esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Segmentation  
The first step in our approach is to detect rails in the original image using a state-of-the-art image segmentation model. For this purpose, we select InternImage [32] as the most suitable model for our use case. According to [33], InternImage achieved the highest performance among models with a publicly available codebase and ranked second overall on the CityScapes dataset, with a mean Intersection over Union (IoU) of 84.1, close behind VLTSeg with 84.4. We consider the CityScapes dataset, which provides outdoor images from the automotive domain, as sufficiently similar to the railway domain.  
InternImage is an encoder architecture that replaces standard convolutions with deformable convolutions, followed by a feed-forward network at each layer, enabling dynamic adaptation of receptive fields. This design yields a flexible, transformer-like architecture while preserving the locality and computational efficiency of convolutional operations. The authors implemented various decoding methods for both image classification and segmentation tasks. In this work, we adopt UPerNet [34] as the decoder, a segmentation-specific architecture that fuses multi-scale features via a Feature Pyramid Network and captures global context through a Pyramid Pooling Module, before upsampling and combining  
Figure 1: Definition of the _ego_ coordinate system \(Q_{ego}\) in contrast to the camera coordinate system \(Q_{c}\).  
the features to generate dense, pixelwise predictions. Within the InternImage pipeline, UPerNet demonstrated strong performance on the CityScapes dataset.  
However, as no pretrained InternImage model includes a class for rails, transfer learning is necessary. To accomplish this, we use the RailSem19 dataset [35], which contains 8500 unique images from the railway domain captured in _driver's view_ with additional semantic segmentations, including a class for rails (_rail-raised_). Since InternImage is built on top of the MMSegmentation [36] pipeline, we integrate RailSem19 into this framework and initially train the model using Cityscapes-pretrained weights, yielding a final mean IoU of 70.03.  
To evaluate the overall impact of the segmentation on the calibration accuracy, we additionally create ground truth segmentations for the OSDaR23 dataset. Although OSDaR23 does not directly provide ground truth segmentations, it includes polylines representing the rails. We leverage these polylines to generate ground truth segmentations by rasterizing them into images. Figure 2 provides an example of the segmentation results. To assess the robustness of our system on entirely unseen data, we explicitly train InternImage exclusively on the RailSem19 dataset and do not use our generated ground truths from OSDaR23 for training.  
By applying segmentation, we assign a label to each pixel, and consequently to each corresponding 3D point. The set of all \(N\) 3D points labeled as _rail-raised_ is defined as \(\textbf{P}_{d}^{r}\), with dimensions \(3\times N\). For images in the OSDaR23 dataset, this typically results in dimensions of approximately \(3\times 7\cdot 10^{6}\).
## 2 Materials and Methods
### Ground Measurements  
EC flux measurements were conducted at a height of 2.0 m in the central of a fetch of at least 200 m in all directions by open-path EC method from 12 May 2009 to 31 August 2013. The EC system consisted of one three-dimensional sonic anemometer (Model CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open-path fast-response infrared gas analyzer (IRGA, Model LI7500, LI-Cor Inc., Lincoln, NE, USA), which provides a digital output of fluctuations in three wind components, sonic temperature, water vapor, and CO\({}_{2}\) density at a rate of 10 Hz. Calculations were carried out for each 30 min period by a data-logger (Model CR1000, Campbell Scientific Inc.). Calibrations of CO\({}_{2}\) and H\({}_{2}\)O flux were performed annually.  
Standard meteorological and soil parameters were measured using an array of sensors. A Li-cor quantum sensor (Model LI190 SB, LI-Cor) was mounted at a height of 1.5 m with the EC system to measure photosynthetically active radiation (PAR). The radiation balance of solar and far-infrared radiation was measured by a Net Radiometer (Model CNR1, Kipp & Zonen, Utrecht, The Netherlands) at a height of 2 m, providing total solar radiation (SR), reflected solar radiation (SRR), far-infrared radiation (LR), and reflected far-infrared radiation (LRR), to allow calculation of net radiation (Rn). Air temperature (Ta) and relative humidity (RHa) were measured with shielded and aspirated sensors (Model HMP45C, Vaisala, Finland) at 2 m (Ta2/RHa2). Vapor pressure deficit (VPD) was calculated as the difference between the saturation and actual vapor pressures at the given temperature based on the measured relative humidity and air temperature. Soil temperature at depths of 5 cm was measured with the thermocouple sensor (Model 107-L, Campbell Scientific Inc.). All channels from meteorological sensors were recorded as 30 min averages with a data-logger (Model CR10X and AM25T, Campbell Scientific Inc.). The data were retrieved by a laptop computer every three weeks.  
