esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Improved Backbone with Vision Transformer
#### 3.2.2 Transformer Encoder Block  
The self-attention mechanism transforms a single feature into another through capturing long-range dependencies across each input via taking \(N\) instances with no contextual information and then returning \(N\) entities with contextual information. In other terms, it accepts inputs in the manner of \([X_{1},X_{2},X_{3},\ldots\ldots.X_{N}]\) by \(x\ \in\ r^{n\times d}\), and further employs the learnable weighted matrices that are queries \(w^{Q}\in r^{D}\times D_{Q}\), keys \(w^{K}\in D\times D_{K}\), and values \(w^{v}\in D\times D_{K}\). Evidently, through combining each value with weights after measuring the query across all keys, the equation below represents the self-attention output.  
\[attention(q,k,v)=\stackrel{{\rightarrow}}{{z}}=SoftMax(\frac{q \cdot k^{t}}{\sqrt{D_{Q}}})v \tag{3}\]  
upon which, \(\stackrel{{\rightarrow}}{{z}}\in r^{N\times D}\) along with SoftMax to achieve the attention level having \(v=xw^{v}\), \(q=xw^{q}\), and \(k=xw^{k}\) using the dot product calculation. Relying on adopting a self-attention layer, the vision transformer applies multi-head self-attention. Where eight headers are often used to streamline various complex connections among different components in a series and handle longer-term dependencies, this corresponds to the aggregated multiple self-attention that is independent of parameters \(w_{i}^{q}\), \(w_{i}^{k}\), and \(w_{i}^{v}\) and possesses similar input, where \(I=0,\ldots.(H-1)\), and \(H\) is the overall length of attention blocks, respectively.  
\[multihead(q,k,v)=concat(HEAD_{1},\ldots\ldots,\ HEAD_{H})\ w \tag{4}\]  
while \(HEAD_{1}=attention(vw^{v},\ qw_{i}^{q}\), and \(kw_{i}^{q}\)), and outcomes are combined to a single matrix, \([c_{0},c_{1},\ldots c_{H-1}]\in r^{H\cdot D\times D_{K}}\).  
Multilayer perception layers (MLPs) in the transformer encoder block have enabled our model to narrow its attention to the relevant features while minimizing the number of parameters after integrating them into the final layer inside the feature extraction stage. The dimensions of the input image along with extracted features and the output can be seen in Figure 2, where \(640\times 640\times 3\) represents the size of the input, and once the input image is converted into a feature map, its dimensions change to \(20\times 20\times 512\). As a result, the transformer encoder block input size is \(20\times 20\times 512\). The feature map's size is \(400\times 512\) (length \(\times\) channel) using patch embedding, which applies a simple additive operation through a learnable vector. Therefore, transformer encoder block input vectors and output vectors are of the same size.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 4 Simulation Model  
We built our simulator using OMNET++ simulation framework [35] from the model developed by [36], designed for sensor networks based on the IEEE 802.15.4 standard, due to the similarities that exist between the architectures of IEEE 802.15.7 and IEEE 802.15.4 standards.
## 3 Methodology and Data Description
### Data Description  
This section outlines the key data used in the study, including road infrastructure, vehicle types, traffic density, and environmental factors. These elements provide the basis for building a realistic simulation model.  
This study relies on comprehensive datasets covering road infrastructure, vehicle types, traffic density, and environmental factors to develop a realistic simulation model. The data includes historical port records, transportation network specifications, and traffic flow details, which form the foundation for accurate modeling.  
[PERSON] processes utilize data such as historical cargo arrival and departure times, delays due to equipment breakdowns, adverse weather, and congestion. Transition probabilities are derived from these records to quantify the likelihood of moving between states such as \"Waiting at Port\", \"In Transit\", \"Delayed\", and \"Delivered\". These probabilities enable the construction of a transition matrix, capturing uncertainties in cargo movement.  
