esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### SISO and SIMO  
Figure 2 displays the BER of SISO and SIMO channel diversity performance. In this case, the data were transmitted over a single channel at the transmitter, while receive diversity was used at the receiver with a uniform linear array antenna that offered array gain to increase SNR and reduce BER. Table 1 lists the simulation parameters. The wavelet family we considered for our research was dB8, and the modulation strategy we used was QAM modulation. The performance of the SIMO DWT, with an SNR of 13.5 dB at \(10^{-3}\) BER, was better than that of the SISO DWT with wavelet transform and much greater than the minimum 13.5 dB required for mm wave communication systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameters** & **SIMO DWT** & **SISO DWT** \\ \hline Modulation Scheme & QAM 16 & QAM 16 \\ DWT family & dB8 & dB8 \\ Type of channel & AWGN & AWGN \\ Number of antennas & \(1\times 2\) & \(1\times 1\) \\ SNR range & [0:20] & [0:20] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for SISO and SIMO.
# A Unified Visual and Linguistic Semantics Method for Enhanced Image Captioning
## 4 Methodology
### Overview  
The methodology is visually depicted in Figure 1. Employing GPT-2 as our language model, we utilize its tokenizer to convert the caption into a sequence of embeddings. Extracting visual information from image \(I^{i}\) involves leveraging the visual encoder from pre-trained models such as CLIP [31] and SegmentCLIP. Subsequently, a lightweight mapping network, denoted as \(M\), facilitates the transformation of the joint embedding into k embedding vectors:  
\[p_{1}^{i},\ldots,p_{k}^{i}=M(\text{CLIP}(I^{i})\oplus\text{SegmentCLIP}(I^{i})). \tag{3}\]  
Here, \(\oplus\) denotes the concatenation operator, and each vector \(p_{j}^{i}\) shares the dimensionality of a word embedding. Subsequently, we concatenate the acquired visual embedding with the embeddings of caption \(S_{i}\):  
\[Z^{i}=p_{1}^{i},\ldots,p_{k}^{i},S_{1},\ldots,S_{\ell}^{i}. \tag{4}\]  
During the training stage, we input the language model with the concatenated prefix-caption sequences \(\left\{Z^{i}\right\}_{i=1}^{N}\). Our training objective revolves around autoregressively predicting the caption tokens conditioned on the prefix. For this objective, the mapping component \(M\) undergoes training employing straightforward yet impactful cross-entropy loss.  
\[\mathcal{L}_{X}=-\sum_{i=1}^{N}\sum_{j=1}^{\ell}\log p_{\theta}(S_{j}^{i}|p_{1 }^{i},\ldots,p_{k}^{i},S_{1}^{i},\ldots,S_{j-1}^{i}). \tag{5}\]  
In bridging the gap from the initial phase of training our model on prefix-caption sequences to introducing the innovative SegmentCLIP image encoding mechanism, it is pivotal to understand the progression towards a more intricate engagement with visual data. The transition from focusing solely on linguistic elements to incorporating a sophisticated visual understanding marks a significant expansion in our model's capabilities. As the mapping component learns to predict caption tokens more effectively through cross-entropy loss, it sets the stage for a deeper exploration into the visual domain. SegmentCLIP emerges  
Figure 1: Framework of UVLS.  
as a natural progression in this journey, aiming to harness the full spectrum of visual information. By encoding images into a structured hierarchical format, SegmentCLIP enables the model to capture and interpret complex visual cues with unprecedented detail. This methodology not only enhances the model's ability to generate contextually rich captions but also fosters a more profound integration of linguistic and visual semantics. As we delve into the architecture of SegmentCLIP, it becomes evident how this mechanism plays a crucial role in transcending traditional boundaries between visual perception and language generation, ultimately leading to a more nuanced and comprehensive model.
## 6 Conclusions  
In conclusion, a psychophysical experiment is conducted to investigate the visual clarity of different text-background lightness combinations by using a head-up display under a dark surround condition and a bright environment in the study. A total of 190 pairs were tested to find the best visual clarity of different text-background lightness combinations among 13 observers.  
