esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 5 Methodology
### Proposed Model  
We have designed our hybrid model based on the study of previous state-of-the-art architectures and the advantages of each architecture. It integrates three different ideas involving traditional convolutions, parallel convolutions, and residual connections. The total number of layers of the proposed model is 91, which are explained in Table 1 and Figure 8.  
At the beginning of the model, we used two traditional convolutional layers with a filter size of \(3\times 3\) and \(5\times 5\) to reduce the input size of the input image. We chose these two filters to avoid losing small or large features. Picking a small filter size, such as \(1\times 1\), would act as a bottleneck that prevents large features passing through, which could help distinguish between classes; picking a large filter size, such as \(11\times 11\), would ignore small details that could lead to false classification. Therefore, we adopted average filter sizes. All convolutional layers in the model were followed by batch normalization and ReLU. Batch normalization speeds up the training progress, while the ReLU layer aids in reducing the effect of the vanishing gradient problem.  
Traditional convolutional layers are followed by five blocks of parallel convolutional layers. Each block consists of four parallel convolutional layers with four different filter sizes (\(1\times 1\), \(3\times 3\), \(5\times 5\), \(7\times 7\)). The output of these four layers is combined in the concatenation layer to pass to the next block. The blocks are connected with long and short connections. The benefits of this type of combination are that it can integrate different levels of features and learn both small and large details, as having a variety of filter sizes. Furthermore, this structure is very helpful for gradient propagation as the error can backpropagate from multiple paths.  
We employed a global average pooling layer on top of the five blocks of convolutional layers. Subsequently, three fully connected layers with two dropout layers were employed. Lastly, Softmax was adapted to produce the output. The global average pooling and dropout layers were used to overcome the issue of overfitting.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Network Protocol
#### 4.1.1 Scalable TCP for 5G MmWave Networks  
S-TCP offers excellent performance and expandability. Previously, in high \"Bandwidth-delay product (BDP)\" networks, NewReno TCP decreased cwnd by half after each packet loss, resulting in a notable decrease in link utilization. S-TCP enhances link utilization by accelerating the growth of cwnd compared to NewReno TCP. Similar to NewReno  
Figure 1: Overall architecture of this research.  
TCP, the cwnd increase and decrease process in S-TCP is determined by packet loss and acknowledgments. For each ACK received, the following is true:  
\[cwnd\gets cwnd+0.01 \tag{7}\]  
For each packet loss, the following is true:  
\[cwnd\gets cwnd-[0.125-cwnd] \tag{8}\]  
Equation (8) S-TCP manages reduction in the congestion window (cwnd) due to packet loss. S-TCP, unlike other classical TCP congestion control approaches like NewReno TCP that normally drop the cwnd to half of the current value at the first loss detection, reduces cwnd gradually by a factor of 0.125 in every packet loss incidence (except retransmission timeout). This method avoids unnecessary cwnd reduction, which would otherwise cause long recovery time in high-speed networks. With the use of a smaller reduction factor, S-TCP guarantees a quicker recovery process, thus minimizing the overall time taken for TCP to achieve its original transmission rate. For instance, for a 10 Gbps connection, this adaptive reduction process enables TCP to recover in about 15 s as opposed to the traditional 4 h recovery period. This method enhances the stability of S-TCP with high throughput and fairness in mmWave networks.
