuge test of [PERSON] et al. [33]; the specific parameters are shown in Table 2. The pore water in the soil is treated as an isotropic fluid, with the relevant fluid calculation parameters listed in Table 3. The consolidation stage involves a coupled analysis of pore water and soil, utilizing the master-slave method to decouple the fluid-solid interaction. In this approach, the fluid calculation is the master process, while the mechanical calculation serves as the slave process.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Algorithm Level
The distance measure characterizes the distance of different data points to the hyperplane, and the Gaussian fuzzy function reduces the impact of the borderline noise. The experiments were performed on several public imbalanced datasets. The results show an improvement over the original FuzzySVM-CIL [114], SVM [16], CS-SVM [149], and SMOTE-SVM [150]. In 2020, [PERSON] et al. [151] proposed a self-adaptive synthetic over-sampling approach (SASYNO). They synthesize data samples near the real data samples, giving priority to the minority class. Their method balances the classes and boosts the performance. They claimed it could be applied to different base algorithms such as SVM, k-NN, DP, rule-based classifiers, DT, and others. They used four databases for their experiments, and the results showed that their approach enhances the overall performance in terms of specificity, F-measure, and overall ranks. The classifiers where they applied their approach were SONFIS [152], SVM [125], and k-NN [153]. In 2020, [PERSON] et al. [154] presented a preliminary many objective algorithm for extracting Emerging Fuzzy Patterns (ManyObjective-EFEP). They combined fuzzy logic, for soft-computing, with NSGA-III [155]; the purpose is to increase the search space in emerging pattern mining. The algorithm is compared against an adaptation of NSGA-II [156] and they used 46 databases from the UCI repository [54]. The quality measuresused are nP, nV, WRACC, CONF, GR, TPR, and FPR [142]. They presented results of a set of patterns with more interpretability and precision. They conclude that the algorithm could be interesting for real-world applications. However, they need to continue the study and analysis due to the complex space and the trade-off among different quality measures. Finally, in Table 1, we show key merit(s) and disadvantage(s) or possible improvements for each proposal presented in Section 3.3.
# Secure and Efficient High Throughput Medium Access Control for Vehicular Ad-Hoc Network
## 4 Results
The average collision reduction of 51.31% is performed by PECA compared to ENCCMA considering varied environmental models. Secondly, the experiment is evaluated the collision of S - PECA and S - ENCCMA, respectively, considering 20 vehicles in the city, highway, and rural environments, shown in Figures 10-12, considering security scheme. The experimental outcome shows that S - PECA reduces collision by 46.15%, 63.41%, and 61.9% over S - ENCCMA, respectively, in city, highway, and rural environments. The average collision reduction of 57.15% is performed in S - PECA compared to S - ENCCMA. considering varied environmental models. The overall result shows that when a security scheme is added to PECA and ENCCMA, the model incurs an average collision overhead of 35.07% and 15.91%, respectively, when provisioning security considering the varied environment model. The overall result obtained shows the proposed PECA model performs much better than the existing ENCCMA significantly in terms of collision performance when provisioning security to it. Figure 9: Throughput performance for the rural environment. Figure 11: Collision performance for highway environment. Figure 12: Collision performance for the rural environment. Figure 10: Collision performance for the city environment. Performance of successful data transmission  
Experiments were conducted to evaluate the packet transfer of the proposed method compared to the existing method, and the overhead of VANET security configuration was also evaluated. First, the experiment evaluated the successful transmission of PECA and ENCCMA using 20 vehicles in the various environments as we can see in Figures 13-15, respectively. The experimental outcome shows that PECA performed a successful transmission of packets by 5.0%, 9.52%, and 33.8%, respectively, compared to ENCCMA in city, highway, and rural environments. The average improvement of the successful transmission is 21.66% achieved by PECA compared to ENCCMA considering varied environmental models. Secondly, the experiment was conducted to perform the packet transfer performance of S - PECA and S - ENCCMA considering 20 vehicles for C.H.R environments as shown in Figures 13-15, respectively, considering security scheme. The experimental outcome shows that S - PECA improves successful packet transmission by 15.0%, 41.93%, and 24.13% over S - ENCCMA in city, highway, and rural environments. The average improvement of successful transmission is 27.02% achieved by S - PECA compared with S - ENCCMA considering varied environmental models. The overall result show that when a security scheme is added to PECA and ENCCMA, the model incurs an average successful packet transmission overhead of 6.63% and 17.97%, respectively, when provisioning security considering varied environment model. However, we can see the proposed PECA model performed much better than the existing ENCCMA, significantly in terms of successful transmission performance when provisioning security to it. Figure 13: Successful transmission performance for city.
