esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The fifth generation (5G) of wireless communication is in its finalization stage and has received favorable reception in many nations. However, research is now geared towards the anticipated sixth-generation (6G) wireless network. The new 6G promises even more severe performance criteria than the current 5G generation. New sophisticated technologies and paradigms are expected to be incorporated into the 6G network designs and procedures to meet the ever-dynamic user needs and standards. These 6G-enabling technologies include digital twin (DT), intelligent reflecting surface (IRS), visible light communication (VLC), quantum computing (QC), blockchain, unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA), among others. Optimal network performance requires that machine learning (ML) techniques be integrated over the 6G wireless network to provide solutions to highly complex networking problems, massive users, high overhead, and computational complexity. Consequently, we have provided a state-of-the-art overview of wireless network generations leading to the future 6G, and huge emphases have been laid on ML's role in optimization applications for different enabling 6G technologies. Several key performance indicators for the different application scenarios have been highlighted. ML has proved to significantly improve the performance of the existing 6G-enabling technologies, and choosing the appropriate approach can ultimately yield optimal results.  
digital twin; blockchain; machine learning; intelligent reflecting surface; UAV; 6G wireless networks +
Footnote †: journal:
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 3 Methodology
### Model Training  
To use self-supervised signals to improve the performance of the POI recommendations, SSTGL uses a multi-task training strategy to optimize the ranking loss and contrastive learning loss jointly:  
\[\mathcal{L}=\mathcal{L}_{main}+\lambda_{1}\mathcal{L}_{ssl}+\lambda_{2}\| \Phi\|_{2}^{2}, \tag{16}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are hyper-parameters used to control the strengths of the SSL and regularization term, and \(\Phi\) denotes the set of GNN parameters.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 3 Proposed Method
### Light Detection and Ranging (LiDAR) Sensing System Overview  
We use LiDAR to scan environmental information in a 3D space. After the LiDAR scan, the packet is taken out, and the information such as distance, angle and reflectivity can be obtained after the packet is decoded. We use the packet information to construct a three-dimensional point-cloud map.
# Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime
## 4 Transmission of OFDM Waveform in a Dispersive Medium  
The orthogonal frequency division multiplexing (OFDM) modulation technique is based on a digital multi-carrier signaling. A set of symbols are mapped onto adjacent sub-carriers in the frequency domain, employing an Inverse Fast Fourier Transformation [15]. A Cyclic Prefix (CP) is added to preserve orthogonality and reduce Inter-Symbol Interference (ISI) [16]. One of the main benefits of CP-OFDM is its simplicity, and its susceptibility against ISI and fading.  
One of the notable drawbacks of OFDM is its wide bandwidth. While preventing ISI, the use of a Cyclic Prefix leads to a reduction in spectral efficiency, as sending a redundant information. The use of CP-OFDM is also characterized by having high Out of Band Power, leading to possible Adjacent Channel Interference [17]. The transmitted complex envelope of the OFDM signaling is given by [7]:  
\[\widetilde{A}_{in}(t)=\sum_{k=0}^{K-1}\left[I_{m}(k)+jQ_{m}(k)\right]e^{jk\frac {2\pi}{t}t} \tag{12}\]  
where \(K\) is the number of subcarriers, \(I_{m}(k)\) and \(Q_{m}(k)\) are the respective in-phase and quadrature components of the \(m\)th Quadrature Amplitude Modulation (QAM) symbol transmitted in subcarrier \(k\), and \(T\) is symbol temporal duration. The Fourier transform of the transmitted complex envelope is:  
\[\widetilde{A}_{in}(jf)=\sum_{k=0}^{K-1}\left[I_{m}(k)+jQ_{m}(k)\right]\delta \left(f-\frac{k}{T}\right) \tag{13}\]Substitution of Equation (12) into Equation (5) results in the received complex envelope:  
\[\overline{A}_{out}(t)=\sqrt{T_{FS}(d)}\sum_{k=0}^{K-1}\left[l_{m}(k)+jQ_{m}(k) \right]\cdot H\left(\frac{k}{T}+f_{\mathcal{E}}\right)\cdot e^{jk\frac{2\pi}{T}t} \tag{14}\]  
Examination of Equation (14) reveals that the constellation of the received signal is affected by the frequency response \(H(jf)\) of the medium. In the case of a wireless link operating in millimeter waves, the resulted complex envelope is affected by the dispersion of both attenuation and phase of the atmospheric refractivity:  
\[\overline{A}_{out}(t)=\sqrt{T_{FS}(d)}\sum_{k=0}^{K-1}\left[l_{m}(k)+jQ_{m}(k )\right]\cdot e^{-[\alpha(\frac{k}{T}+f_{\mathcal{E}})+j\beta(\frac{k}{T}+f_{ \mathcal{E}})]t}\cdot e^{jk\frac{2\pi}{T}t} \tag{15}\]  
Inspection of Equation (15) for the complex envelope reveals the influences of absorptive and dispersive atmospheric propagation effects on the constellation expected at the receiver site. It is noted that not only is attenuation resulted due to the atmospheric absorption, but also a frequency dependent phase shift that leads to a distorted constellation. The expression will be used to study the role of atmospheric refractivity in the determination of the performance of OFDM wireless link operating in millimeter wavelengths. In the following sections, we will focus on EHF bands allocated for the next generation of cellular networks--the 5G.
