esa-sceva/satcom-chunk-collection huggingface dataset:
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 2 Preliminaries
### Value Matrix  
In order to evaluate the transmission effect of the equipment in each operating frequency band, the value matrix is set, which is obtained by polling the transmission fre  
Figure 1: Model framework of DSTT.  
quency band during the trial operation of the station's transmission equipment. The value matrix can be described as follows:  
\[ValueMatrix=\begin{bmatrix}Va_{11}&Va_{12}&\cdots&Va_{1i}&\cdots&Va_{1m}\\ Va_{21}&Va_{22}&\cdots&Va_{2i}&\cdots&Va_{2m}\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ Va_{i1}&Va_{i2}&\cdots&Va_{ij}&\cdots&Va_{im}\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ Va_{n1}&Va_{n2}&\cdots&Va_{nj}&\cdots&Va_{nm}\end{bmatrix} \tag{3}\]  
where \(n\) is the number of equipment, \(m\) is the number of frequency-band divisions, and \(Va_{ij}\) is the evaluation value of the transmitting effect of the \(i\)-th equipment working in the \(j\)-th frequency band.  
Based on the supervised-learning theory, the value of the matrix is weighted and adjusted in combination with the results collected from each daily transmission to ensure that the matrix is dynamically updated to meet the practical needs of the system.
## 4 Results and Analyses
### Tunnel Convergence  
Figure 11 illustrates the horizontal displacement pattern of the tunnel lining. The image reveals that under ground load, the lining's deformation resembles a \"transverse ellipse\" with its horizontal axis elongated and vertical axis compressed. As the external pressure increases, this elliptical distortion becomes more pronounced. The deformation is most noticeable at the vault, arch bottom, and haunch of the tunnel, where the joints open differently: those near the vault and arch bottom tend to open inward, while those near the haunch open outward. Figure 11c,d reveals that the lining structure becomes asymmetrical following deformation. The main reason for this is that the structural model employed in this study is a lining ring structure assembled with three-ring misaligned joints, where the position of the top piece in each ring differs. This three-ring misaligned joint assembly does not result in a symmetrical lining ring structure. Furthermore, the earth pressure acting on the structure and the additional loads generated by surface stacking are not vertically symmetrical. Therefore, the deformation of the lining ring structure should also be asymmetrical.  
Figure 12 demonstrates the correlation between the horizontal convergence of the tunnel lining (\(\Delta D\)) and the magnitude of ground surcharge (\(P_{0}\)). The concept of horizontal convergence in tunnel engineering refers to the change in the tunnel's horizontal diameter, a commonly used metric for assessing tunnel deformation. \(\Delta\)D represents the difference in tunnel diameter before and after deformation. A positive \(\Delta\)D indicates an increase in tunnel diameter, whereas a negative \(\Delta\)D signifies a decrease. This clarification has been added to the document for comprehensive understanding. The data indicate a direct, linear relationship: as \(P_{0}\) increases, the horizontal convergence also increases. This pattern is primarily due to the use of constant-stiffness compression springs in the study to model the interaction between the tunnel and the surrounding soil. When the tunnel lining is subjected to ground surcharge, it deforms and the ground springs provide a restraining force. These radial springs, designed to only compress, maintain constant stiffness. Consequently, as  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & \(P_{\textbf{v1}}\) **(kPa)** & \(K_{\textbf{r}}\) **(MPa/m)** & \(K_{\textbf{0}}\) & \(S\) **(m)** & \(B\) **(m)** & \(L\) **(m)** & \(P_{\textbf{0}}\) **(kPa)** \\ \hline C1 & 300 & 10 & 0.6 & 0 & 18 & 18 & 120 \\ C2 & 300 & 10 & 0.6 & 0 & 18 & 18 & 240 \\ C3 & 300 & 10 & 0.6 & 0 & 18 & 18 & 360 \\ C4 & 300 & 10 & 0.6 & 0 & 18 & 18 & 480 \\ C5 & 300 & 10 & 0.6 & 0 & 18 & 18 & 600 \\ C6 & 300 & 10 & 0.6 & 0 & 18 & 18 & 720 \\ C7 & 300 & 10 & 0.6 &