From the experimental results, the combination of the white text with a black background (i.e., \(L^{*}\)background = 1.88; \(L^{*}\)text = 100) presents the clearest under the dark surround. On the other hand, the black text with white background (\(L^{*}\)background = 100; \(L^{*}\)text = 96.19) is the clearest text-background combination under the bright environment (9000 lx). Furthermore, the judgments of visual clarity under the dark surround show that the combinations of text with a white background provide less visual clarity than those with a black background. Additionally, the observers generally consider that combining with a white background is distinct from those with other backgrounds luminance for 9000 lx. Furthermore, a third-order polynomial regression model is developed based on the visual data, with a high correlation of determination.  
When using a head-up display, appropriate lightness combinations for the text and background can improve visual clarity and reduce the time it takes for drivers to accommodate and identify content information. It can ultimately enhance driving safety. The findings of this work can be regarded as guidelines for the head-up display design to enhance drivers' visual perception and safety, depending on the ambient illuminance level.  
H.-P.H., concept provider, project administration, conducting experiments, data analysis, and writing--manuscript preparation. H.-C.L., data analysis, modeling, and writing--manuscript preparation. M.W., concept provider, supervision, and writing--review and editing. G.-H.L. conducting experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council (NSTC), grant number NSTC 110-2410-H-263-003.  
This study is exempt from review by the Institutional Review Board (IRB) of National Taiwan University (IRB No: 202105 ES131).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available upon request from the authors.  
The authors declare no conflict of interest.  
Figure 7: Scatter plots of prediction for visual clarity.
## 2 Materials and Methods
### Cardiopulmonary Exercise Test (CPET)  
To assess cardiopulmonary fitness, a cardiopulmonary exercise test (CPET) on a cycle ergometer was performed 4 days before and after the intervention. All subjects underwent exercise with a facemask fixed on the face to measure minute ventilation (V\({}_{\text{E}}\)), oxygen consumption (VO2), and carbonic dioxide production (VCO2) breath by breath using a computer-based system (Master Screen CPX, CareFusion, Hochberg, Germany). After 5 min baseline resting period records, a 2 min warm-up period (60 rpm, unloaded pedaling) was started, followed by incremental work (30 W elevation for each 3 min) until exhaustion (i.e., progressive exercise to maximal O2 consumption, VO2 max). The criteria used to define VO2 max and the ventilation threshold were consistent with the guidelines of the American College of Sports Medicine for exercise testing [22]. During the CPET, continuous monitoring of 12-lead electrocardiography, blood pressure, and pulse oxygen saturation was performed. Oxygen saturation (SpO2) was monitored by finger pulse oximetry (BCI 3301, Smiths Medical PM, Inc., Waukesha, WI, USA); the blood lactate concentration was determined using an i-STAT clinical analyzer (i-STAT analyzer, Abbott, Chicago, IL, USA).
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis
### Outage Probabilities Analysis  
To determine the likelihood that a data stream may have an outage event where the achievable rate is lower than the encoded data rate [15], we examine the outage probability where the UE\({}_{1}\) can detect \(x_{2}\) and \(x_{1}\). Given the previous definition, the outage probability (\(P_{1}\)) of UE\({}_{1}\) with the threshold SNRs of CR-NOMA users UE\({}_{1}\) and UE\({}_{2}\) (denoting \(\Omega_{1}\), \(\Omega_{2}\), respectively) may be described in [109] as  
\[P_{1}=\left[1-P_{r}(\xi_{\rm RS}\geq\xi^{\prime}_{\Omega_{2}},\,\xi_{\rm RS} \geq\xi^{\prime}_{\Omega_{1}})\right]\times\left[1-P_{r}(\xi_{l,2}^{X}\geq\xi^ {\prime}_{\Omega_{2}},\,\xi_{1}^{X}\geq\xi^{\prime}_{\Omega_{1}})\right] \tag{30}\]  