To optimize the transportation network using max-flow models, detailed maps of roads and railways linking Dar es Salaam Port to inland destinations are critical. These include information on connectivity, accessibility, and capacity data, which specify the maximum cargo volume or vehicle flow each segment can handle. We obtained the origin-destination (O-D) matrix by analyzing historical cargo throughput data and transport records. These data were used to estimate the flow of cargo from Dar es Salaam Port to various inland destinations, considering factors such as cargo volumes, delivery schedules, and peak demand periods. The matrix reflects the flow of goods between the port and its hinterland, enabling accurate modeling of transportation logistics. Additionally, demand data, such as cargo volumes, delivery time windows, and fluctuations during peak periods, ensure realistic network modeling  
Traffic flow modeling requires data on traffic volumes, including real-time vehicle counts, average speeds, and congestion patterns. Road characteristics, such as lane numbers, road types, speed limits, and the placement of intersections, provide essential context. Historical and real-time travel time data for routes connecting the port to hinterland locations are also incorporated, considering factors like congestion and road conditions. Together, these datasets support precise modeling of transportation efficiency and network optimization.
# Study on Mechanical Properties of Modified Polyurethane Concrete at Different Temperatures
## 2 Experimental Program  
### Test Specimens  
#### 2.1.1 Mix Design  
The main ingredients of modified polyurethane concrete adopted in the experiment were aggregates, modified polyurethane binder (multiple groups of thermosetting polymer materials) and admixtures. The admixtures, which were composed of toluene diisocyanate trimer and hexamethylene diisocyanate trimer, could promote the solidification of modified polyurethane concrete. Aggregates include coarse aggregate and fine aggregate rich in silica, which was mixed according to the maximum density curve. The coarse aggregate was continuously graded natural gravel and machine-made sand with round and flat particles and without acute angle. The fine aggregate was continuously graded natural river sand. All the raw materials required for the experiment were provided by Roaby company (Ningbo, China). The mix proportion of the concrete specimens was designed according to the strength grade of C60 (characteristic strength of 60 MPa). The sand-binder ratio, the ratio of modified polyurethane to aggregate in modified polyurethane concrete, obtained through a large number of experiments, was 15% to 17%. The mix proportions of the modified polyurethane concrete are shown in Table 1 and Figure 1.
## 2 Related Work
### IEEE 802.11 ax (Wi-Fi 6 / Wi-Fi 6E)  
The IEEE 802.11 ax standard represented a significant advance over its predecessors, offering improvements in both network performance and efficiency. These capabilities were later extended to the 6 GHz band, commercially known as Wi-Fi 6E, providing even more of the spectrum and reducing congestion.  
The incorporation of Orthogonal Frequency Division Multiple Access (OFDMA) technology allows a channel to be split into smaller sub-channels, each assigned to different devices, improving spectral efficiency and reducing latency in dense environments. This feature is particularly relevant for real-time industrial services and applications requiring ultra-low latency. Additionally, MU-MIMO is enhanced, allowing simultaneous communications with up to eight devices for both uplinks and downlinks. Another technique added to improve spectral efficiency is the use of Basic Service Set (BSS) colouring. This technique assigns different colours to different BSSs, enabling neighbouring networks operating on the same channels to reduce their interference. This standard also introduced new mechanisms such as Target Wake Time (TWT) to improve the energy efficiency of devices by allowing them to negotiate when and how often they wake up to send or receive data.  
With the advent of Wi-Fi 6E and the expansion to the 6 GHz spectrum, congestion in the 2.4 GHz and 5 GHz bands is reduced, providing more non-overlapping channels and improving overall Wi-Fi network performance and capabilities. By expanding the available spectrum, Wi-Fi 6E supports more devices connected simultaneously without performance degradation, ideal for industrial environments demanding high device densities and performance. By incorporating these techniques and technologies, Wi-Fi 6E paves the way for future innovations in wireless networking.
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Tumour Tissues
#### 5.1.3 Brain Glioma  
Brain cancers are common malignant diseases in neurosurgery, and brain glioma is the highest incidence brain cancer. The current treatment for glioma is a combination of surgical resection, radiotherapy, and chemotherapy. The effect and prognosis of the traditional treatment are not good, so the earlier the glioma is discovered, the more effective the treatment can be for the patients [86]. Correct boundary division between normal and cancerous tissues is important for surgery and the treatment of brain glioma. In 2019, [PERSON] et al. demonstrated the reflected CW THz imaging system that showed in Figure 2a with human brain cancer tissues in and ex vivo and visualised the cancer tissue areas clearly in both fresh resected and paraffin-embedded tumour sections [87]. Then, they utilised the same system to image the frozen sections of mouse brain cancer ex vivo and found good consistency with other traditional methods, including H&E staining, computed tomography, magnetic resonance imaging, and fluorescence imaging. The difference between normal and mouse cancer brain tissues is because of the increased water content in cancer tissues, which is caused by new blood vessels, body fluids through necrotic debris, and changes of the cell density [88]. The structure of normal brain tissues is complete, regular, and orderly. On the contrary, due to the infinite growth of malignant tumour cells, the structure of brain glioma tissues becomes deformed, dense, and irregular. Single-point scanning THz imaging technology can distinguish normal brain from cancer tissues by detecting the structure and shape of the glioma, providing a new tumour detection method as the basis for clinical application of detecting brain glioma disease.
# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 4 Experiment
### Ablation Study  
To thoroughly examine the impact of individual components within our proposed GC4 MRec model, as shown in Figure 6, a comprehensive ablation study was conducted across three distinct real-world datasets. This analysis aims to isolate and quantify the contributions of each component to the overall model performance. The following GC4 MRec variants were systematically evaluated to achieve this objective:  
* **GC4 MRec-GCN-ONLY:** This variant removes all modules from GC4 MRec, using only GCN. This helps analyze the impact of the layer-wise convolution approach. We can see that GC4 MRec always outperforms GC4 MRec-GCN-ONLY.  
* **GC4 MRec-NoModalityFusion:** This variant removes the multimodal fusion strategy from GC4 MRec and retains other modules. This assesses the impact of multimodal fusion on performance. The ablation shows that the performance drops by 3.5%, 3.5%, and 5.5% respectively, compared with GC4 MRec, indicating that capturing information from multiple modalities fusion is essential for accurate recommendations.  
* **GC4 MRec-NoGraphRefinement:** This variant ablates the graph refinement process. We observe a performance decrease of 3%, 3%, and 5.4% when graph refinement is removed, showing that refining the graph structure is crucial for improving recommendation accuracy.  
These ablation results demonstrate that each component of GC4 MRec contributes to its overall performance. Removing any of these components leads to a noticeable decrease in recommendation accuracy, validating the design choices and effectiveness of our proposed method.  
Figure 5: Performance comparison curves against a state-of-the-art model on the Baby database.
## 2 System Model  
Let us consider a uniform linear array (ULA) with \(M\) sensor elements, which receive \(K\) narrowband signals where \(K\leq M\). The directionsof arrival (DoAs) of the \(K\) signals are \(\theta_{0},\ldots\theta_{K-1}\). The received vector \(\mathbf{x}[i]\in\mathbb{C}^{M\times 1}\) at the \(i\)-th snapshot (time instant) can be modelled as:  
\[\mathbf{x}[i]=\mathbf{A}(\mathbf{\theta})\mathbf{s}[i]+\mathbf{n}[i],\qquad i=1,\ldots,N \tag{1}\]  
where \(\mathbf{\theta}=[\theta_{0},\ldots,\theta_{K-1}]^{T}\in\mathbb{R}^{K\times 1}\) convey the DoAs of the \(K\) signal sources. \(\mathbf{A}(\mathbf{\theta})=[\mathbf{a}(\theta_{0}),\ldots,\mathbf{a}(\theta_{K-1})]\in \mathbb{C}^{M\times K}\) comprises \(K\) steering vectors, which are given as:  
\[\mathbf{a}(\theta_{k})=[1,e^{-2\pi j\frac{\lambda}{\lambda_{c}}\cos(\theta_{k})},\ldots,e^{-2\pi j(M-1)\frac{\lambda}{\lambda_{c}}\cos(\theta_{k})}]^{T} \tag{2}\]where \(\lambda_{c}\) is the wavelength and \(\iota\) is the inter-element distance of the ULA. The \(K\) steering vectors \(\mathbf{a}\{\mathbf{\theta_{k}}\}\in\mathbb{C}^{M\times 1}\) are assumed to be linearly independent. The source data are modelled as \(\mathbf{s}\in\mathbb{C}^{K\times 1}\), and \(\mathbf{n}[i]\in\mathbb{C}^{M\times 1}\) is the noise vector, which is assumed to be zero-mean; \(N\) is assumed to be the observation size, and \([i]\) denotes the time instant. For full-rank processing, the adaptive beamformer output for the SoI is written as:  
\[y_{k}[i]=\mathbf{\omega}_{k}^{H}[i]\mathbf{x}[i] \tag{3}\]  
where the beamformer \(\mathbf{\omega}_{k}\in\mathbb{C}^{M\times 1}\) is derived according to a design criterion. The optimal weight vector is obtained by maximizing the signal-to-interference plus noise ratio (SINR) and:  
\[\text{SINR}_{\text{opt}}=\frac{\mathbf{\omega}_{\text{opt}}^{H}\mathbf{R}_{k}\mathbf{ \omega}_{\text{opt}}}{\mathbf{\omega}_{\text{opt}}^{H}\mathbf{R}_{i+n}\mathbf{\omega}_{ \text{opt}}} \tag{4}\]  
where \(\mathbf{R}_{k}\) and \(\mathbf{R}_{i+n}\) denote the SoI and interference plus noise covariance matrices, respectively.