## 4 Results and Discussion
### Temporal Performance Analysis
The results of our analysis of the temporal performance of SEM-LEWS in the study region are summarized in Table 5.  
Figure 14: Progression of changes in rainfall and the corresponding warning levels over a specific time scale leading to Landslide occurrence 2 in Figure 13a. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Mean Lead Time**} & \multicolumn{2}{c}{**Annual Expected Total**} & \multicolumn{2}{c}{**Landside Probability**} & \multicolumn{2}{c}{**Landside Probability at**} & \multicolumn{2}{c}{**Approximate Critical**} \\ \hline
**Attention** & **Watch** & **Aleet** & **Emergency** & **Duration of Warning** & **During Warning Event** & **High Warning Levels** & **Continuous Rainfall Amount** \\ \hline
1 d 8 h & 4 h & 1 h & 1 h & 7 d & 13\% & 80\% & \(-\)20 mm (small-scale landslides) \\  & 25 min & 43 min & 10 min & 7 d & 13\% & 80\% & \(-\)310 mm (large-scale landslides) \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of temporal performance of the landslide early warning system based on the sequential evaluation method in the specific case study region.
## 5 Adaptive Decision Threshold Design Method Based on Auxiliary Channel
### Noise Estimation Based on Auxiliary Channels  
Since \(V_{th}=\lambda P_{n}\), when \(\lambda\) is constant, \(V_{th}\) is only related to \(P_{n}\). The estimator of \(P_{n}\) is \(\hat{P}_{n}\). The real-time estimation of noise variance can be realized based on auxiliary channels. We multiply the received sample sequence with unrelated pseudo-noise sequences (values of \(\pm 1\)) and add them up in a symbolic period to obtain  
\[y_{l}=\frac{1}{N}\sum_{k=(l-1)N+1}^{IN}x^{*}(k)r(k) \tag{20}\]  
Assuming that the observation length is \(L_{obs}\) and the number of auxiliary channels is 1, the estimation result of the noise variance of the auxiliary channel is as follows:  
\[\hat{P}_{n}=M_{1}=\frac{1}{L_{obs}}\sum_{l=1}^{L_{obs}}y_{l}y_{l}y^{*} \tag{21}\]  
If based on \(p\) auxiliary channels, the estimated noise \(M_{p}\) of \(p\) auxiliary channels can be calculated using \(p\) uncorrelated pseudo-noise sequence \(x(k)\), and its mean is the estimated value of \(P_{n}\)  
\[\hat{P}_{n}=\frac{1}{p}\sum_{i=1}^{p}M_{p} \tag{22}\]  
When the SNR changes at high speed, the number of auxiliary channels can be increased, and the length of observation can be shortened, so the noise estimation can become more accurate.
## 5 Vulnerabilities of SDN to LDDoS Attacks
### LDDoS Attack against Control Layer
#### 5.2.1 Saturation of Controller Resources  
As mentioned in background section for SDN, the controller is a centralized component in SDN. Although this offers many advantages of SDN, it represents a single point of failure [93]. An LDDoS attack can cause significant damage across the SDN by targeting the controller with a resource overload attack. This attack, similarly to the control channel bandwidth saturation attack, achieves its goal by taking advantage of a vulnerability in the standard SDN design. In order to deal with spurious packets, the SDN controller sends rules and policies packets to network elements such as OpenFlow switches to drop these packets and track the sources of the sent IP addresses, thus draining the controller's resources, such as CPU processing capabilities, RAM, physical memory, etc. [94].
