esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 3 Methodology
### Step1: The Framework of CCL: C-YOLOv4
#### 3.2.3 Improvement of Multi-Scale Identification Structure  
After the input image passes through the backbone network, C-YOLOv4 will output feature maps at scales 13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52. These feature maps are sent to YOLO Head for decoding and prediction after stacking convolution and other operations by the feature fusion module.  
However, in the CCL task, the container code area occupies a relatively small proportion in the whole picture. The container image corresponding to each grid in the 13 \(\times\) 13 scale feature map is too large, which makes it difficult to accurately locate the position of the container code area boundary container. Therefore, the C-YOLO v4 algorithm deletes the 13 \(\times\) 13 scale feature map and detection head.  
C-YOLO v4 uses the k-means clustering algorithm to obtain nine prior bounding boxes of different scales. The scales of these prior bounding boxes are generated based on the COCO or VOC dataset, which are not applicable to the container dataset.  
The k-means clustering algorithm randomly selects the initial clustering center point, leading to a certain degree of randomness in the clustering results. It is easy to fall into a local minimum. Compared with the k-means clustering algorithm, the k-means++ algorithm improves the selection of the initial clustering center point, resulting in clustering results that are more stable and reasonable. Therefore, we selected the k-means++ clustering algorithm as the prior bounding boxes clustering method. The C-YOLO v4 algorithm recalculates six greater prior bounding boxes. The recalculated prior container size is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & **Operator** & **Exp Size** & **Out** & **SE** & **NL** & **S** \\ \hline \(416^{2}\times 3\) & Conv2d & - & 16 & - & HS & 2 \\ \(208^{2}\times 16\) & bneck, 3 \(\times\) 3 & 16 & 16 & - & RE & 1 \\ \(108^{2}\times 16\) & bneck, 3 \(\times\) 3 & 64 & 24 & - & RE & 2 \\ \(104^{2}\times 24\) & bneck, 3 \(\times\) 3 & 72 & 24 & - & RE & 1 \\ \(104^{2}\times 24\) & bneck, 5 \(\times\) 5 & 72 & 40 & ✓ & RE & 2 \\ \(52^{2}\times 40\) & bneck, 5 \(\times\) 5 & 120 & 40 & ✓ & RE & 1 \\ \(52^{2}\times 40\) & bneck, 5 \(\times\) 5 & 120 & 40 & ✓ & RE & 1 \\ \(52^{2}\times 40\) & bneck, 3 \(\times\) 3 & 240 & 80 & - & HS & 2 \\ \(26^{2}\times 80\) & bneck, 3 \(\times\) 3 & 200 & 80 & - & HS & 1 \\ \(26^{2}\times 80\) & bneck, 3 \(\times\) 3 & 184 & 80 & - & HS & 1 \\ \(26^{2}\times 80\) & bneck, 3 \(\times\) 3 & 480 & 112 & ✓ & HS & 1 \\ \(26^{2}\times 112\) & bneck, 3 \(\times\) 3 & 672 & 112 & ✓ & HS & 1 \\ \(26^{2}\times 112\) & bneck, 5 \(\times\) 5 & 672 & 160 & ✓ & HS & 2 \\ \(26^{2}\times 160\) & Conv2d & - & 256 & - & HS & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of MobileNetV3.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design
### Transmission Waveform Design
#### 3.1.3 IOTA Filter Design  
IOTA is obtained from orthogonal Gaussian pulse which holds the optimal time-frequency focusing property. The IOTA pulses are generally called root-Nyquist self-transform pulses, where self-transform means that the beginning of the derivation is Gaussian-shaped pulse and root-Nyquist means the end of the derivation is orthogonal pulse.  
IOTA filter orthogonalizes the Gaussian filter, and has the same good time-frequency focusing performance as the Gaussian filter [29]. It can be used to reduce the out-of-band emission, PAPR and the ISI brought by FTN signaling.  