## 4 Results and Discussion
### Unigram Features  
With unigram features alone, good accuracy was achieved for most of the models, with maximum accuracy (94.48%), precision, recall, and F1-score achieved for SVM with unigram alone. Random Forest and Logistic Regression also failed it with an accuracy of 93.56%, which is close to SVC. The worst performance of all models concerning accuracy was recorded by Naive Bayes with 70.55% accuracy. The model had better performance in the discrimination of classes by unigrams, which means offensive language may have been identified only by the weighted appearance of individual words, and therefore, they are within class areas especially well represented for our assignment.
## 3 Results
### Genotyping Analysis of the Thrombophilic Variants in the Infertile and Fertile Women  
Table 4 shows the genotypes present in the G1691A, G20210A and C677T variants in the _FVL_, _FII_ and _MTHFR_ genes in both the cases and controls. The GG, GA and AA genotypes of the _FVL_ gene present in the cases and controls appeared to be 95%, 4%, 1% and 99%, 1%. Genotyping analysis was conducted for the cases and controls and were as follows: GA vs. GG (OR-4.168 (95%CI: 0.45-37.97); \(p=0.171\)) and AA vs. GG (OR-2.084 (95%CI: 0.06-62.84); \(p=0.665\)). The different genetic models included GA + AA vs. GG (OR-5.211 (95%CI: 0.59-45.42); \(p=0.097\)), AA + GA vs. GG (OR-0.242 (95%CI: 0.02-2.21); \(p=0.174\)) and GG + GA vs. AA (OR-0.495 (95%CI: 0.016-14.92); \(p=0.679\)). Meanwhile, the G20210A SNP present in the FI cases was found to have 93% GG, 6% GA and 1% AA genotypes, while in the control subjects, it was found to have 98% GG and 2% GA genotypes. The statistical analysis was as follows: GA vs. GG (OR-3.161 (95%CI: 0.62-16.06); \(p=0.144\)) and AA vs. GG (OR-2.108 (95%CI: 0.06-63.55); \(p=0.661\)). The various genetic models included GA + AA vs. GG (OR-3.688 (95%CI: 0.74-18.21); \(p=0.088\)), AA + GA vs. GG (OR-0.319 (95%CI: 0.06-1.62); \(p=0.149\)) and GG + GA vs. AA (OR-0.495 (95%CI: 0.01-14.92); \(p=0.679\)). However, the AA genotype was completely absent in the control population for the G1691A and G20210A variants present in the _FVL_ and _FII_ genes. Finally, the CC, CT and TT genotypes present in the infertile women were found to be 74%, 21%, 5% and 72%, 26%, 2% in the controls for the C677T variant in _MTHFR_ gene. Genetic analysis showed CT vs. CC (OR-0.785 (95%CI: 0.40-1.52); \(p=0.473\)) and TT vs. CC (OR-2.432 (95%CI: 0.45-12.94); \(p=0.284\)). The three genetic models included CT + TT vs. CC (OR-0.903 (95%CI: 0.48-1.68); \(p=0.751\)), TT + CC vs. CT (OR-1.322 (95%CI: 0.68-2.54); \(p=0.404\)) and CC + CT vs. TT (OR-0.387 (95%CI: 0.07-2.04); \(p=0.249\)).
## 4 Photon Diagnostics Systems
### Online Photon Diagnostic Systems  
These systems can be separated into residual gas systems, naturally interfering only minimally with the X-ray beam, and systems using very thin solid films or crystals, thereby only absorbing a minor fraction of the FEL pulse. This latter method is only applicable to hard X-ray radiation as otherwise the absorption is too strong.  
For residual gas diagnostic systems photoionization of rare gases (Xe, Ne, Ar or Kr) or nitrogen is applied making these devices indestructible and highly transparent [51]. This non-invasive diagnostic method is best suited for high peak energies and high average flux since there is no issue with damage or heating due to the absorbed X-ray pulse energy. At European XFEL these systems are employed in the beam transports to measure pulse energy, beam position and polarization of the X-ray pulse. Residual gas monitors can operate continuously up to very high pulse repetition rates, limited by the flight time of ions and electrons used for the measurement of pulse properties, and work even for hard X-rays if a sufficient sensitivity is able to compensate for the reduced cross-sections. As of today no reports about distortion of coherence and wavefront properties due to residual gas monitors have been reported, however for highest repetition rates and elevated gas pressures depletion may occur [52].  
