esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
proposed a multi-agent dynamic spectrum resource allocation scheme based on the proximal policy optimization algorithm, which improved the channel transmission rate and the success rate of vehicle information transmission [19]. The PPO algorithm can be competent in multiple, continuous action, space environment decision-making regarding vehicle control, and its training stability, high robustness, and easy-to-adjust parameters are suitable for simulation research [20]. The present study proposes an innovative approach to address the issue of social vehicles dominating urban roads during the initial stage of autonomous vehicle implementation. Specifically, we introduce a coordinate convolution dynamic multi-objective reward mechanism into the proximal policy optimization (PPO) algorithm. Our research focuses on enhancing the interaction between autonomous vehicles and their surrounding environment at intersections by incorporating gap acceptance theory [21]. To achieve this, we integrate CoordConv network and coordinate convolutional network into our algorithm model to capture and store low-dimensional environmental information. By optimizing the dynamic multi-objective reward mechanism model, we aim to enhance safety, stability, and driving efficiency of autonomous vehicles. We select PPO algorithm for optimization purposes. In an unsignalized roundabout scenario, our proposed approach enables effective interaction between autonomous vehicles and other vehicles as well as traffic signs in the scene. We define a reward mechanism function for training purposes with the objective of establishing a decision model throughout various scenes and obtain an optimal decision strategy for autonomous vehicles.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 3 Analog Fronthaul Signal Processing  
The analog fronthaul links employed in the C-RAN system under study pose several challenges in the system design, which are addressed in this section. Firstly, the radio signal received at each EN needs to be mapped over the corresponding fronthaul resources in both frequency and space dimension. Secondly, the signal at the output of each fronthaul link needs to be processed in order to maximize the Signal-to-Noise Ratio (SNR) for all UE signals. Finally, the power constraints in (11) must be properly enforced. All these requirements are to be addressed by all-analog processing in order to meet the low-complexity and latency constraints of the analog fronthaul. In the rest of this section, we discuss each of these problems in turn.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
###### Abstract  
Pipe-jacking is a construction method widely used in pipeline constructions. Prediction of ground deformation induced by pipe-jacking, is important for safety and scheduling purposes. This paper presents an approach to predict ground deformation during pipe-jacking considering following factors: (i) bulkhead additive thrust; (ii) friction on jacking machine; (iii) grouting pressure; and (iv) ground loss. [PERSON]'s solution was used to calculate the ground deformation induced by bulkhead additive thrust and friction on the jacking machine. The shearing disturbance coefficient was adopted to evaluate the mitigation effect of shearing behavior on ground deformation. [PERSON]'s solution was used to simulate the effect of grouting pressure. [PERSON]'s method was adopted to consider the ground loss induced by over-excavation. Subsequently, a three-dimensional analytical solution for ground deformation induced by pipe-jacking was obtained. A case study based on a pipe-jacking project undertaken in Jiangsu, China was analyzed to validate the proposed approach. The results indicated that the proposed approach was robust and could be implemented for future pipe-jacking projects.  
pipe-jacking; ground deformation; prediction method; grouting pressure Article
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 3 Datasets
### Liver Tumor Segmentation (LiTS)  
LiTS is a benchmark dataset designed to evaluate segmentation methods for detecting liver/liver tumors in medical imaging. It is known for being challenging due to the complexity of accurately identifying both the liver and tumors within CT scans. It includes 201 abdominal computed tomography (CT) images, with 194 of these scans showing lesions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Authors** & **Method** & **Purpose and Used Data** & **Evaluation Metrics** \\ \hline
2023 & [PERSON] et al. [29] & Pix2 Pix GAN &
\begin{tabular}{c} Decision support for liver trauma \\ triage; total patients: 20, total \\ images: 2823, liver masks: 1153, \\ tumors or cysts: 467 \\ \end{tabular} & Dice scores of 97\% for liver, 93\% for lcreations \\ \hline
2024 & [PERSON] et al. [30] & 3D CBAMNN & \
\begin{tabular}{c} Detection of traumatic abdominal \\ injuries; 1302 scans (87\%) for \\ training and validation and 194 \\ scans (13\%) for testing \\ \end{tabular} &
\begin{tabular}{c} Spleen injury model accuracy of 0.938 \\ and specificity of 0.952; liver injury \\ mode accuracy of 0.820 and specificity \\ of 0.847; kidney injuries model accuracy \\ of 0.959 and specificity of 0.989 \\ \end{tabular} \\ \hline
2024 & [PERSON] and [PERSON] [31] & MSLUNet & \
\begin{tabular}{c} Enhanced medical image \\ segmentation; BUSI dataset: total \\ images: 780, normal breast: 133 \\ benign tumor: 437 malignant tumor: 210 used 647 (benign + malignant \\ tumor); Kvasir-SEG dataset: 1000 \\ images + 1000 masks \\ \end{tabular} &
\begin{tabular}{c} Evaluation metrics on MSLUNet are \\ Dice, recall, precision, and specificity \\ of 91.1, 93.2, 95.6, and 94.9, \\ respectively \\ \end{tabular} \\ \hline
2024 & [PERSON] et al. [32] & 2D Semantic Segmentation &
\begin{tabular}{c} Abdominal injury detection; 855 out \\ of 3147 patients had confirmed \\ abdominal trauma. \\ \end{tabular} & Accuracy of 93.2\% in renal injuries \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The dataset includes 131 training samples, each annotated with segmentation masks that outline the liver/any tumors present [35].