# An Improved Product Code-Based Data Hiding Scheme
## 4 An Improved Product Code-Based Data Hiding Scheme
### Example
#### 4.3.1. Embedding
\begin{array}{cccccc}1&0&1&1&0&1&0\\ 1&1&1&0&0&0&1\\ 0&1&0&1&0&0&0\\ 0&0&1&0&0&0&0\\ 1&0&0&0&0&0&0\\ 1&0&0&0&1&0&0\\ 0&0&0&0&1&0&0\end{array} \right]\]Note  
\[\text{S}(\mathbf{T})=\text{P}(\mathbf{T}_{\text{IB}})\oplus\mathbf{T}_{\text{P}}= \left[ \begin{array}{cccccc}1&0&0&0&1&1&0\\ 1&1&1&0&0&1&0\\ 1&0&0&0&1&1&0\\ 0&0&0&&&\\ 0&0&0&&&\\ 1&1&1&&&\\ 0&1&1&&&\end{array} \right]\oplus\left[ \begin{array}{cccccc}0&0&1&1&1&0&0\\ 0&1&1&0&1&1&1\\ 1&1&0&1&1&1&0\\ 0&0&1&&&\\ 1&0&0&&&\\ 0&0&0&&&\\ 0&0&0&&&\\ \end{array} \right]=\left[ \begin{array}{cccccc}1&0&1&1&0&1&0\\ 0&0&0&1&0&1\\ 0&1&0&1&0&0&0\\ 0&0&1&&&\\ 1&0&0&&&\\ 1&1&1&&&\\ 0&1&1&&&\end{array} \right].\]  
\[\text{S}(\mathbf{T^{\prime}})=\text{P}(\mathbf{T}_{\text{IB}}^{\prime})\oplus \mathbf{T}_{\text{P}}^{\prime}=\left[ \begin{array}{cccccc}0&0&0&0&0&0&0\\ 0&1&1&0&1&0&0\\ 0&0&0&0&0&0&0\\ 0&0&0&0&&&\\ 0&0&0&&&\\ 0&1&1&&&\end{array} \right]\oplus\left[ \begin{array}{cccccc}1&0&1&1&0&1&0\\ 1&1&1&0&0&0&1\\ 0&1&0&1&0&0&0\\ 0&0&1&&&\\ 1&0&0&&&\\ 0&0&0&&&\\ \end{array} \right]=\left[ \begin{array}{cccccc}1&0&1&1&0&1&0\\ 0&0&0&1&0&1\\ 0&1&0&0&0&0\\ 0&0&1&&&\\ 1&0&0&&&\\ 0&0&0&&&\\ \end{array} \right].\]  
Hence, \(\text{S}(\mathbf{T})=\text{S}(\mathbf{T^{\prime}})=\text{TS}\), where  
\[\text{S}(\mathbf{X})=\text{P}(\mathbf{X_{\text{IB}}})\oplus\mathbf{X}_{\text{ P}}=\left[ \begin{array}{cccccc}0&1&1&0&1&0&0\\ 0&0&0&1&1&0&1\\ 0&0&1&0&1&1&1\\ 1&1&1&&&\\ 1&0&1&&&\\ 0&1&0&&&\\ 1&1&0&&&\end{array} \right]\oplus\left[ \begin{array}{cccccc}0&1&0&0&1&0&1\\ 1&1&0&1&1&1&0\\ 0&0&1&1&1&1&0\\ 1&0&1&&&\\ 0&0&0&&&\\ 1&1&0&&&\end{array} \right]=\left[ \begin{array}{cccccc}0&1&0&0&0&1\\ 1&1&0&0&0&1&1\\ 0&0&1&1&1&1&0\\ 1&0&&&\\ 0&1&1&&&\\ 0&0&0&&&\\ \end{array} \right]\]  
and  
\[\text{TS}=\text{S}(\mathbf{X})\oplus\mathbf{M}=\left[ \begin{array}{cccccc}1&0& 1&1&0&1&0\\ 1&0&0&0&1&0&1\\ 0&1&0&1&0&0&0\\ 0&0&1&&&\\ 1&0&0&&&\\ 1&1&1&&&\\ 0&1&1&&&\end{array} \right].\]  
_Step 6_: \(\mathbf{T^{\prime}}\) is further refined as \(\mathbf{T^{\prime\prime}}\) by adding the 4 th code array in the first round to the toggle array \(\mathbf{T^{\prime}}\) while combining the technique of the weight approximation embedding scheme. In the second round, there are no code arrays remaining in set \(PO\), which can be further used to refine the toggle array \(\mathbf{T^{\prime\prime}}\). \[\mathbf{T^{\prime\prime}}=\left[ \begin{array}{cccccc}1&0&1&1&0&1&0\\ 1&1&1&0&0&0&1\\ 0&1&0&1&0&0&0\\ 0&0&1&0&0&0&0\\ 1&0&0&0&0&0&0\\ 1&0&0&0&1&0&0\\ 0&0&0&0&1&0&0\end{array} \right]\oplus\left[ \begin{array}{cccccc}1&0&1&0&0&0&1 \\ 1&0&1&0&0&0&1\\ 0&0&0&0&0&0\\ 1&0&1&0&0&0&1\\ 0&0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0&0\end{array} \right]=\left[ \begin{array}{cccccc}0&0&1&0&1&1\\ 0&1&0&0&0&0&0\\ 0&1&0&1&0&0&0\\ 1&0&0&0&0&0&1\\ 1&0&0&0&0&0&0\\ 1&0&0&0&1&0&0\\ 0&0&0&1&0&0\end{array} \right]\]  
_Step 7_: Finally, the stego array \(\mathbf{Y}\) is obtained by adding \(\mathbf{T^{\prime\prime}}\) to \(\mathbf{X}\).