# An Intelligent Parking Management System for Urban Areas
## 2 Related Studies
From the point of view of the type of sensors used, these systems mainly use infrared and electromagnetic sensors. As for wireless communications, they use radio frequency, with ZigBee technology being the most used. In terms of network topology the most widely used is a tree topology. The system proposed in this article is designed to monitor the entry and exit of vehicles into and out of outdoor parking zones located on public roads, not to monitor every single car space available, thus reducing the costs related to the deployment of technology. The system has been tested in closed car parks. On public roads, the system would not account for every single slot, but it is designed to provide estimations on real-time occupation levels. From a technological point of view, it uses a wireless network of photoelectric sensors, and uses 6 LoWPAN over IEEE 802.15.4 for communications. It can be used across multiple communications platforms, using the IPv6 stack, which is hugely important to enable the IoT. IPv6 provides a basic transport mechanism to produce complex control systems and to communicate with other devices in a cost-effective manner using low-power wireless network. The network topology adopted for this system is a tree topology. As a result of mass production, the sensors used for the system have a very low cost in comparison with other sensors used for parking management systems, such as electromagnetic or pressure sensors. Despite their low cost, these sensors perform very well in detection applications; indeed, infrared sensors are widely employed in industrial automation processes, having proven reliability. Furthermore, their compact size makes it possible to enclose the whole node in a small box. In addition to this advantage, both the sensors themselves and the controlling boards have very low power consumption, and for this reason each sensor node is to be powered by a combination of batteries and solar power, minimising the deployment efforts and costs, as no wiring is needed. Another advantage of this approach over others is that it does not rely on the user's involvement for the system to meet its purpose. There is no need for the vehicles to be equipped with smart location systems, or for the users to employ technological gadgets to access or update information about car park availability. The system is non-intrusive too: it does not identify the vehicles or their drivers at all, nor does it have to record or take pictures of them. Finally, this solution does not need more data processing than that done by the mainboards, further reducing infrastructure and system costs. Although it has not yet been developed, the system is intended to share parking availability data with the public authorities, contributing to a reduction in both traffic and its carbon footprint. In conclusion, the system, as will be seen in this article, is characterised by its ease of deployment, flexibility, and affordability when incorporating new nodes to monitor the access roads to the parking areas, and even new sensor subnets to monitor new parking areas.
## 3 Catalytic Advanced Oxidation Processes (AOPs)
### Fenton-like (H\({}_{2}\)O\({}_{2}\)) Activation  
Numerous SDB possess iron, which facilitates the catalytic decomposition of hydrogen peroxide into hydroxyl radicals (-OH). H\({}_{2}\)O\({}_{2}\) activation by SDB highly relies on the iron speciation and carbon phase. For instance, SDB pyrolyzed at 400/600 \({}^{\circ}\)C tend to release Fe\({}^{2+}\) for homogeneous H\({}_{2}\)O\({}_{2}\) activation, whereas SDB pyrolyzed at 800/1000 \({}^{\circ}\)C first adsorb 4-chlorophenols rapidly and oxidize them [37]. [PERSON] et al. [38] synthesized an iron-enriched SDB and applied it for tridosan degradation via Fenton-like reactions. The study revealed H\({}_{2}\)O\({}_{2}\) activation was primarily driven by iron species (80.3%), with a smaller yet notable contribution from persistent free radicals (PFRs, 19.7%), ultimately improving the efficiency of pollutant degradation. Pristine SDB has also demonstrated the ability to activate H\({}_{2}\)O\({}_{2}\) under near-neutral pH conditions. For instance, a study reported approximately 91.5% removal of ofloxacin, using raw municipal sludge biochar in conjunction with H\({}_{2}\)O\({}_{2}\), demonstrating inherent iron species participation [39; 40]. To optimize performance, researchers have also enhanced H\({}_{2}\)O\({}_{2}\) activation by intentionally doping SDB with metal species (e.g., Fe, Mn, and Cu). For instance, in situ iron impregnation (e.g., using FeCl\({}_{3}\) pretreatment) produces an SDB that releases Fe\({}^{2+}\)/Fe\({}^{3+}\) under reaction conditions, accelerating -OH generation. One recent study used Cu-loaded sludge biochar to effectively oxidize bisphenol-A in high-salinity water, addressing the challenge of organic removal in saline industrial waste [41].