## 5 Negation and Speculation Detection  
The deep learning approach described previously has been able to extract cancer-related entities. However, many of these entities can be affected by negation or speculation. Detecting speculation and negation is a crucial step to extract a cancer diagnosis correctly. This section shows a rule-based approach to detect negation and speculation in clinical texts written in Spanish. This task is commonly divided into two subtasks: _Cue identification_ and _Scope recognition_. Cues are words or terms that express negation (e.g., not, nothing, negative) or speculation (e.g., possible, probable, suggest) [13]. The scope is the text fragment affected by a cue in a sentence [50]. In the next example, the cue is shown in bold, and the scope is underlined.  
_\"Patient with **possible** lung cancer in July 2014, we recommend a chest test to confirm.\"_.  
Given a text sentence, the cues will first be detected, and later the tokens affected by these cues will be found. We propose to enrich this process by improving the lexicon used with popular medical cues. Besides, an analysis of the sentences in which negation or speculated terms appear is performed to improve scope recognition.  
The main motivation to enrich the negation and speculation detection is that clinical records are written by highly skilled physicians and nurses using domain-specific terms. These records are written under time pressure, the text is short and efficient, and written in telegraphic style [51]. Consequently, clinical texts can contain sentences shorter than those found in other domains, and the lexicon to express speculation and negation can be richer and more complex.  
Taking into account the above facts, this approach is composed of the following steps: (i) developing a cue lexicon focused on the medical domain, (ii) defining regular expressions for detecting cues in clinical text, (iii) analyzing the sentences where the cues are detected and finally, (iV) recognizing the scope for each detected cue.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion
### Comparison with Existing Positioning Techniques
#### 5.2.6 Photogrammetry and Visual-Based Positioning  
The positioning based on monocular images aims the mapping of conductors in a transmission grid. In addition, optical aerial images can be used to monitor the vegetation, to inspect power line components and to map electrical towers. This method stands as a promising candidate to position aerial platforms in transmission grids due to its flexibility in data acquisition, low cost, and noise immunity. However, several factors can affect image quality. In addition, the lighting conditions are an important restriction (cannot be used in dark conditions). The 3D modeling of conductors is extremely difficult and depends on weather conditions.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.6 Performance of Blockchain Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
Overall, permissioned blockchain infrastructures provide more holistic and balanced solutions to the key requirements of IoE systems, such as security, transaction capacity, and energy efficiency. The DAG-based structure of IOTA eliminates the need for blocks and miners, enabling a parallel transaction confirmation architecture where transactions validate each other directly. This offers significant advantages such as low latency, high transaction throughput, and near-zero transaction costs. However, it also shows limitations in access control, data privacy, and enterprise governance. In contrast, Hyperledger Fabric, with its permissioned structure, offers strong access management, a customizable modular architecture, and high scalability, making it particularly suitable for real-time IoE applications where security and privacy are critical. Therefore, building the system architecture on permissioned platforms such as Hyperledger Fabric emerges as a more rational choice in terms of both technical capability and operational sustainability [92; 93; 94; 95; 96; 97].
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 5 Experiments
### Detectability  
If a warden would be capable of observing the originally generated as well as the transmitted hash values, it could compare both values. We assume that such a scenario is rather unlikely but it is of theoretical interest in order to determine the detectability of our channels.  
We used the same covert channel as before, but the message comprised \(t=16\),382 letters \"A\" plus the termination symbol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**OTP Hash Function** & **Without CC** & **With CC** & **With CC and Encrypt.** \\ \hline MD5 & 0.019 s & 0.350 s & 0.349 s \\ SHA-3 & 0.386 s & 6.737 s & 6.803 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Runtime results of performance test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**OTP Hash Function** & **Without CC** & **With CC** & **With CC and Encrypt.** \\ \hline MD5 & 0.8212000 & 0.7555420 & 0.8038680 \\ SHA-3 & 0.6928455 & 0.6198775 & 0.6928455 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(p\)-values from NIST cumulative sums test, averaged between forward and reverse tests.  