## 5 Simulation and Evaluation
### Simulation Setup  
To evaluate the performance of FQ-AGO, we implemented our scheme in the ns-3 simulator. We simulated a network with 100 nodes randomly deployed in a 1500 m \(\times\) 1500 m area. In our simulations, all network nodes were capable of roaming randomly in the network according to the modified Random Way Point (RWP) mobility model [44] at a predefined uniformly distributed speed \((V_{min},V_{max})\), where \(V_{min}\) was fixed to be 5 m/s, and \(V_{max}\) assumed various values to reflect various network mobility levels. No group mobility models were considered in this simulation. The simulation was carried out using the IEEE 802.11 p standard for Physical and MAC layers. A Lognormal shadowing propagation channel model was adopted to simulate the channel fading. We set the values of path-loss exponent equal to \(\beta=2.7\) and standard deviation \(\delta\) dB = 6 dB as in [45; 46].  
In the simulation, we considered constant bit rate (CBR) data sessions between a randomly selected source and destination pairs. The source nodes generated a constant bit rate (CBR) traffic at a constant time interval with a packet payload size of 512 bps at a rate of \(\lambda\) packets per second.  
We studied networks with 20% of the nodes with long transmission range. These nodes were randomly deployed. By varying the number of nodes, the maximum moving velocity, and the CBR source rate, we could study the performance of FQ-AGO under various network configurations. We performed a set of 15 runs for each experiment to plot the average result for each simulation configuration, and each run was executed for 500 s of simulation time. The remaining simulation parameters are listed in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Acronym** & **Value** \\ \hline Number of nodes & \(N_{i}\) & 100 \\ Long transmission nodes ratio & - & 20\% \\ Simulation Area & - & 1500 m \(\times\) 1500 m \\ Long Transmission Range & \(R_{T}(LR)\) & 200–400 m \\ Short Transmission Range & \(R_{T}(SR)\) & 200 m \\ Maximum speed & \(v_{max}\) & 5–30 m/s \\ Packet size & \(S_{p}\) & 512 B \\ Data rate & \(DR\) & 10 pps \\ Buffer size & \(BS\) & 0 packets \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of our simulation parameters.
# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 3 CPS-Based Reliability Approach
### Statistical and Artificial Intelligence-Based Methods  
The Bayesian and Hidden Markov techniques are the most widely applied modeling techniques for reliability assessment under fuzzy environments [72; 73]. A Bayesian network is a directed acyclic graph consisting of a set of nodes, representing random variables and a set of directed edges, representing their conditional dependencies. The dependencies in a Bayesian network can be adaptively determined from a dataset through a learning process. The objective of this training is to provide the best description of the probability distribution over the dataset because the attribute values are not supplied in the dataset [74].  
Figure 1: Procedure for sensor reliability assessment using a model-based approach for sensor data and key performance indices.  
In addition to those probabilistic methods, new tools have been reported in the literature, highlighting the use of Artificial Intelligence (AI) techniques and, in particular, Machine Learning (ML), to solve complex situations [34]. AI techniques also provide cognitive abilities, so that performance may be improved by increasing network lifetime and reliability [75]. Those techniques include ANN and fuzzy inference systems [76; 77]. [PERSON] et al. proposed a soft-computing system based on Genetic Algorithm-Support Vector Regression (GA-SVR), in order to facilitate the reliability and survivability of the Structural Health Monitoring (SHM) system faced with, for example, an invalid fiber link in the sensor network [78].
## 4 Results and Discussion
### Effects of Background Winds on Scatterometer Retrievals
The wind divergence field obtained from UHR-MERRA-2 winds at the overpass of ASCAT-A (Figure 8a) captured divergence along the front edge of the MCS. On the contrary, UHR-RAP (Figure 8b) captured convergence over the same area. [PERSON] and [PERSON] (1973) found that as cold pools spread out along the surface, they form a surge of flow that leads to moisture convergence induced by the low-level wind. At the overpass of ASCAT-B the UHR-MERRA-2 (Figure 8c) and UHR-RAP (Figure 8d) divergence field has the same response as in the overpass of ASCAT-A, i.e., divergence and convergence, respectively. Thus, the depiction of low-level wind features associated with convective systems depend to a large degree on the accurate representation of the background winds. This analysis also supports the usage of the wind gradient as more independent metric to  
Figure 7: Background wind vectors are depicted in black for **(a)** ECMWF, **(b)** MERRA-2 and **(c)** RAP model at 02:51 UTC (ASCAT-A) on 28 May 2016; similarly for **(d,e,f)** but at 03:42 UTC (ASCAT-B). KHGX rainfall rate is depicted in light blue filled contours and reported at 02:57 UTC for ASCAT-A and 03:46 UTC for ASCAT-B. Buoy measurements at different stations are depicted in red vectors. capture outflow boundaries, as natural characteristics can complicate the interpretation of the divergence field.