# Acoustic and Aerodynamic Coupling during
## 1 Introduction
However, this conclusion was drawn only on the basis of experiments with an acoustic excitation by a loudspeaker at the glottis. Although [PERSON] et al. [33] investigated the flow in a simplified VT replica excited with vocal fold replicas that show flow-induced oscillations, they did not perform a comparison with realistic VT shapes. A second restriction is that previous studies were performed with VT models that exclusively rely on the geometry of single subjects. The influence of individual characteristics were not considered or evaluated, respectively. Thus, our hypotheses are that (1) the resulting sound signal includes additional sound components generated by the unsteady flow field inside the VT when the VT is excited by a pulsatile jet flow and (2) a generalized geometry of the VT shows the relevant formants with reduced individual sound characteristics of the unique subjects' VT geometries. Hence, the first aim of our study is to investigate the influence of the VT simplification on the radiated sound in a setup with coupled acoustics and aerodynamics of the VT replicas and self-oscillating vocal folds. Thus, we manufactured VT replicas with realistic and simplified shape, according to [PERSON] et al. [13], based on the VT geometries from the MRI data obtained from six professional tenors who phonated the vowel /a/ at same pitch and register [34]. These VT replicas were included in our experimental larynx replica with self-oscillating vocal folds [35]. To decrease the influence of individual geometric features, the second aim of this study is to generate and evaluate a simplified, mean VT replica that is based on the simplified VT geometries of the six tenor subjects. To evaluate the quality, we evaluated the first four formant frequencies of the different VT replicas.
# On the Security of Offloading Post-Processing
## 3 Outsourcing Information Reconciliation  
As mentioned in Section 2, information reconciliation (IR) is the most demanding task in post-processing of QKD, independent of the protocols being used on the quantum level. Error correction is computationally intense because of high error rates encountered in combination with the constraints on the amount of information disclosed during error correction. The information revealed during the public discussion must be kept as short as possible to maximize the overall system performance. Ideally, IR works close to the Shannon limit. If keys have to be processed in real time, error correction is the bottleneck of post-processing and can introduce substantial problems in resource constraint environments.  
On a quantum level, QKD protocols can be divided into two classes--_discrete variable_ (DV) and _continuous variable_ (CV) QKD--which also result in different requirements for information reconciliation. In DV-QKD protocols (e.g., BB84 [17]), qubits are measured by single photon detectors. Due to channel attenuation and non-perfect detectors, the rate of detected photons is typically orders of magnitudes lower than the rate of prepared photons. Consequently, in DV-QKD, IR schemes must typically provide the possibility to operate on the final key rates in the order of kilobits per second [18] up to megabits per second [14]. In CV-QKD systems, signals are only perturbed and not lost through channel effects, resulting in very high raw key rates but also high error rates compared with a discrete variable system.  
Additionally, two basic types of IR protocols can be distinguished in QKD systems. On the one hand, interactive two-way protocols have been developed for highly efficient correction capabilities near the Shannon limit, with CASCADE [19; 20; 1] being the most prominent representative. They can achieve smaller leakage than any one-way protocol, but their practical performance is limited due to their interactive nature by the latency of the classical channel. On the other hand, forward error correcting schemes have been adopted and developed further to be used in operational regimes encountered in QKD [21]. One-way IR based on low-density parity check codes (LDPC) is currently the most efficient representative in this category and is used in many prototype systems [22].  
One-way schemes have many desirable properties when it comes to realization and can easily be parallelized to increase performance. Therefore, in our work and for the design of our protocols, we assume the use of forward error correction where necessary and, in particular, LDPC-based error correction in case concrete implementations are considered.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Targeted Use Cases  
The objective of Wi-Fi 6 is to satisfy the requirements of mobile users especially in dense environments, for IoT networks, and for high-bitrate, low-latency multimedia applications. This section describes these use cases and how Wi-Fi 6 resolves challenges.