## 3 Optical Error Analysis
### Simulation Experiment Results
#### 4.2.1 Broadening of the FPI Response
According to the Nyquist sampling theorem, the minimum sampling interval of the interferogram is \(\lambda_{\min}/2\), which, in this simulation, is 0.25 \(\upmu\)m. Based on this, if the FWHM of the OPD error function of the FPI optical defect is smaller than \(\lambda_{\min}/4\), the broadening effect can be considered negligible. With the current mature FPI manufacturing technology, the non-parallelism tilt angle is typically better than 1\({}^{\prime\prime}\), and the non-flatness is within tens of nanometers, usually meeting this threshold. However, for cases with large broadening caused by special conditions such as large aperture sizes, this issue can be addressed using the improved algorithm.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 2 Related Work
### Spatial-Temporal Information-Based Vehicle Re-Identification  
While local feature-based methods excel in capturing intricate details, some methods also attempt to explore spatial-temporal information to improve vehicle re-identification. For example, [PERSON] et al. [24] utilized the Markov random field chain to formulate candidate visual-spatial-temporal paths for pairs of vehicles, and then introduced a Siamese-CNN+Path-LSTM model to generate similarity scores for vehicle re-identification, which was the first work to introduce spatial-temporal cues for vehicle re-identification. [PERSON] et al. [25] constructed a maximum transfer time matrix between pairs of cameras, utilizing it to filter gallery images with transfer times exceeding the corresponding matrix values. [PERSON] et al. [10] observed that the same vehicle shared a smaller space distance and time interval, and they modeled the spatial-temporal relationship to re-rank vehicle re-identification results based on visual features. However, the Siamese-CNN+Path-LSTM model implicitly combined spatial-temporal and visual cues for vehicle re-identification, which lacked interpretability. Furthermore, relying on empirical assumptions or observations, the latter two methods roughly modeled spatial-temporal information of vehicles as constraints to filter unreasonable images, which still faced challenges in complex scenarios. On one hand, they performed limitedly because empirical assumptions or observations were not always accurate in complex cases. On the other hand, they separately utilized the visual and spatial-temporal cues for vehicle re-identification, and the improvement from introducing spatial-temporal information was ineffective. Instead, kernel density estimation (KDE) can flexibly capture deeper characteristics of spatial-temporal data distribution for vehicles by estimating the probability density function in a non-parametric manner. Consequently, leveraging KDE, we construct a fine-grained spatial-temporal probability and fuse it with a visual feature distance to improve vehicle re-identification.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 2 Background and Motivation
### Motivation to Use Airtime Concept  
To properly understand the aforementioned problem, it is essential to consider the interaction of carrier sense. When there is a frame transmission in a network (e.g., Network 1), the other that is out of the carrier-sensing range of Network 1 may also have a transmission. In that case, there is a possibility of having simultaneous transmissions in the networks. It is impossible to get the simultaneous transmission time from the previous analytical models, except the ones with the airtime concept. However, it is difficult to consider that the airtime concept is applied to WLAN analysis because the airtime concept was proposed for expressing the hidden node collisions and carrier-sensing relationships. The analysis of WLANs with airtime concept was proposed in [13], in which the network nodes are assumed to be in saturation and non-saturation heterogeneous conditions. Moreover, the analysis in [14] can express dynamics of various network topologies including multi-hop network and WLANs by using airtime concept. It is also effective to use another extended version of Markov-chain model to express the BT-decrement and frame-transmission states.  
However, the analysis in [13] is based on the assumption that there are all nodes in the same carrier-sensing range per one network. Ref. [14] and earlier works [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13] have a strict condition that is  
Figure 1: Scenario with inter-network interference.  
Figure 2: An exemplary effect of inter-network interference.  
the main reason for network throughput decreasing is the frame collision. The frame collisions do not occur in the context mentioned in Section 2.2. Therefore, it is necessary to consider the carrier sensing duration. More specifics, the reason of network throughput decreasing is only during the carrier sensing duration. We aim to use the airtime concept to newly express the carrier-sensing duration and the simultaneous transmission time for understanding carrier-sensing duration intuitively.
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 7 Experiments
### Comparison with the State of the Art  
Table 1 compares our proposed method with other competitors. The top part of Table 1 enumerates the performance of previous methods used as baselines. Except for the results of weighted query expansion (\(\alpha\)QE), other results are obtained by using our implementations. Due to the strength of diffusion in mining data manifolds, both diffusion methods achieve significant performance improvement to the k-NN search baseline. Although the proposed method is originally designed to approximate the offline diffusion, it outperforms offline diffusion by up to 3% in mAP. As mentioned in Section 7.2, the proposed method essentially replaces the original gallery feature with the feature reconstructed from its nearest anchors. This behavior is similar to database-side feature augmentation and is therefore beneficial to the performance [31].  
Figure 4: Illustration of extra computational costs for handling new data added to \(\mathcal{R}\)Oxford. We use \(s=10\) for local anchor embedding. (**A**) Comparison of extra runtime costs. (**B**) Comparison of extra pre-computational costs.  