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 2 Basic Theory
### Altitude Subsystem  
The altitude subsystem consists of a pitch angle loop, track angle loop and altitude loop.
## 2 Methodology
### Uncertainty Modeling  
The system effectively utilizes a technique that looks at future scenarios to adjust well to shifts in its operating environment. It considers uncertainties linked to policy alterations and energy costs by employing an approach that merges scenarios and stochastic optimization. The model has four scenarios; each represents a blend of policy circumstances and market price levels--for instance, policies with low market prices. The assessment examines policies with high market costs or prices based on historical data and expert opinions to assess their likelihood accurately. We delineated four distinct scenarios by amalgamating two principal factors: policy regulation (favourable versus unfavourable) and market prices (high versus low). The resulting scenarios are favourable policy regulation and high market prices, favourable policy regulation and low market prices, unfavourable policy regulation and high market prices, and unfavourable policy regulation and low market prices. A model with a probability distribution for both unfavourable scenarios in a risk-aware optimization strategy is used. The system follows a framework that considers the progression of time to aid in making investment and operational decisions every five years. Factors at each stage impact choices and allow for strategy adaptation based on evolving conditions. This adaptable decision-making method improves the model's capacity to manage uncertainties that arise gradually over time, evaluating aspects through scenario testing to guarantee compliance with policy guidelines and adjust to market changes, ultimately enhancing efficiency and reducing risks linked to deviations in scenarios.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Routing Complexity
#### 3.3.1 Bandwidth Overhead  
In our probe-based routing approach, for each interest packet and data packet, the additional overhead is 2 bytes to store a content name and 20 bytes to store a router ID. As described in Section 4.2, as compared to the basic CCN, our approach adds the size of 22 bytes for interest packets and data packets. As a result, our approach's bandwidth overhead is k \(*\) 22 bytes, where k is the throughput.  
Figure 4: _Probe-based routing process._
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 2 Materials and Methods  
### Experimental Set Up  
#### 2.1.1 Pressure Sensor  
The typical non-contact tonometer operating distance was assumed to be 11 mm. In view of these assumptions, a range of sensors were tested (silicon piezoresistive, dual pressure sensor; piezoresistive transducer, differential pressure sensor, piezoresistive static force sensor; piezoresistive transducer, gauge pressure sensor; differential compensated pressure sensor) for their applicability. The linear time-invariant systems were found to be best suited for the requirements, and thus the gauge pressure sensor (the MPX5050 GP (Freescale Halbletter Deutschland GmbH, Munich, Germany) was chosen for investigation. The MPX5050 GP is a piezoresistive, monolithic silicon pressure sensor which offers a differential sensitivity of 7.241 mmHg for a full scale of 362.004 mmHg, with an accuracy of \(\pm\)2.5% over the whole detection span.
## 3 Model Construction
### Comparison between CIEM and IEM  
Both CIEM and IEM can provide backscattering coefficient values considering the characteristics of the sensor and target. In this section, the difference between IEM and CIEM of SAR for VV and VH polarizations in C-band was studied. In Figure 2, by using IEM, the biases are 4.7 dB in the VV polarization and 0.8 dB in the HV polarization. However, by using CIEM, the biases are 0.8 dB in the VV polarization and \(-1.8\) dB in the HV polarization. In addition, the RMSE between the simulated and measured backscattering coefficient for VV polarization decreased from 5.8 dB to 2.2 dB after using CIEM, and the RMSE for HV polarization decreased from 4.2 to 2.8 dB. The results show that the CIEM performs better than the IEM, as observed by [PERSON] et al. [35; 36]. Therefore, only CIEM was used to simulate the backscattering characteristics in this section.