where \(\xi^{\prime}_{\Omega_{1}}=2^{2\tilde{\mathcal{R}}_{1}}-1\) and \(\xi^{\prime}_{\Omega_{2}}=2^{2\tilde{\mathcal{R}}_{2}}-1\) are the decoding threshold with \(\tilde{\mathcal{R}}_{1}\) and \(\tilde{\mathcal{R}}_{2}\) being the target rate of UE\({}_{1}\) and UE\({}_{2}\), respectively. \(\xi_{\rm RS}\) is the SINR between TX and RS, \(\xi_{\rm RS}\) is the decoding threshold of the direct transmission to the RS. \(\xi_{1}^{X}\) and \(\xi_{2}^{X}\) are the SINR defined at (20), (21), (26) and (27), where \(X\) in the equation denotes AF or DF coding scheme. Similarly, the outage probability (\(P_{2}\)) of UE\({}_{2}\) is  
\[P_{2}=P_{r}(\xi_{\rm RS}<\xi_{\Omega_{2}}^{\prime},\,\,\,\xi_{2}^{X}<\xi_{\Omega _{2}}^{\prime}) \tag{31}\]
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 2 System Model  
A multi-cell network with a set of UEs, M ={\(m_{1},~{}m_{2}\cdots m_{m}\)}, which need to send their data to the core network, was considered. The set of all the NR base stations (BSs) was denoted by L ={\(l_{1},~{}l_{2},\cdots l_{l}\)}, and the set of all the WLAN access points (APs) was denoted by W ={\(w_{1},~{}w_{2},\cdots w_{w}\)}. Each UE could accommodate two parallel links, one to the NR network and another to the WLAN. Additionally, since WLAN cannot exist independently (without association with the NR network) in our model, a UE maintains its connection to the NR network through its first link, which is referred to as the dedicated link, and ensures the transmission of its control commands. The NR network chosen by each UE as its dedicated link is then introduced in the next module. On its second link, referred to as the adaptive link, the UE may choose the WLAN AP to send data to the core network. The WLAN chosen by each UE as its adaptive link is the one from which the strongest pilot signal is received on a downlink control channel. All the links in the network (i.e., UE to NR and UE to WLAN) can allocate their transmit power and, consequently, their data rate. Considering the available power vector of the UEs as \(P_{\text{max}}=[~{}P_{\text{max},1},\cdots P_{\text{max},m}]\) (unit: W), the transmit powers of the UEs, with respect to their dedicated and adaptive links, are represented by the vectors \(P_{l}=\left[P_{1}^{(l)},P_{2}^{(l)}\cdots P_{m}^{(l)}\right]\) and \(P_{w}=\left[P_{1}^{(w)},P_{2}^{(w)}\cdots P_{m}^{(w)}\right]\), respectively, where \(P_{i}^{(l)}+P_{i}^{(w)}\leq P_{\text{max},i}\).
## 2 Sound Sampling and Data Transmission
### Node Implementations
#### 2.2.4 Cloud Server  
The cloud server is designed to control the entire system, as well as store and visualize data such as A-weighted SPL information, classification results, and network topology. Figure 5 illustrates the cloud server's workflow hosted on a workstation at the University of Auckland. The server comprises four key components: controller, web server, database, and user interfaces. The controller orchestrates interactions between the web server, database, and user interface for seamless operation. The web server, built on the lightweight Flask framework [30], enables the Jetson Nano to upload data and retrieve settings via predefined URLs periodically. Parsed data are stored in an SQLite database, which is chosen for its simplicity, low resource usage, and fast performance [31]. Users can access a data visualization interface created using a Python library, \"folium\" [32], to view statistical insights, including node locations, network topology, packet statistics, A-weighted SPL curves, and sound category distributions, providing comprehensive information on network performance and environmental sound levels.
# An Overview of Key Technologies in Physical Layer Security  
[PERSON],*  
1 Electrical, Computer, and Telecommunications Engineering, Botswana International University of Science and Technology, Palapye, Botswana; [EMAIL_ADDRESS] (G.A.M.); [EMAIL_ADDRESS] (T.M.L.J.); [EMAIL_ADDRESS] (B.B.); [EMAIL_ADDRESS] (J.M.C.)  