The vegetation leaf area index (LAI\({}_{\rm G}\)) was regularly measured by a leaf area meter (Model AM200, ADC BioScientific Ltd., Hertfordshire, UK) approximately every two weeks during the growing season (May to September). Five sampling quadrants (0.5 m \(\times\) 0.5 m) were randomly measured both in hummocks and hollows, the biomass samples were oven-dried at 65 \({}^{\circ}\)C till that the weight did not change and then recorded.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 4 Use-Cases  
We assembled three basic cases, in each of which we examined one failure type in the tree topology shown in Figure 11. These cases showcase the detection process during which our framework locates links that act differently than they supposed to, based on the network specification. Since SDN is applied extensively in data centers, we opted to showcase detected failure types on such a topology that is still simple enough to understand and represents the properties of a data center network. We note that the framework can be used on more complex cases and topologies, and we selected these cases based on simplicity to demonstrate operation and the underlying aim: To detect that a certain type of traffic deviates from its path defined by the specification. The three cases are the following:  
* Use-case 1: The use-case emulates a situation where the network layout does not match the specification. Concretely, we investigated the effects of a possible host misconfiguration. We connected two hosts--_h4_ and _h5_--to the network with their medium access control (MAC) and IP addresses swapped;
* Use-case 2: Here we introduced a configuration error into one of the pieces of forwarding equipment to emulate a problem with the control application. We added a forwarding rule to switch _sw1_ that drops the packets destined to _h1_;
* Use-case 3: In this case, we investigated the problems caused by a link failure. We turned the link between _h3_ and _sw4_ to down.  
In the following sections, we demonstrate the results gained from running our framework on the above use-cases.
## 4 Simulation and Result Analysis  
### Simulation Conditions  
In this section, simulation results are provided to illustrate the performance of the proposed UWAOSDM/PN system over UWA channels. In the simulation, the duration \(T_{s}\) of each OSDM symbol is 213 ms, the data bits D of each OSDM symbol is a constant 1024, that is to say, the product of M and N is a fixed value D.  
Table 1 shows the simulation parameters of the UWAOSDM/PN system.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Band frequency & 21–27 kHz & Channel coding & Convolutional \\ Sampling rate & 96 kHz & Bandwidth & 6 kHz \\ Relative velocity & 5 m/s & Mapping & QPSK \\ Code rate & 1/2 & Up-sample coefficient & 16 \\ \(T_{cp}\) & 42.7 ms & \(T_{PN}\) & 213 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of the pseudo-noise sequence-based underwater acoustic orthogonal signal division multiplex (OSDM) communication system (UWAOSDM/PN).  
As shown in Equation (16), the time-domain model of the UWA channel is composed of the attenuating component and the time-delay component. Assuming that the channel response is wide-sense stationary within one data frame.  
\[h(\tau)=\sum_{p}A_{p}\delta(\tau-\tau_{p}) \tag{16}\]  
where, \(p\) represents the number of multipaths, \(A_{p}\) denotes the attenuation coefficient of each path, \(\delta\) means impulse response function and \(\tau_{p}\) stands the delay time of the \(p\)th path.  
Figure 6 shows two UWA channel models adopted in this paper. Both models have been normalized. As shown in the figure, the horizontal axis is delay time, the vertical axis is normalized amplitude, and Figure 6a shows the channel model 1 of BELLHOP [22] which contains five paths. The first path is direct path. The maximum delay spread is about 6.9 ms. Figure 6b shows the channel model 2 which contains seven paths [23]. The first path is a direct path. Its maximum delay spread is about 12.5 ms.
## 1 Introduction
However, the Wannier approach does not immediately justify truncation to the lowest band because the linear part does not have the band gaps assumed in [23; 26; 27]. Thus the question of justifying the derivation of finite systems of (possibly a few) DNLS equations from the infinite system requires some additional analysis, and also motivates a better understanding of the structure of the infinite system. A first result of the paper is an outline of the global existence theory, that is the boundedness of a suitable norm of the solutions. This type of result is a mathematical way to describe the absence of catastrophic self-focusing (beam collapse) and the possibility of stable localized beams [28]. The result also implies an estimate for the energy (optical power) at different length scales and provides a heuristic justification of truncation to a finite number of DNLS systems, corresponding to Wannier modes of the first bands. The main result of the paper is a proof that the infinite system resulting from the Wannier basis expansion is a Hamiltonian system. This fact implies the Hamiltonian structure of the finite band truncations and can useful in analyzing discrete soliton structures, using for instance methods from [8; 9]. The proof assumes that the Wannier functions are real, and we subsequently give examples of an explicit construction of real Wannier functions in terms of explicit Bloch functions. We also examine some features of the linear part of the problem, in particular we show that it is diagonalized by the trigonometric functions. This observation implies that the dispersion relation and the coupling between the modes can be computed with relative ease, and that the linear part of the problem is homogenized in the Wannier basis, i.e., is effectively a translation invariant [29; 30]. This latter property is an additional motivation for further developing Bloch-Wannier analysis in nonlinear wave equations. The paper is organized as follows. In Section 2 we outline the global existence theory for the coupled Schrodinger-elliptic system and show that the system in the Wannier basis is Hamiltonian. In Section 3 we discuss the construction of real Wannier functions. We also discuss translation invariance properties of the linear part of the system. In Section 4 we discuss some questions for further work.