In the time-domain, the IOTA pulse, denoted as \(\xi_{\tau_{0}}(t)\), can be given by [30]  
\[\begin{split}\xi_{\tau_{0}}(t)&=\frac{1}{2}\sum_{k= 0}^{K-1}\bigg{\{}\overline{d}_{k,x_{0}}\bigg{[}\mathbf{h}_{EGF}\bigg{(}t+\frac{k}{ v_{0}}\bigg{)}+\mathbf{h}_{EGF}\bigg{(}t-\frac{k}{v_{0}}\bigg{)}\bigg{]}\bigg{\}} \cdot\sum_{l=0}^{K}\bigg{[}\overline{d}_{l,\tau_{0}}\cos\bigg{(}2\pi l\frac{t}{ v_{0}}\bigg{)}\bigg{]},\\ &\qquad-4\tau_{0}\leq t\leq 4\tau_{0},\end{split} \tag{6}\]  
where \(K\) is a constant parameter for IOTA, \(\mathbf{h}_{\text{EGF}}(t)\) represents the extended Gaussian filter (EGF) in the time domain and \(\overline{d}_{l,v_{0}}\) represents the IOTA coefficients given by  
\[\overline{\mathbf{d}}_{l,x_{0}}=\sum_{q=0}^{Q-1}\mathbf{b}_{k,q}\cdot\text{e}^{-\pi( 2q+k)},0\leq k\leq K-1,0\leq q\leq Q-1, \tag{7}\]  
with \(Q\) also being a constant value and \(\mathbf{b}_{k,q}\) the pre-defined weight coefficients.  
The important parameters to generate IOTA filter are listed in Table 1.  
The proposed waveform uses an IOTA filter as the prototype filter by replacing \(p(n)\) in (1) with (6). We get \(p(n)\) by discrete sampling on IOTA filter, with the sampling period the same as DFT-s-OFDM (i.e., \(T/L\)), where \(p(n)\) can be found by  
\[\begin{split} p(n)&=\frac{1}{2}\sum_{l=0}^{L-1}\left\{ \overline{d}_{l,x_{0}}\bigg{[}\mathbf{h}_{EGF}\bigg{(}\frac{nt}{L}+\frac{l}{v_{0}} \bigg{)}+\mathbf{h}_{EGF}\bigg{(}\frac{nt}{L}-\frac{l}{v_{0}}\bigg{)}\bigg{]} \right\}\cdot\sum_{l=0}^{L}\bigg{[}\overline{d}_{l,x_{0}}\cos\bigg{(}2\pi l \frac{nt}{v_{0}L}\bigg{)}\bigg{]},\\ &\qquad-4\tau_{0}\leq t\leq 4\tau_{0}.\end{split} \tag{8}\]  
IOTA has good performance of focusing on the time and frequency domain, and thus is better at resisting ISI and inter-channel interference (ICI) than a rectangular waveform or root-raised cosine (RRC) filter. IOTA filter also holds similar out of band (OOB) performance as the RRC filter as shown in Figure 2. This indicates that no additional bandwidth is required for deploying IOTA.
## 4 Bigram and Traditional Results
### Forecasting Water Levels
#### 4.3.2 Traditional Analysis
The results offer the potential to improve understanding and address peatland vulnerability to fire and associated carbon losses, benefiting the scientific community and informing peatland restoration strategies. A study on the development of neurocomputing intelligence models to forecast lake water level (LWL) fluctuations in five large lakes located in the northern US was conducted in [77]. The study uses three machine learning models, including LSSVR, MARS, and M5-Tree, and various input combinations based on correlated lags and time series periodicity. The performance of the models is evaluated using statistical metrics and graphical visualization, and the results indicate that the P-LSSVR model is the most powerful for modeling all lake levels and a better alternative to the other three neurocomputing intelligence models. Groundwater level mapping is crucial for resource management in arid regions with intricate terrain. In [29], researchers constructed a geospatial database and applied various machine learning techniques to learn groundwater depth using well data from the Mu Us Sandy Land, China. The Bayesian optimization algorithm was employed to fine-tune hyper-parameters, and GL maps were generated based on the predicted groundwater depth and elevation data. Machine learning approaches, such as Random Forest and extreme gradient boosting algorithms, demonstrated effectiveness in tackling regression problems related to groundwater research. Dissolved organic matter (DOM) is a crucial component in inland waters, impacting ecosystems, food webs, and the global carbon cycle. Although lake ecosystems make up a small portion of the Earth's water resources, they are especially susceptible to environmental and climate changes. Dissolved chromophoric organic matter plays a pivotal role in aquatic light regimes, trophic status, and drinking water safety. Urbanization and economic growth influence the composition of terrestrial organic matter and microbial degradation processes, leading to uncertainties in regional aquatic ecosystems. Fluorescence emission-excitation matrix spectroscopy is a sensitive technique for evaluating DOM sources and compositions. Remote sensing techniques are indispensable for monitoring water quality and DOM fluctuations, with machine learning methods offering the potential to characterize DOM components and sources. In [76], the authors aim to develop an algorithm for DOM fluorescence parameters in Chinese lakes and showcase its environmental connection with human activities and water quality management.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Observability and Auditing