2 Center for Wireless Communications, University of Oulu, 90570 Oulu, Finland; [EMAIL_ADDRESS] (PLS) has been identified as one of the promising security approaches to safeguard the transmission from eavesdroppers in a wireless network. It is an alternative to the computationally demanding and complex cryptographic algorithms and techniques. PLS has continually received exponential research interest owing to the possibility of exploiting the characteristics of the wireless channel. One of the main characteristics includes the random nature of the transmission channel. The aforesaid nature makes it possible for confidential and authentic signal transmission between the sender and the receiver in the physical layer. We start by introducing the basic theories of PLS, including the wiretap channel, information-theoretic security, and a brief discussion of the cryptography security technique. Furthermore, an overview of multiple-input multiple-output (MIMO) communication is provided. The main focus of our review is based on the existing key-less PLS optimization techniques, their limitations, and challenges. The paper also looks into the promising key research areas in addressing these shortfalls. Lastly, a comprehensive overview of some of the recent PLS research in 5G and 6G technologies of wireless communication networks is provided.  
artificial noise; beamforming; intelligent reflective surface; MIMO; optimization; physical layer security; zero forcing
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 2 Materials and Methods  
In this study, three parameters were initially considered for the performance evaluation of the CO\({}_{2}\)-IPDA: the systemic error, random error, and spatial coverage. The major product of CO\({}_{2}\)-IPDA is XCO\({}_{2}\). However, the XCO\({}_{2}\) data provided by this product is subsequently used to invert carbon fluxes to reveal the spatial-temporal distribution of carbon sinks and sources. Errors that vary geographically would be detrimental for these inversions. Additionally, the coverage of XCO\({}_{2}\) determines where scientists can retrieve reliable carbon fluxes. In general, the systematic error remains constant globally. Moreover, systematic errors depend strongly on the technological level of the hardware systems. There is almost no correlation between the systematic error and energy consumption or volume occupation of the satellite. Therefore, systematic errors are not a major concern of this work. The coverage could be a critical advantage of IPDA over passive sensors, and is related to orbit height; consequently, this parameter is considered in this study. Finally, random errors are the primary focus of this work because they vary geographically and are strongly related to the design parameters of the CO\({}_{2}\)-IDPA.  
The performance evaluation requires consideration of the interactive relationship between the sensor, environmental, and product models. Hence, all three are involved in this work, as shown in Figure 1. Parameters for the sensor and product models were chosen according to the configuration of China's planned CO\({}_{2}\)-IPDA. Parameters for the environmental model were derived from remotely sensed data. In this section, we demonstrate the methodologies used to simulate random errors, systematic errors, and orbit sampling in this work, as well as the values of relevant parameters used in the simulations.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 3 System Model and Threat Model
### Threat Model  
In this paper, we adopt the universally accepted Dolev-Yao (DY) threat model [30] for security analysis of the proposed scheme. In accordance with the DY model, an attacker is able to seize transmitted messages through an open channel, and eavesdrop, delete, inject or modify on the seized messages.  
* The attacker has full control over and learns from messages sent over open channels. The attacker can then insert, modify, or remove valid messages.
* Because guessing more than one value at a time is a \"computationally infeasible operation\", the attacker can only guess one value in polynomial time. In addition, this paper additionally adopts the assumptions of the \"Canetti and Krawczyk model (CK model)\" [31]. It is a more powerful threat model compared to the DY model and is considered the de facto standard for modeling key exchange protocols.
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
## 2 Construction of Mathematical Models
### Modeling Based on Joint Allocation System
\begin{split}&(\mathcal{P}_{1})\underset{\boldsymbol{u}, \boldsymbol{p}}{\max}\;\text{SNR}_{\text{s}}(\boldsymbol{u},\boldsymbol{p})\\ &\text{s.t.}\left\{\begin{array}{ll}\sum\limits_{k=1}^{K}p_{k} \leq P&(12\;.a)\\ \xi\leq p_{k}\leq\frac{\eta P}{K}&(12\;.b)\\ \sum\limits_{k=1}^{K}\text{log}_{2}\bigg{(}1+\frac{|\alpha_{k}|_{2}(1-u_{n})p_{ k}}{\sigma_{\text{c}}^{2}}\bigg{)}\geq\kappa\;|\boldsymbol{u}|_{1}=\kappa\sum \limits_{k=1}^{K}u_{k}&(12\;.c)\\ u_{k}\in\{0,1\}&(12\;.d)\end{array}\right.\end{split}  \tag{12}\]  
where \(\xi\) represents the power allocation constraint, which is imposed to prevent all power from being allocated solely to the subcarriers with the highest SNR and ensure that subcarriers with the lowest SNR do not receive zero power [33], and \(|\boldsymbol{u}|_{1}\) indicates the number of subcarriers used for SAR. The lower bound of the data transmission capacity needs to match the data returned by \(|\boldsymbol{u}|_{1}\) radar subcarriers.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 4 Design of the PAUM System  
This section characterizes previously discussed research challenges into achievable clarification. Addressing proper access among UEs to communicate with eNB, the events classification model opens up the idea of handling the access based on the priorities of different events.  