Data Link with a High-Power Pulsed Quantum Cascade Laser Operating at the Wavelength of 4.5 \(\upmu\)m  
[PERSON]  
This article is a short study of the application of high-power quantum cascade lasers and photodetectors in medium-infrared optical wireless communications (OWC). The link range is mainly determined by the transmitted beam parameters and the performance of the light sensor. The light power and the photodetector noise directly determine the signal-to-noise power ratio. This ratio could be maximized in the case of minimizing the radiation losses caused by atmospheric attenuation. It can be obtained by applying both radiation sources and sensors operated in the medium infrared range decreasing the effects of absorption, scattering or scintillation, beam spreading, and beam wandering. The development of a new class of laser sources based on quantum cascade structures becomes a prospective alternative. Regarding the literature, there are descriptions of some preliminary research applying these lasers in data transmission. To provide a high data transfer rate, continuous wave (cw) lasers are commonly used. However, they are characterized by low power (a few tens of mWatts) limiting their link range. Also, only a few high-power pulsed lasers (a few hundreds of mWatts) were tested. Due to their limited pulse duty cycle, the obtained modulation bandwidth was lower than 1 MHz. The main goal of this study is to experimentally determine the capabilities of the currently developed state-of-the-art high-power pulsed quantum cascade (QC) lasers and photodetectors in OWC systems. Finally, the data link range using optical pulses of a QC laser of \(\sim\)2 W, operated at the wavelength of \(\sim\)4.5 \(\upmu\)m, is discussed.  
free space optics; quantum cascade lasers; optical wireless communications; infrared detectors 21
## 4 Discussion
### Study Limitations  
This study has several limitations. An important limitation is the retrospective design, which could affect the quality of the data. For instance, data were extracted from medical records that were filled out by clinical staff, and the use of administrative data can also expose the study to various deficiencies. These could include underreporting, misclassification, and missing data. The use of the FIM instead of the Spinal Cord Independence Measure for assessing the functional improvement of patients with spinal cord injury is also a limitation of this study. The retrospective nature/design of the study and the choice of FIM over SCIM for assessing functional independence exposes the study to inherent limitations and deficiencies, including the quality, accuracy, and sensitivity of the data. Moreover, the use of VAS, which is a subjective evaluation, could not give us specific details about the type of pain. The data collected in the dynamics show an improvement of the clinical-functional parameters after only 10 days of treatment, but it could be necessary for a long period of time for treatment or follow-up.
# Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
## 2 Related Work
### Pipeline Construction Acceptance  
BIM technology is gradually transforming traditional inspection methods in pipeline construction acceptance testing. Conventional approaches primarily rely on manual visual inspections and non-destructive testing (NDT) [5]. Although these approaches ensure pipeline quality, they are time-consuming and vulnerable to human error. On the contrary, the BIM creates a three-dimensional digital pipeline system model that precisely records and monitors every stage of the construction process [6]. Researchers have highlighted that BIM models allow inspectors to compare actual construction with design drawings in real time, promptly identifying and rectifying potential issues, such as deviations in pipeline positioning and improper connections [7].  
Recently, with the rapid advancement of deep learning technology, more studies have begun to explore its application in automated pipeline acceptance testing, mainly focusing on the effectiveness of convolutional neural networks (CNNs) in identifying defects in images. For instance, ref. [8] effectively detected and located corrosion in pipelines through a custom CNN, significantly improving the accuracy and efficiency of corrosion detection. Additionally, in [9], the authors proposed PipeNet, a novel deep learning network capable of detecting pipes regardless of the input data size and the target scene scale while predicting pipe centerline points along with other parameters. Furthermore, integrating sensor data from building information modeling (BIM) with deep learning algorithms facilitates real-time monitoring and analysis of pipeline conditions. This integration enables predictive maintenance strategies, reducing downtime and maintenance costs [10]. Such an approach enhances the accuracy and efficiency of inspections and provides valuable insights for future maintenance. Hence, combining BIM and deep learning is paving the way for more intelligent construction management practices, enabling a shift towards a more proactive approach in pipeline maintenance [11].  