#### 4.5.1 Auditing  
The Kubernetes auditing policy delineates the logging of API server activities, specifying the types of events to be recorded and the level of detail required. An example of an audit policy definition yaml file is presented in Figure 13:  
An effective auditing policy records API calls throughout various lifecycle phases (such as request receipt and response transmission) and encompasses contextual information, including user identity, resource type, and namespace. This detailed configuration aids in identifying misconfigurations and potential security incidents, while facilitating compliance monitoring in extensive environments [14; 21]. Centralized logging frameworks augment observability and threat detection within distributed Kubernetes clusters [14].  
Auditing regulations are delineated in the policy and serve as criteria for determining the volume of information documented for correlating API events. These regulations assess parameters such as users, actions (create, delete), or target resources and designate a log level: None, Metadata, Request, or RequestResponse. Evaluation is hierarchical, with the initial corresponding rule being implemented. Meticulously formulated regulations diminish log volume while maintaining critical security insights, facilitating effective anomaly detection and forensic examination [14; 22].  
The Kubernetes audit backend is an essential element of the auditing framework, tasked with storing and managing audit logs produced by the API server. It specifies the destination and format of audit data, accommodating multiple backends including log files, webhooks, or external logging solutions. This adaptability enables the integration  
Figure 13: Proposed starting auditing policy.  
of logs with SIEM platforms or centralized log collectors such as the ELK stack or PLG stack [14; 23]. An optimally configured audit backend enhances compliance, enables real-time threat detection, and aids post-incident analysis. Audit logs can be directed through webhook backends to external processors for sophisticated filtering, correlation, and enrichment, which is especially crucial in microservices environments characterized by high log volume [14; 24]. Furthermore, distributed log aggregation systems utilizing microservices for log collection across nodes have demonstrated efficacy in scalable Kubernetes deployments [24]. Backend configurations must equilibrate log granularity with resource limitations to ensure security and performance, utilizing container-native methodologies like eBPF for improved visibility and efficiency [25].
## 2 Grid Forming and Small-Signal Modeling
### Base Case and Active Power Control Loop  
To complete the analysis, the active power dynamics under setpoints is determined by Equation (7), a particularized form of Equation (4). Again, various values of \(m_{p}\) are considered, including two different inertia cases (H = 2.5 s and H = 5 s). Results are shown in Figure 6.  
\[\frac{\Delta P}{\Delta P^{*}}=-\frac{m_{p}\omega_{b}K_{t}}{\tau_{p}s^{2}+s+m_{ p}\omega_{b}K_{t}} \tag{7}\]  
Looking at the results, the control bandwidth is way lower than in GFL (which is typically in the range of 100-200 Hz or higher [34]), and again, it is closely related to the emulated inertia. The larger the inertia, the smaller the bandwidth.  
Some control structures in GFM allow for the implementation of the inertia with larger bandwidth for the active power setpoint [35], for example, the scheme depicted in Figure 7 with the transfer function expressed in Equation (8).  
\[\frac{\Delta P}{\Delta P^{*}}=-\frac{m_{p}\omega_{b}K_{t}}{s+m_{p}H_{c}\omega_ {b}K_{t}} \tag{8}\]  
However, this increment in the bandwidth (Figure 8) comes at the cost of a less damped response, and the bandwidth still highly depends on the inertia.  