## 5 Existing Security Mechanisms
### Resilience to Node Capture  
Resilience against node capture is one of the most challenging problems in sensor networks. In real-time healthcare applications, the medical sensors are placed on a patient's body, whereas, the environmental sensors are placed on hospital premises (e.g., ward room, operation room _etc._) which may be easily accessible to attackers. Thus, an attacker might be able to capture a sensor node, get its cryptographic information and alter the sensor programming accordingly. Later, he/she can place the compromised node into the network, and that could endanger application success [87]. The current cryptographic functions (_i.e._, node authentication and identification) may detect and defend against node compromised attacks to some degree, but these compromised node attacks cannot be detected instantly [87], which is a big issue for healthcare application. For example, consider the case of a false alarm. One possible solution to prevent this attack is to use tamper resistant hardware; however, tamper resistant hardware is not a cost effective solution.
## 2 Literature Review
However, it only provides integrity at one part of the communication, which is between cluster heads and BS. A semi-blind watermarking technique that uses linear interpolation to embed the watermark into the data was proposed by [PERSON] et al. [25]. The main advantage of the technique is that it does not introduce any extra bits for the watermark. However, it uses a fixed watermark parameter for all nodes in the system that can be easily cracked. Robust watermarking, which is used to provide copyright protection, was combined with the fragile watermark by [PERSON] et al. [32]. The authors proposed a digital watermarking scheme based on multiple functions to protect images transmitted in a WSN. The scheme embeds two types of watermarks (robust and fragile watermarks) to provide copyright protection and integrity, respectively. The robust watermark is generated from the stable features of the original picture. Image pixels are used to generate the fragile watermark. The scheme is only used in wireless multimedia sensor networks that deal with visual sensors. [PERSON] et al. [33] proposed another robust watermarking scheme for node authentication. The proposed scheme computes the watermark based on a randomly chosen data sampling time interval. The scheme is not robust in detecting false data insertion attacks, as it does not consider all of the data in generating the watermark. Reversible watermarking techniques were proposed in [34; 35; 36]. [PERSON] et al. [34] proposed a reversible watermarking algorithm based on prediction-error expansion. The algorithm groups every two adjacent data items together. The first one is used to generate the watermark, while the other is used as a carrier for it. The drawback of this method is that verification is end-to-end, since verification needs extra storage as it uses buffering. [PERSON] et al. [35] proposed a similar end-to-end algorithm (called RDE), in which the watermark is generated based on the difference expansion. The algorithm groups \(n\) data items and uses them all to calculate the watermark. The embedding process calculates the weighted difference between each data item with the first data item. The embedding process is straightforward, as every bit of the watermark is embedded in the least significant bit of each data item. This algorithm can ensure the complete recovery of data at the base station. However, it cannot detect integrity along the route of the communication. [PERSON] et al. [36] have proposed an improved version of the difference expansion method used by [PERSON] et al. [35]. The whole network was assumed to be viewed as an image with each sensor node as a pixel. The technique generates a single watermark for the all of the system's data and splits it into several segments. Each segment is assigned to random sensors for embedding. The extracting operation will use a location map to know which sensor nodes were chosen to embed the watermark. The weakness of this technique is that responsibility for the watermarking process in each round is given to a single node.
## 4 Results
### SHM Plate with Stringer
#### 4.1.2 Detectability of Damage  
The starting point for the analysis of the measurement data obtained from the _SHM plate_ is shown in Figure 10. This visualization contains a full analysis of the complete SHM-dataset by means of the signal energy of the differential signal. Therefore, the average signals of the 20 baseline measurements for each actuator-sensor pair serves as reference. The arrows on top indicate the baseline state (B) and the damage states \(D_{1}\) to \(D_{3}\). An increasing damage indicator can be seen for each structural condition due to the increasing damage size.  
In addition, we have considered the signal energy to evaluate the differences between the 20 baseline measurements \(u_{i,j}^{B}[t]\) and the measurements for damage \(D_{1}\) to \(D_{3}\) represented by \(u_{i,j}[t]\). The index \(i\) denotes the \(i\)-th actuator and the index \(j\) the \(j\)-th receiver. We have first bandpass-filtered the data--separately for each transmitter-receiver pair-- and computed the average from all baseline measurements. In a next step, the signal energy of the differential signal \(E(i,j)\) for each reference damage size was computed according to:  
\[E(i,j)=\sum_{k=1}^{N}\left(u_{i,j}[t_{k}]-u_{i,j}^{B}[t_{k}]\right)^{2}. \tag{2}\]  
In this equation, the variable \(N\) denotes the number of time domain samples.  