As shown in Figure 2, the differences between the covert channel without encryption (denoted as _simple_) and the legitimate hash values are only visually recognizable if a suitable visualization method (in our case: differences modulo 8) is selected (in large hash values of e.g., 512 bit size, a (randomized) modification of the least significant bits would be invisible on charts that cover the whole range of hash values). The encrypted covert channel's values (denoted as _improved_) match those of the legitimate traffic.  
However, both covert channels' hash values are barely distinguishable from legitimate values if the original traffic is not available. Several hash values of both channels are either close to (and a few matching) the legitimate values (_simple_ channel) or partially close and mostly matching (_improved_ channel) while following a pseudo-random distribution typical for hash values. In our tests, approx. 6.7% of the modulo results of the _improved_ channel did not match those of the legitimate traffic while all other values matched exactly.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
Figure 4: WISCANet emulates real-time operations by executing a single RF frame, freezing all nodes’ transmit and receive operations, performing baseband processing in MATLAB (or other compatible language), and resuming all RF operations on the edge nodes at the same time for the next frame. From the radios’ perspectives, this application looks real-time; but the extra processing time allows users to test minimally-viable code without needing to optimize and accelerate it (traditionally a tedious and time-consuming process). This also allows WISCANet to support languages like MATLAB that are traditionally slower than more optimal languages.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 3 Methodology
### Proposed Model Design
#### 3.1.1 Ensemble Empirical Mode Decomposition
Then, after the EEMD decomposition process, the original signal \(x_{m}(t)\) can be mathematically expressed as the sum total of each of the IMFs \(C_{1}(t)\) components and the residue \(R_{1}(t)\). Hence,  
\[x_{m}(t)=\sum_{i=1}^{n}C_{i}(t)+R_{1}(t) \tag{4}\]  
where \(n\) and \(C_{i}(t)\) denote the total number of the IMFs \(C_{1}(t)\) components and the \(i^{th}\) IMF, respectively; and \(R_{1}(t)\) represents the final residue. Stage 8: By adding a different noise in each trial, repeatedly execute Stage 2 to Stage 7 until \(m=M\) if \(m<M\), through a consecutive increment of the value of m by using \(m=m+1\). Stage 9: Determine the \(i^{th}\) ensemble mean \(\overline{C_{i}}\) of the M trials for individual IMF, by way of expression,  
\[\overline{C_{i}}=\frac{1}{M}\sum_{m=1}^{M}C_{i},m\hskip 28.452756 pti=1,\;2,\; 3,\;\cdots,\;n \tag{5}\]  
and the ensemble residue \(\overline{R}_{n}\) can be expressed as  
\[\overline{R}_{n}=\frac{1}{M}\sum_{m=1}^{M}R_{n},m. \tag{6}\]  
Therefore, the original DO time series data are efficiently decomposed through EEMD method into \(n\) ensemble IMFs and a single ensemble residue. In each frequency band, the contained IMF components are individually different, and can change with the variation of the DO dataset \(x(t)\). Additionally, the ensemble residue denotes the general trend of the DO dataset \(x(t)\).
## 3 Results and Discussion
### XRD Analysis  
In combination with the results of the macroscopic performance tests described above, five doses were selected from the three retarders that were overall superior (0.3% \(\beta\)-CD, 0.3% CA, 0.5% B, 0.3% \(\beta\)-CD + 0.3% CA, and 0.3% \(\beta\)-CD + 0.5% B); in order to further study the effect of different retarder and water-reducing agent compounding on the hydration products of HBCSA, the XRD analysis was carried out. Figure 13 shows the XRD patterns of different retarders and cement pastes hydrated for 1 d and 28 d, and ettringite AFt is the main hydration product. Figure 13a shows the XRD patterns of different retarders at an age of 1 d, and when the diffraction angle is about 8\({}^{\circ}\), the diffraction intensity of AFt is the highest. The components are all reduced to varying degrees, and there is also unreacted CS (diffraction angle of about 26\({}^{\circ}\)), dicalcium silicate (diffraction angle of about 32\({}^{\circ}\)), and AFm with lower diffraction peaks (diffraction angle of about 10\({}^{\circ}\)). The samples mixed with 0.3% CA have a higher diffraction intensity of gypsum, indicating that CA has a strong inhibitory ability on cement hydration. However, the diffraction peaks of ettringite of the samples mixed with 0.3% \(\beta\)-CD and 0.5% B are more obvious than those of CA, and the diffraction peaks of gypsum are also reduced, which also shows that the two retarders have little effect on the hydration rate of cement. When the three retarders are compounded in pairs, the diffraction peaks of the hydrated product ettring are weaker than those of single-doped \(\beta\)-CD and B, but compared with single-doped CA, when \(\beta\)-CD and CA are compounded, the diffraction peaks of ettringite are somewhat stronger. With the increase in the hydration time, when the hydration age reaches 28 d, gypsum and ye'elimite are further consumed, and ettringite is continuously formed, the position and shape of its characteristic diffraction peaks have not changed. Also, the diffraction peaks of CS and C\({}_{2}\)S have not changed. The hydration degrees of the blank group and the blended retarder were  
Figure 11: Damage interface of cement mortar test blocks at 3 d: (**a**) Blank and (**b**) 0.3% \(\beta\)-CD (yellow circles represent pores and loose parts).  