To further demonstrate the ability of our proposed method to handle growing data, we simulate the data growth by only using a portion of the gallery set in pre-computation. For example, in Table 1, the notation (90% Gallery + 10% Growth) means that 90% of the gallery set is used to construct the anchor graph, and the remaining 10% serves as new data to be added into the gallery set. As we randomly split the gallery into two portions, we run the proposed method five times and the average mAP is used for comparison. Even though we observe that using the partial gallery causes obvious performance degradation compared to using the full gallery on \(\mathcal{R}\)Oxford, the performance is still competitive in comparison to previous methods. Interestingly, it seems that the data growth almost does not affect the performance on \(\mathcal{R}\)Paris. This is because there are fewer outliers on \(\mathcal{R}\)Paris since the retrieval performance of naive k-NN is already satisfactory. Overall, our proposed method outperforms the state of the art on both benchmarks and is capable of handling data growth at an affordable cost. As hypothesized in Section 1 (lines 69-72), the superior performance of our method is rooted in a rational modeling foundation, where anchor graph construction effectively approximates global data structure and LAE preserves local geometric consistency, thereby enabling robust handling of incremental data and directly validating this structural approximation capability.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
###### Abstract  
Wireless body area networks are increasingly featuring cognitive capabilities. This work deals with the emerging concept of cognitive body area networks. In particular, the paper addresses two important issues, namely spectrum sharing and interferences. We propose methods for channel and power allocation. The former builds upon a reinforcement learning mechanism, whereas the latter is based on convex optimization. Furthermore, we also propose a mathematical channel model for off-body communication links in line with the IEEE 802.15.6 standard. Simulation results for a nursing home scenario show that the proposed approach yields the best performance in terms of throughput and QoS for dynamic environments. For example, in a highly demanding scenario our approach can provide throughput up to 7 Mbps, while giving an average of 97.2% of time QoS satisfaction in terms of throughput. Simulation results also show that the power optimization algorithm enables reducing transmission power by approximately 4.5 dBm, thereby sensibly and significantly reducing interference.  
c 2017/17/80; doi:10.3390/s17040780
## 5 Conclusions  
In this study, we applied machine learning methods to classify the signals retrieved from lidar measurements in Iceland. The first challenge is to label the lidar data for the supervised machine learning algorithm. We used an unsupervised data clustering method, DBSCAN, and combined it with the conventional threshold-based method and manual correction. We applied the method to classify four days of data obtained during two dust events in 2019. Subsequently, we use this labeled data set to train two models, the noise discrimination model and the classification model with the RF algorithm. With these two trained models, we can accurately identify the noise data and classify the lidar data into eight classes, including three types of aerosols, two types of clouds and rain. For all classes except rain, the true positive rate is higher than 94%; however, the model is underpredicting rain. In most cases, the model can identify rain correctly but cannot identify all rain data points. The accuracy of model prediction is directly related to labeling accuracy. With more accurately labeled lidar data, the classification model can be further improved. A larger data set with more and different weather conditions might also improve the performance of the model. In addition, the calibration and correction of lidar data can also affect the results. We found an artifact layer at around 600 m due to the lack of focal correction of the lidar depolarization channel in some cases.  
This method was tested on different data sets from different dates and events, as well as from two identical Doppler lidars in Iceland, and the results remained valid and robust. This suggests that the machine learning method can be used for the classification of lidar measurements.  
Machine learning algorithms depend on the training data sets. Based on our results, we conclude that a continuous application of new training data would improve the results further. Accordingly, the presented algorithms may be useful to interpret in real-time lidar observations and provide valuable information to end-users, such as aviation service providers and air quality observers.  
Conceptualization, S.Y., F.P., G.N.P., S.v.L. and D.C.F.; methodology, S.Y. and F.P.; programming, S.Y.; resources, G.N.P., S.v.L. and D.C.F.; data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, F.P., G.N.P., S.v.L. and D.C.F.; visualization, S.Y.; supervision, D.C.F., G.N.P. and S.v.L.; project administration, D.C.F.; funding acquisition, D.C.F. All authors have read and agreed to the published version of the manuscript.  
This study is funded by ISAVIA, the aviation authority in Iceland.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Experimental Environment and Data
### Experimental Data  
In this study, Chinese land use patch data were selected as the experimental data. The data were downloaded from OpenStreetMap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 1 July 2023). The data contain a variety of polygons, including simple polygons, concave polygons, polygons with holes, and self-intersecting polygons. These data have the general characteristics and application value of surface geographic data in overlay analysis. Moreover, to verify the feasibility of the method proposed in this paper, eight subject polygons of different shape complexities and one clipping polygon in the Chinese land use patch data were selected for overlay analysis. The experimental data are shown in Figure 1.