Figure 11 shows the analysis of this metric for three representative actuator-sensor combinations. The differential signal \(E(i,j)\) is plotted over the damage size. It must be noted, that each damage size has 5 samples in this graph for the 5 separately performed measurements. The biggest changes in \(E(i,j)\) are in the pairings where the reference damage lies on the vertical line between the transducers. Moreover, the metric increases in proportion to the damage size so that the proposed reference damage  
Figure 9: Ultrasound measurements at \(f_{c}=260\) kHz of the flat _SHM plate_ (OGW1) and the _SHM plate_ with stringer (OGW3). In this example, the first transducer \(T_{1}\) works as actuator and all other transducers \(T_{2}\)–\(T_{12}\) serve as receivers. The influence of the stringer can be clearly identified especially for receivers \(T_{7}\)–\(T_{12}\) that are located on the opposite side of the stringer.  
seems to be a good candidate for reliability assessment of GW-SHM systems. Since a reference damage with a stepwise adjustable size was used, the corresponding damage metric should also have a step-like behaviour as shown in the beginning for the reference damage \(D_{2}\). However, the metric is still sensitive to the repeating application of the reference damage leading to fluctuations. In conclusion, the \(E(i,j)\) curve is a robust indicator for damage occurence.
## 3 Proposed Method
### Spatial-to-Depth Convolution
#### 3.3.1. SPD-Conv Network Structure  
SPD-Conv represents a novel module within Convolutional Neural Networks (CNNs) that addresses the challenges encountered by traditional CNN models when processing low-resolution images and small objects. By introducing spatial-to-depth transformations and non-striding convolution operations, SPD-Conv successfully enhances the performance of these models in such tasks. Composed of a spatial-to-depth (SPD) layer and a non-striding convolution (Conv) layer, this module can be seamlessly integrated into most CNN architectures. SPD-Conv emerges as an efficient and practical neural network component, and its overall structure is illustrated in Figure 6. The integration of SPD-Conv into CNNs offers a promising approach to improve the detection accuracy and robustness of models dealing with complex visual scenes, particularly those involving low-resolution inputs or the need for precise localization of small objects.  
Figure 5: Example of replacing the backbone network in YOLOv8 with SPD-Conv modules. This modification aims to enhance the model’s capability to detect low-resolution images and small target defects.  
Figure 6: SPD-Conv Network Architecture. By integrating Spatial to Depth (SPD) layers with non-strided convolutional layers, this architecture optimizes the convolutional neural network’s handling of low-resolution images and small target defects, enhancing model performance while minimizinginformation loss. The key elements of the SPD convolution are composed of (**a-e**), Feature map (**a**): A traditional feature map that contains information on the number of channels, height, and width. Space-to-depth transformation (**b**): Through the space-to-depth operation, spatial blocks of pixels are rearranged into the depth/channel dimension, increasing the number of channels to 4 while reducing the spatial dimensions by half. Channel merging (**c**): Different channel groups are merged along the channel dimension. Addition operation (**d**): The merged feature map may be added to other processed feature maps (not detailed in the figure). Stride-1 convolution (**e**): A stride-1 convolution is applied to the resulting feature map, reducing the channel dimension while maintaining spatial resolution, which remains half the original size.
## 7 Applications and Future Research Directions of RIS
### Solving the Localized Dead Zone Issue  
The use of RISs to counteract localized coverage holes in urban scenarios and indoor harsh propagation conditions is another promising case study. Indeed, in many urban and heavily populated cities around the world, there are localized dead zones where the signal strength is not good enough. In indoor settings, including industrial plants and underground metro stations, similar problems exist. Conventional options for overcoming coverage holes in these situations are to deploy more BSs or relays/repeaters. These solutions, unfortunately, are costly and raise the carbon footprint of wireless communications. On the other hand, the introduction of RISs is a cost-effective and environmentally sustainable approach to address the problems of localized gaps in coverage.