Figure 12: Strength of cement mortar test blocks with different retarder components at various ages: (**a**) flexural strength and (**b**) compressive strength.  
basically the same, and there was no significant difference in the diffraction peak intensity of ettrinigite. It can also be seen that the intensity and position of the diffraction peaks of AFm and C\({}_{2}\)S did not change at 1d and 28d, and in different retarders. To sum up, the addition of a retarder significantly weakens the hydration of cement but does not change the types of HBCSA cement hydration products.
## 2 Blue Shift in HHG
### Blue Shift by Propagating Effect  
Free electrons created during the laser pulse induce a change of the refractive index [13],  
\[\delta n_{{}_{e}}(r,z,t)=-\frac{2\pi}{\omega^{2}}\,N_{e}(r,z,t) \tag{2}\]  
where \(N_{e}(r,z,t)\) denotes the electron density. The variation of the refractive index leads to a blue shift of the fundamental laser field. The shift can be estimated by the following expression (CGS units) [18],  
\[\delta\lambda=-\frac{\lambda^{3}e^{2}L}{2\pi mc^{3}}\,\frac{dN_{e}}{dt} \tag{3}\]  
where \(L\) is the propagation length. For HHG with order \(n\), the total blue shift comes from two contributions: a shift \(\delta\lambda_{{}_{1}}\) induced by the shift of the fundamental field and the other shift \(\delta\lambda_{{}_{2}}\) caused by the generated harmonics propagating in the medium. \(\delta\lambda_{{}_{1}}\) is proportional \(\delta\lambda/n\), while \(\delta\lambda_{{}_{2}}\) is propotional to \(\delta\lambda/n^{3}\).
## 3 Creation of the Prototype and Establishment of the Dynamic and Static Testing Systems
### Construction of the Dynamically Varying Pressure Testing System  
The dynamically varying pressure testing system utilizes a peristaltic pump driven by a stepper motor, Luer connectors, Luer three-way valves, and pressure regulation valves to build an experimental system for dynamic measurement accuracy testing, as shown in Figure 8. The peristaltic pump has a three-lobe rotor structure, where the rotor squeezing the tube mimics the contraction of the heart, and the rotor's release from the chamber mimics the heart's relaxation, analogous to the human heart beating process.  
The dynamically varying pressure testing system simulates the internal fluid circulation process of a living body through a closed-loop piping system. The water flow is driven by a stepper motor peristaltic pump, and the control module can precisely control the motor's speed via switch signals, thereby precisely controlling the pump's pulsation frequency, flow rate, and the direction of fluid flow. A high-precision standard pressure sensor is placed at the outlet of the peristaltic pump, and since it is close to the pressure source, its output curve can be considered the standard pressure curve within the tubing.  
Figure 8: Schematic diagram of the dynamically varying pressure testing system.  
Figure 7: Pressure adjustment time.  
On the other side, a resistance regulation valve is used to adjust the overall system's fluid pressure. In this experiment, the external part's length and diameter of the catheter body are variables to explore the impact of the external structure of the catheter body on the dynamic measurement accuracy of the pressure measurement catheter. We added a Luer connector to the tail of the original pressure measurement catheter body, allowing for the replacement of different external part sizes without changing the size of the internal part of the catheter body. Figure 9 shows the physical photo of the dynamic system.
# Construction Progress and Aviation Flight Test of BDSBAS
## 4 BDSBAS Aviation Flight Test  
As with other SBASs, BDSBAS must be certified by the civil aviation department in order to serve civil aviation users. Therefore, an aviation flight test is one of the necessary tasks to verify the SF service performance of BDSBAS meeting the APV-I requirements (the HAL and VAL of which are 40 m and 50 m, respectively). General aviation aircraft have characteristics such as low cost and high flexibility. Using them to carry out BDSBAS preliminary service performance testing can provide experience and reference for subsequent cooperation with the civil aviation department to carry out formal testing and airworthiness certification.