Figure 7: (a) Base case PSL (VSG + Droop) and (b) modified PSL to increase P* bandwidth.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Energy Efficiency  
We define the EE, in \(\text{bit}/\text{J}/\text{Hz}\), as  
\[\eta_{\text{sch}}=\frac{R\cdot(1-\mathcal{O}_{\text{sch}})}{P_{\text{total}}^ {\text{(sch)}}}, \tag{16}\]  
in which the numerator represents the system throughput, in \(\text{bit}/\text{s}/\text{Hz}\), and \(P_{\text{total}}^{\text{(sch)}}\) is the total power consumption of each scheme. Let us recall that, according to (3), \(P_{\text{total}}^{\text{(sch)}}\) depends on the number of active antennas of each transmission scheme, with \(n_{\text{t}}=n_{\text{r}}=1\) for both SISO and AS schemes, while \(n_{\text{t}}=N_{\text{t}}\) and \(n_{\text{r}}=N_{\text{r}}\) for the SVD scheme. In addition, the power consumption of the circuit blocks also depends on the employed RF front-end architecture.  
It is also worth noting that AS is expected to yield a higher EE than SISO, since the outage probability of AS is lower than that of SISO, for the same total power consumption. On the other hand, the outage probability of the SVD scheme is lower than AS; however, since all antennas are active in both transmitter and receiver, the power consumption is also higher, which leads to a trade-off in terms of EE.
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 1 Introduction
Such a system is also very helpful during the recovery and rehabilitation process. In these contexts, people with disabilities can avoid feelings of isolation and loneliness and, thus, their quality of life and health issues can be improved. Furthermore, remote monitoring and telehealth from the wheelchairs are common challenges for people with disabilities. Telepresence wheelchair systems are designed not only to provide assistance to the wheelchair users but also allow wheelchair users to interact with remote users such as healthcare professionals, nurses, as well as doctors for healthcare instruction. Moreover, telepresence wheelchairs can transmit the wheelchair user's biological signals such as information on heart rate, blood pressure, and body temperature for advanced monitoring in assistive healthcare applications. Individuals with disabilities would greatly benefit from these technologies. However, designing and developing such a telepresence wheelchair remains a technical challenge of real-time communication, remote control, and a wide field of view. The primary objective of this research is to investigate the feasibility of a 360-degree field of view for a telepresence system and develop a telepresence wheelchair to provide assistive mobility for people with disabilities and older adults. A novel approach to emerging web real-time communication to improve system performance and develop an independent application is explored. The key idea of this unique telepresence wheelchair is to provide efficient and safe wheelchair navigation. Several techniques are involved in the process of completing the design in real-time applying real hardware. Finally, a complete telepresence system with a wide field of view and remote collaborate-control is designed. The aim is to not only provide a prototype for a telepresence wheelchair for healthcare assistants but also adopt the advancement of emerging information technology in the field of robotics, and biomedical engineering. Our major contributions in this paper are summarized as follows. First, we developed and implemented the telepresence wheelchair with 360 degrees field of view. Then, we developed the real-time communication protocol of audio and video based on WebRTC with the acceptable performance of video quality. Finally, we developed the artificial neural network for QoE estimation of video performance, which helps users can control the wheelchairs avoiding the region with the poor signal performance. The implemented wheelchair which was tested in various experiments demonstrated its applicability to telepresence functions. The rest of the paper is organized as follows. Section 2 introduces the methodological approach. Section 3 is devoted to the experiments and results. Section 4 presents the discussions. Finally, Section 5 provides a conclusion to this paper and, then, sketches the directions for future research.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 3 Experiments of Estimating Significant Wave Height from SAR with Long Integration Times
### Case 2-Multiple Ocean Waves from SAR with Long Integration Times
#### 3.2.2 Experiment of Significant Wave Height Estimation of the Second Ocean Wave  
When \(\Delta V_{opt}^{2}=-30\) m/s, the refocused SAR image of the second ocean waves is shown in Figure 10a. The corresponding wave spectrum is shown in Figure 10b.  