The block diagram in Figure 2 illustrates a detailed understanding of our proposed interdependent event influenced prioritized data transmission access scheduling. The following subsections will delineate the events classification and dispensation of proposed algorithms. Preparing a scheduling of UEs tends to mitigate any mutual interference. Maximizing user utility through the optimal allocation of bandwidth resources has been depicted in the latter part of this section.
## 4 Discussion  
### Discussion of the Influencing Mechanism of Frost Heave on Vehicle-Track Coupling System  
When subgrade frost heave occurs, the track structure is lifted by the crest of the frost heave, and the whole system works like a lever, and the dynamic process of the vehicle-track system in the frost heave zone is summarized in Figure 16.  
When the vehicle is passing through the affected area, the track irregularity influences the dynamic response first; the influence of the track irregularity can be derived from the results of Model-A. When the wheel load passes by the contact loss area (if it exists), it causes the interlayer space to close, and the adjacent longitudinal structures exhibit an upturned compatible deformation due to the structure bending stiffness. This can be seen as a kind of leverage effect. Besides this, the initial bending condition caused by frost heave deformation and the weak vertical constraint of the slab track amplify this effect, which also decreases the stability of the slab track and increases the dynamic irregularity.  
In summary, the influencing mechanism of frost heave on the vehicle-track coupling system can be separated into three parts:  
1. Track irregularity, which is responsible for the changes in the dynamic response and for determining the length of the affected area of wheel-rail force.
2. Contact loss area underneath the structure, which may change the dynamic irregularity (increasing or decreasing for different wheelsets) and increase the vibration of the slab track;
3. Leverage effect of slab track caused by the constraint condition and the bending stiffness itself, which further increases the short-wavelength dynamic irregularity.  
Figure 16: Influencing mechanism of frost heave on vehicle-track coupling system.  
Figure 15: Time history of vertical acceleration of car body force with different models.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 3 Methodology  
### Timing Optimization Model  
#### 3.1.1 Decision Variables  
As described in the problem formulation in Section 2, the decision variable in this paper is the time \(t_{ij}^{+}\) when the CAVs arrive at the conflict zone of the intersection, where \(i\in\mathcal{I},j\in\mathcal{N}_{i}\). Through analysis, it can be inferred that this decision variable is a positive real number.
## 5 Performance Evaluation and Simulation Results  
### Simulation Setup  
The simulation parameters are shown in Table 3. In the simulation, it is assumed that the transmission delay of the 5G core network is 20 ms. The subcarrier spacing (SCS) of the 5G network is set as 15 kHz. The time slot length is set as 1 ms. The number of OFDM symbols in one time slot is 14 by default. The time length of RB is one time slot, that is, 1 ms. Assuming that QAM64 modulation is adopted, the amount of data carried by one RB is 126 bytes. The semi-persistent scheduling period is set as the minimum event interval of the ET flows, i.e., \(T_{S}\) = 50 ms.  
The simulation includes six types of traffic, including emergency demand response, fast frequency regulation, regular frequency regulation, normal demand response, peak regulation, and power trade market. The flow characteristic settings are based on Table 2. The number of reserved RBs is set based on the data size of three types of ET traffic, including fast frequency regulation, regular frequency regulation, and emergency demand response traffic.  
In VPP systems, the delay guarantee of ET flows is critical. The 5G system uses limited spectrum resources to transmit data. The simulation compares the performance of the proposed SPS-RBSP algorithm with the traditional static scheduling algorithm and the traditional dynamic scheduling algorithm in terms of end-to-end delay and system resource utilization.