###### Abstract  
In this paper, we introduce a half-duplex (HD) energy harvesting (EH) relay network over the different fading environment with the effect of hardware impairment (HI). The model system was investigated with the amplify-and-forward (AF) and the power splitting (PS) protocols. The system performance analysis in term of the outage probability (OP), achievable throughput (AT), and bit error rate (BER) were demonstrated with the closed-form expressions. In addition, the power splitting (PS) factor was investigated. We verified the analytical analysis by Monte Carlo simulation with all primary parameters. From the results, we can state that the analytical and simulation results match well with each other.  
amplify-and-forward (AF); relay network; achievable throughput (AT); outage probability (OP); BER; energy harvesting (EH) +
Footnote †: journal: Applied Sciences
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Internet of Things (IoT)
#### 5.1.5 Industrial Internet of Things (IioT)  
The IioT refers to a network of connected machines and devices in the industry for machine-to-machine (M2M) and machine-to-human (M2H) communications [93]. The IioT market is expected to reach $197 billion by 2023 from $115 billion in 2016 with a CAGR of 7.5% from 2017 to 2023 [94]. Applications involve predictive maintenance, quality control, safety management, and supply chain optimization. The IioT sensors and devices  
Figure 16: Types of communications on the Internet of Vehicles (IoV).  
are often placed in noisy environments to support mission-critical safety applications. These applications have stringent latency and reliability requirements for proper control decisions [95]. In some cases, the IIoT may require a reliability of 99.99999% [96] as information loss could be catastrophic in some scenarios such as nuclear energy plants.
# TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition
## 4 Experiments
### Ablation Study
#### 4.2.4 Analysis of Attention-Based BiLSTM  
In Table 4, we evaluated the performances of different variants of the visual representation decoder by using the sequential features from the two-stream CNN encoder as inputs. The result showed that adding the LSTM network was beneficial for decoding the long-term visual representations into temporal dependencies because of its capability of \"remembering\" information, which yields margins of at least 1.3% and 3.1% on UCF101 and HMDB51. Furthermore, the bidirectional structure outperformed the unidirectional LSTM structure, because it provides double temporal contexts by gathering information from both previous and future segments. Additionally, the temporal attention mechanism was applied to the LSTM network, and the results verified that with the additional temporal attention layer, both unidirectional and bidirectional LSTMs, performed better than the LSTMs without the attention mechanism. This was because the temporal attention mechanism simulates the attention of humans by focusing less on unimportant video segments and concentrating more on salient parts by assigning different attention weights.  
We also compared the performance of using the residual connection to integrate CNN visual representations with BiLSTM temporal relationships as depicted in Figure 9. From the comparison of the last two rows of Table 4, we noticed that the network with the residual connections achieved  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decoder** & **UCF101** & **HMDB51** \\ \hline TBRNet without decoder & 92.0 & 65.2 \\ LSTM \(\rightarrow\) & 93.5 & 69.0 \\ LSTM \(\leftarrow\) & 93.3 & 68.3 \\ LSTM \(\rightarrow\) + attention & 94.1 & 69.7 \\ BiLSTM & 94.7 & 71.4 \\ BiLSTM + attention & 95.2 & 72.5 \\ BiLSTM + attention + shortcut & 95.4 & 72.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracies (%) in ablation study of the attention-based BiLSTM decoder on UCF101 and HMDB51. Here, \(\rightarrow\) denotes the chronological order, and \(\leftarrow\) denotes the reverse chronological order.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Layers** & **UCF101** & **HMDB51** \\ \hline A-Conv5 and M-Conv5 & 80.1 & 51.6 \\ A-fc6 and M-fc6 & 88.5 & 62.0 \\ A-fc7 and M-fc7 & 90.8 & 63.4 \\ A-fc7 and M-fc6 & 87.3 & 58.5 \\ A-fc6 and M-fc7 & 92.0 & 65.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracies (%) of different fusion strategies for two streams on UCF101 and HMDB51. “A-” means the layer from the appearance stream, and “M-” means the layer from the motion stream.  
higher accuracy, and we attribute this improvement to the better propagation of global spatiotemporal representations within the whole network resulting from the overall fusion.