The completely invisible wave system in Figure 6a is shown in Figure 10a. As shown in Figure 10b, the wave spectrum is very clear, which is more conducive to estimate significant wave height. Using the process shown in Figure 1, the wavelength \(\lambda_{p}^{2}=61.46\) m of ocean waves can be obtained. Due to \(\Delta V_{opt}^{2}<0\), the wave propagation direction after \(180^{\circ}\) ambiguity removal is \(\phi^{2}=173.49^{\circ}\). Finally, the significant wave height \(SWH^{2}=0.40\) m is calculated by Equation (19).
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 2 Research Object and Numerical Method  
### Research Object  
The object of this study is the wing fuel tank located in the wing of the aircraft, referred to as the wing tank, as shown in Figure 1. The tank has a flat shape and a wall thickness of 3 mm. The tank is equipped with baffles and related pipelines with flow holes inside, and the baffles mainly include multiple transverse frames and a longitudinal wall, dividing the tank area into multiple small compartments. The tank wall and pipelines are made of aluminum with a density of 2700 kg/m\({}^{3}\). The tank is not completely filled with liquid fuel, with a fill ratio of 85%. The density of the fuel is 780 kg/m\({}^{3}\).  
To facilitate accurate capturing of the free liquid surface height and impact pressure at different locations, eight virtual sensors are installed in the tank to measure the fuel immersion height, i.e., the corresponding liquid surface height at each position, and sense the impact pressure of the fuel. The position coordinate information of each sensor in the tank is shown in Table 1, where the \(X\) coordinate is based on the origin of the aircraft nose, the \(Y\) coordinate is based on the aircraft horizontal reference plane, and the \(Z\) coordinate is based on the aircraft symmetry plane with units all in meters.
## 3 Human Activity Recognition Techniques
### AI-Driven HAR Algorithms
#### 3.2.3 IoPVT-Based HAR
Deep learning models in conventional HAR can be computationally heavy, often requiring powerful hardware or offloading to the cloud, which introduces delays. The IoPVT uses edge computing, processing data closer to the source while using virtual components in the cloud for heavier tasks like model optimization. Edge-driven HAR has already shown promising accuracy with lower resource use. The IoPVT could take this further, dynamically balancing workloads to optimize power and speed. Digital twins, virtual replicas of physical entities, could simulate activities in real time, predicting outcomes or filling in gaps when sensor data are noisy or incomplete. Conventional HAR rarely incorporates such predictive modeling. For instance, if a sensor fails in mid-activity, the IoPVT could use a virtual twin trained on past data to infer the activity, which static AI models cannot easily do. Additionally, the virtual layer of the IoPVT supports cross-domain learning and transfer learning, allowing models trained in well-monitored urban environments to be adapted for less-resourced rural or coastal areas. By simulating rare or region-specific activities, digital twins enable models to generalize better by anticipating edge cases that might not be captured in limited physical datasets. We believe that IoPVT-enabled HAR is a game-changer, pushing the boundaries of context-aware AI and ubiquitous computing. Using advances in AI, digital twins, and next-generation IoT networks, HAR systems will become more intelligent, efficient, and privacy-aware, making them indispensable in healthcare, smart environments, and industrial applications.
## Open Issues in DTN Multicasting
### Selfish Behavior of Nodes  
Most DTN routing approaches expect nodes to assist with message forwarding in a cooperative way. However, this is not necessarily true in the real-world environment due to the selfish behavior of the nodes, which are classified into individual and social selfishness [85]. The former refers to the reluctance of a node to relay messages to conserve resources, and the latter points to its willingness to forward messages to nodes in the same community. Several incentive mechanisms [11; 79; 86] are proposed for DTN routing approaches to motivate selfish nodes to cooperate and help in improving system-wide performance.  
Multicast in DTN is intended to provide efficient message delivery to a group of users with less resource overhead. However, it is also similarly affected by the selfish behaviors of nodes. [PERSON] et al. [42] and [PERSON] et al. [87] studied the impact of this selfish behavior on two-hop relay and epidemic routing schemes in terms of average latency and transmission cost. They demonstrated that individual selfishness increases both the message transmission delay and the cost, whereas social selfishness increases only the message transmission delay and reduces the transmission cost. Therefore, individually selfish and socially selfish nodes impact network performance differently. A sophisticated multicast routing algorithm is thus required to detect and handle the two types of selfish nodes differently according to the context information to improve performance.