Online solid-state systems employ either thin foils to scatter a fraction of the X-ray beam, using the detection of this scattered fraction to measure the pulse energy and, in a special configuration, beam position [53], or thin curved crystals to disperse the incident spectrum on a position sensitive detector [54]. In both cases, only a small fraction of the X-ray beam is absorbed or scattered, however, these systems face limitations when it comes to very high pulse energies. In particular, heat transport limitations of thin films restrict their high repetition rate applications.
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
###### Abstract  
In the laser powder bed fusion (LPBF) process, the surface quality of intermediate layers impacts interlayer bonding and part forming quality. Due to the complex dynamic process inherent in LPBF, current monitoring methods struggle to achieve high-quality in situ online monitoring, which limits the in-depth understanding of the evolution mechanisms of the surface morphology of LPBF intermediate layers. This paper employs an optimized coaxial optical imaging method to monitor key LPBF processes and analyzes the intermediate layer surface morphology evolution mechanism considering heat, force, and mass transfer. Results indicate that LPBF intermediate layer surfaces are influenced by energy density, melt pool behavior, and previous layer morphology, forming complex topological structures. At a low energy density, insufficient powder melting causes balling, extended by subsequent melt pools to form a reticulated structure and local large-scale protrusions. Heat accumulation at a high energy density promotes melt pool expansion, reduces melt track overlap, and effectively eliminates defects from previous layers via remelting, with spatter becoming the main defect. Additionally, the melt pool wettability on the part contours captures external powder, forming unique, overhanging contour protrusions. This paper enhances understanding of LPBF intermediate layer surface morphology formation mechanisms and provides a theoretical basis for optimizing surface quality.  
laser powder bed fusion; in situ co-axial monitoring; melt pool; surface topography; forming mechanism; build quality Article
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 5 Stability Analysis  
### Network Stability of DHBP  
Assume the input process as \(A^{c}_{n}(t)\), and the arrival rate matrices as \(\lambda^{c}_{n}\). \(\lambda^{c}_{n}(t)=\lim_{t\to\infty}(A^{c}_{n}(t))\) is the average arrival rate of \(A^{c}_{n}(t)\). \(\lambda^{c}_{n}\) satisfies  
\[P\Bigg{\{}\lim_{t\to\infty}\frac{1}{t}\sum_{s=0}^{t-1}A^{c}_{n}(s)=\lambda^{c} _{n}\Bigg{\}}=1. \tag{16}\]  
The stability of the network means that, in the LSN, the total input rate is equal to the total transmission rate to the destination. Thus, the network is stable if \(\lambda^{c}_{n}\) (for a long-term duration) satisfies [40]  
\[\lambda^{c}_{n}\leq\sum_{b\in N}\mu^{c}_{nb}-\sum_{a\in N}\mu^{c}_{an}. \tag{17}\]  
**Remark 1**: _For flow \(c\) at \(t\), define data rate \(X^{c}_{a}(t)\) as the accumulation of intended routing variables:_  
\[X^{c}_{a}(t)=\sum_{b\in N}\mu^{c}_{ab}(t). \tag{18}\]  
_If \(\mu^{c}_{ab}(t)\) is not scheduled, then \(\mu^{c}_{ab}(t)=0\). Similarly, we also have_  
\[Y^{c}_{a}(t)=\sum_{d\in N}\mu^{c}_{da}(t). \tag{19}\]  
According to the stability requirement [40], an important direction in ensuring the stability of routing is to minimize  
\[\lambda^{c}_{n}-\sum_{b\in N}\mu^{c}_{nb}+\sum_{a\in N}\mu^{c}_{an}. \tag{20}\]  
When the LSN satisfies the stability condition, the queue length should be bounded [25].  
**Lemma 1**: _Given \(\hat{X}^{c}_{n}=\lim_{t\to\infty}\hat{X}^{c}_{n}(t)\), \(\hat{Y}^{c}_{n}=\lim_{t\to\infty}\hat{Y}^{c}_{n}(t)\) is the average input, output. If the LSN is stable, then_  
\[\lambda^{c}_{n}-\hat{X}^{c}_{n}+\hat{Y}^{c}_{n}\leq 0. \tag{21}\]  
The proof of Lemma 1 is given in Appendix A.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Single Node Upset Recovery Analysis
#### 2.5.2 Single Event Transient Occurs at Node QB  
QB changes from '0' to '1'. This deactivates transistor P4. However, the other nodes remain unaffected by the SET at QB, preserving their original values. As a result, even though transistor P4 is deactivated, Q maintains its '1' state due to the activation of N5. Node S3 retains the value '1', which activates transistor N4, allowing QB to recover from '1' to '0'. Through this process, all nodes are restored to their original states.