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 5 Conclusions  
This study proposes an adversarial learning network called AdvSemiSeg-MA for semi-supervised semantic segmentation of mechanical assembly images. First, a fusion method of ASFF multiscale output is proposed. This fusion method enables the network to make full use of feature information at different scales and improves the segmentation accuracy of the network for small-target objects. Then, an RFASPP module of the dilated convolutional pyramid that imitates the human receptive field is proposed to improve the network's ability to extract deep features. Subsequently, in the semantic segmentation network, the CoordConv module is introduced to enable the convolution to have spatial perception ability and make the semantic segmentation network position-sensitive. Finally, in the discriminator network, the method of spectral normalization is introduced into the discriminator network, and the depth of the discriminant network is deepened. This improves the stability of semi-supervised network training and enhances the segmentation accuracy. This study establishes the synthesized assembly depth image dataset and actual assembly RGB image dataset for semantic segmentation. The experimental results show that the MIoU of the AdvSemiSeg-MA network on the synthesized assembly depth image dataset and actual assembly RGB image dataset are 97.728% and 95.412%, which are 4.483% and 5.736% higher than that of the baseline AdvSemiSeg, respectively. To further validate the segmentation accuracy, the AdvSemiSeg-MA network is compared with the recently proposed PS-MT and U2 PL networks on the synthesized assembly depth image dataset. The results show that the MIoU of AdvSemiSeg-MA is 4.205% higher than that of the PS-MT network and 3.605% higher than that of the U2 PL network. Meanwhile, to verify the effectiveness of the network, it is tested on a public dataset. AdvSemiSeg-MA is 1.879% higher than the baseline AdvSemiSeg. Therefore, the AdvSemiSeg-MA network proposed in this study can achieve high segmentation accuracy even when there are only a few labeled datasets and the segmentation target is small.  
Conception of project, C.C. and S.W.; execution, C.C. and S.W.; manuscript preparation, S.W. and J.W.; writing of the first draft, S.W. All authors have read and agreed to the published version of the manuscript. The research is supported by the National Natural Science Foundation of China: 52175471.  
Not applicable.  
The data used in this research are available on GitHub at [[https://github.com/DeeplearningXiaobai/AdvSemiSeg-MA](https://github.com/DeeplearningXiaobai/AdvSemiSeg-MA)]([https://github.com/DeeplearningXiaobai/AdvSemiSeg-MA](https://github.com/DeeplearningXiaobai/AdvSemiSeg-MA)) (accessed on 22 September 2022).  
The authors declare no conflict of interest.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### 5G Threats
#### 3.2.2 Attacks against Integrity  
According to [56], there are six attacks in this category, which are as follows: tempering attack, message insertion attack, message modification attack, cloning attack, message-blocking attack, and spam attack. The attack against integrity occurs when data are transmitted between the 5G nodes and mobile users although hash functions are mostly used for assuring the integrity of exchanged data.
## 1 Introduction and Background
[11] corroborate this with their study demonstrating that assessing performance with a randomized training and test split indicates remarkably accurate identification of eight activities, attaining a 96% F1 score in the context of singular participant and stationary position data. However, when subjected to thorough backtesting, the F1 score declines, settling at 54%, indicating challenges with robustness and generalization. Ref. [3] propose subject cross-validation in their educational approach to the common challenges in the ARC chain of HAR. Ref. [12] highlighted the impact of overlapping windows, a common segmentation technique, in the correlation challenges of k-fold cross-validation, recommending subject CV (further explained in Section 3) as a performance evaluator for HAR systems. A recent study by [13] further validates this conclusion on three datasets of varying modalities: the CASAS containing binary motion sensors, MHEALTH, and PAMAP2 on-body inertial sensors trained on Random Forest (RF) and Graph Neural Networks (GNNs). These showed that the traditional techniques' reported accuracy is highly overestimated, regardless of the data type. Ref. [14] introduced a residual learning framework designed to simplify the training of much deeper networks than those traditionally used. By restructuring the layers to learn residual functions that relate to the layer inputs rather than learning functions without reference, ref. [14] made training more manageable. Comprehensive empirical results demonstrate that these residual networks are more straightforward to optimize and benefit from significantly increased depth, achieving higher accuracy. Ref. [15] propose an efficient approach to reduce DenseNet redundancy by replacing its bottleneck with their SMG  
Figure 1: The process of the activity recognition chain. module, enhanced with local residuals. The SMG module uses a two-stage pipeline designed to integrate previous outputs. It gradually condenses redundant features with hierarchical convolutions, followed by multi-kernel depth-wise convolutions. This results in a compact output with richer, multi-scale features. This work explores the effectiveness of LOSO based on training models on extracted features. These systems show a more valid and lower generalization accuracy of 76% when validated with LOSO and 89% on k-fold cross-validation.