Despite these advancements, current research still faces challenges related to data quality, model interpretability, and integration with existing inspection systems. Therefore, future studies should continue to investigate the potential of BIM in pipeline construction, focusing on developing datasets, algorithm optimization, and practical system integration to promote the intelligent advancement of pipeline construction acceptance testing.
# Zero-Voltage Ride-Through Capability of Single-Phase Grid-Connected Photovoltaic Systems
###### Abstract  
Distributed renewable energy systems play an increasing role in today's energy paradigm. Thus, intensive research activities have been centered on improving the performance of renewable energy systems, including photovoltaic (PV) systems, which should be of multiple-functionality. That is, the PV systems should be more intelligent in the consideration of grid stability, reliability, and fault protection. Therefore, in this paper, the performance of single-phase grid-connected PV systems under an extreme grid fault (i.e., when the grid voltage dips to zero) is explored. It has been revealed that combining a fast and accurate synchronization mechanism with appropriate control strategies for the zero-voltage ride-through (ZVRT) operation is mandatory. Accordingly, the representative synchronization techniques (i.e., the phase-locked loop (PLL) methods) in the ZVRT operation are compared in terms of detection precision and dynamic response. It shows that the second-order generalized integrator (SOGI-PLL) is a promising solution for single-phase systems in the case of fault ride-through. A control strategy by modifying the SOGI-PLL scheme is then introduced to single-phase grid-connected PV systems for ZVRT operation. Simulations are performed to verify the discussions. The results have demonstrated that the proposed method can help single-phase PV systems to temporarily ride through zero-voltage faults with good dynamics.  
single-phase photovoltaic (PV) system; grid synchronization; phase-locked loop (PLL); zero-voltage ride-through (ZVRT); grid fault +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Comparison of Findings with Existing Studies, Applications, and Limitations
Design variable values may also be constrained; for example, when the selection of building orientation is limited by the surrounding environment, it can be determined first, and then the best scheme can be determined from among the orientation options. Similarly, when constrained by multiple conditions, the optimal scheme can be determined step by step, starting from the restricted objective or variable. The priority order can also be given in combination with the entropy value proposed in Section 3.1. In the future, a user-friendly interface could be developed to facilitate operations and filter related parameters. In addition, although the above findings can provide a basis for design practice, there are several limitations that need to be noted. Firstly, considering the shielding effect of the surrounding buildings against solar radiation, we chose the residence in the middle of the building group as the research object, but buildings in the first row, which are unshielded, will receive more solar radiation. Secondly, the utilization of solar radiation considered in this paper is an ideal state, which takes the building as a whole, assuming that the south facade is laid with photovoltaic panels except for the windows, but some locations on the facade receive less solar radiation and will not be laid with photovoltaic panels in reality, so the photovoltaic power generation and carbon sink will be reduced. Thirdly, our analysis was confined to a single climate zone. This research will be expanded to multiple climate zones to provide further reference for more regional building design.
## 2 Methods  
The workflow of the proposed method is shown in Figure 1. Similar to a basic local stereo matching method, the proposed method consists of four steps. (1) Matching cost computation: we propose a new matching cost computation method that uses a combination of the enhanced image gradient-based cost and improved census transform-based cost. (2) Cost aggregation: first, a cross-based adaptive shape support window is constructed for each pixel. Then modified guided filter is implemented based on the constructed support window to aggregate the matching cost inside the window. (3) Disparity selection: a winner-take-all (WTA) strategy is used to find the optimal disparity for each pixel according to the aggregated cost. (4) Multi-constraints-based disparity refinement framework. In order to further detect the incorrect matching results, a multi-constraints-based disparity refinement framework is implemented. Outlier detection with left-right consistency checking process, occlusion/mismatch handling, weighted median filter, and subpixel enhancement are included in this framework.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 5 Simulation Results
### Performance in Sparse Scenario
#### 5.1.2 Convergence Analysis  
To show the convergence of MCBR (Neighborhood Information), Figure 3 depicts the evolution of the number of links on each subchannel. It is seen that the subchannel allocation converges to a steady state in about 78 iterations. This observation validates the convergence of the proposed MCBR (Neighborhood Information) algorithm.  
Figure 2: Average system capacity with varying numbers of subchannels.