## 4 Results
### Experiment Setup  
In the following section, we present the results obtained by testing our models trained in different conditions:  
* Atrial fibrillation prediction:
* Training model on synthetic and real data.
* Training model on real data only.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Number** & **Feature Name** & **Synthetic (1000)** & **Real (58)** & \(p\)**-Value** \\  & & **Median [IQR]** & **Median [IQR]** & **Median [IQR]** & \\ \hline
11 & **NNxx (beats)** & 10.00 [0.00, 24.00] & 1.50 [0.00, 13.25] & **0.009** \\
12 & **pNNxx (\%)** & 5.62 [0.00, 13.14] & 0.92 [0.00, 7.75] & **0.007** \\
51 & **VLF (Hz) AR** & 0.04 [0.03, 0.04] & 0.04 [0.04, 0.04] & **<0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Synthetic features statistically different with respect to the original AF data. To see the full table with all listed features check Table A3 in Appendix A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Number** & **Feature Name** & **Synthetic (100)** & **Real (23)** & \(p\)**-Value** \\
**Number** & **Name** & **Median [IQR]** & **Median [IQR]** & **Median [IQR]** \\ \hline
14 & **TINN (ms)** & 49.50 [24.75, 74.25] & 115.00 [71.50, 189.00] & **<0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Synthetic features statistically different with respect to the original CE data. To see the full table with all the listed features, check Table A4 in Appendix A.  
Figure 2: Box plots comparing two examples of HRV features, SDNN (ms) and pNNxx (%), between synthetic (blue) and real (green) data for cardiovascular events (CE). Each box shows the median (horizontal line), interquartile range (IQR, box boundaries), and mean (represented by the triangle inside the box), with whiskers indicating the data range. \(p\)-values (SDNN: 0.840, pNNxx: 0.827) are displayed inside the chart, highlighting the similarity between the synthetic and real data distributions.  
* Cardiovascular events prediction:
* Training model using transfer learning and real data.
* Training model on real data only, without transfer learning.
## 1 Introduction
### 3-PG Model Description  
The 3-PG model (Physiological Principles in Predicting Growth) [27] is a simplified process-based, single species, stand growth model. 3-PG calculates gross primary productions (GPP) using utilizable, absorbed photosynthetically active radiation and canopy quantum efficiency. It is a relatively simplified model, which applies well-established physiological relationships and proven constants [27,28]. The calculation of respiration is not necessary by 3-PG model; instead, the model uses the ratio of net to gross primary production (NPP/GPP) [27]. Given its simplicity, 3-PG is more accessible to local forest managers than other research models, which often depend upon complicated physiological principles. Compared to other simplified models that produce total carbon fixed and biomass, 3-PG also yields tree mass along with allocation to stem, foliage, and roots based on allocation equations [27]. This makes the 3-PG model more practical when distributions of biomass and tree growth constraints become a major concern to foresters. Similar to other models, 3-PG requires a range of inputs which can be summarized into three categories--climate data, species parameters (listed in Table 1), and site variables. Climate data are provided in monthly time steps and include total short wave radiation, mean precipitation, number of frost days, and minimum and maximum temperatures [27]. Information about soil depth, available soil water (ASW), initial stocking and stand age and individual species parameters are also required [29]. These site variables and species parameters can be derived from the literature and field measurements [30]. 3-PG utilizes climate modifiers which are dimensionless numbers ranging from 0 to 1, representing the extent to which a climatic factor can constrain photosynthesis by high daytime vapor pressure deficits (VPD), soil water deficits, and extreme minimum/maximum temperatures [27]. The 3-PG model estimates the site productivity and climatic modifiers for a given species at a stand level. Site productivity is expressed at either annual or monthly time steps and includes stand density (TPH), leaf area index (LAI), mean annual increment (MAI), mean diameter at breast height (DBH, also known as DOB\({}_{1.3}\)), stand volume, and basal area (BA). For this study, species parameters for Chinese fir are shown in Table 1. They were derived as noted by [2,31,32]. One of the key benefits of the 3-PG model is that it is a physiological model driven by key physiological parameters. As a result, parameters do not need to be empirically fitted every time. We note that we have undertaken this approach successfully in a number of previous papers [28, 29, 30,33,34].