esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 1 Introduction
Beyond that, this paper examines the effectiveness of the algorithm on a cleaning vehicle, studies the variation of velocity and foresight distance in the tracking, and evaluates its performance by the distance deviation of the straight line and the turning section in the field experiment. The real vehicle experiment result shows that the proposed controller is efficient not only in tracking the pre-defined path points smoothly but also in not affecting by the path noise points. The rest of this article is structured as follows. In Section 2, we briefly review the technical background of the vehicle model, stability condition and the chain structure of LSTM. Section 3 describes the proposed tracking algorithm, which includes the discrimination of the foresight distance, the system description and the detailed network structure. In Section 4, the simulation and field experiments results are presented and Section 5 concludes this paper.
## 3 Experimental Results
### Transient Response  
Transient mid-point displacement versus time data from the tests on 120\({}^{\circ}\) V-structures are shown in Figure 13. Unfortunately, the very early response (the first 0.1 ms) is missing due to motion blur in the images, which prevented clear displacement measurements. There are some small \"gaps\" in the post-peak vibration, which is evident in one of the 15 g detonations, caused by particulates obscuring the field of view for one of the cameras.  
Figure 12: Graph of permanent midpoint displacement vs. charge mass obtained from the blast tests.  
Figure 13: Graph of transient midpoint displacement vs. time obtained from blast tests on 120\({}^{\circ}\) V-structures, 32 mm tip radii. Circles indicate the discrete times for the selected deformation profiles.  
Despite these challenges, the peak displacements for each test were captured, occurring after approximately 1 ms. The rebound and post-peak elastic vibration phases were also evident. The difference between the peak and the permanent displacements was in the order of several plate thicknesses. The close similarity between the two 15 g detonations indicated good repeatability of the response.  
Similar responses were obtained from the 105\({}^{\circ}\) V-structures, shown in Figures 14 and 15: motion blur and obscuration caused similar gaps in the data. The gaps were more significant for 20 g detonations, probably due to the higher displacement magnitudes (and higher initial velocities). The 105\({}^{\circ}\) V-structures exhibited a less distinct peak displacement and lower rebounds, probably due to the difference in damage mode.  
Figure 14: Graph of transient midpoint displacement versus time obtained from the blast tests on 105\({}^{\circ}\) V-structures with a 32 mm tip radius.  
Figure 15: Graph of transient midpoint displacement versus time obtained from the blast tests on 105\({}^{\circ}\) V-structures with 62 mm tip radii (circles indicate the discrete times for the selected deformation profiles).  
The transient displacement profiles along the ridge plane and the final deformed ridge profiles for selected 105\({}^{\circ}\) V structures are shown in Figure 16. The transient deformation along the ridge varied only in magnitude and not in shape for a given charge mass and V-structure type. The deformed region of the ridge increased with increasing charge mass, was higher for the larger internal angle (120\({}^{\circ}\) V structure), and was larger in the 105\({}^{\circ}\) V structures with the 62 mm tip radius than for the smaller tip radius of 32 mm.  
The transient cross-cut response (not shown) was very similar to the permanently deformed profiles shown in Figures 8-10, with small increases in out-of-plane displacement for increasing internal angle and tip radius. As expected, the transient displacement initially increased rapidly and then oscillated about the deformed displacement values.
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd  
### Dynamic Traffic Distribution in Outdoor Dynamic Crowd  
In dense urban scenario which is one of the important scenarios in 5G, network densification is necessary because of the exponentially increasing traffic volume. Such traffic is generated not only by smart phones and tablets but also by sensors and wirelessly connected cameras which produce e.g., augmented reality information etc. Especially, this paper focuses on a so-called outdoor dynamic crowd scenario where users tend to gather and move as large and dynamic crowds while want to keep connectivity to the cloud. Typical use case of the scenario is a medium outdoor area located in the metropolitan city centre where thousands of people may spend part of their daily life. The area is characterized by a several possible outdoor hotspots like bus stops, stations and recreation parks. Users at such outdoor hotspots might download large volume contents, such as tourist and shopping information, high definition 3D live broadcast of a game happening at a stadium nearby, or upload and share through SNS photos and videos recently taken near the place. They can also download multi-language information, 3D indoor maps, shopping promotion video clips, 4K/8K live videos etc. for better shopping experience at the next destination nearby e.g., shopping departments around center stations. The key difference of this use case compared to the other ones is that the traffic pattern changes very dynamically during a day, in accordance to users' activities, e.g., from light to heavy traffic. It necessitates dynamic formation of backhauling toward high data rate mmWave SC-BSs. [PERSON] et al. [15] demonstrated a meshed backhauling example of such scenario around at a famous intersection in the periphery of a metropolitan train station in central Tokyo.  
The measured downlink traffic in 2014 in an area of 4 squared kilometers [27] in the periphery of a metropolitan train station in central Tokyo are visualized in Figures 1 and 2. In this measurement, the average traffic demand per user was about 62 kbps and total area traffic at peak hour was 44 Mbps. The spatial traffic distribution accumulated over one hour 10 AM is presented in Figure 1. The figure reveals the non-uniformity of traffic distribution in space domain and especially, there exist several hotspots. On the other hand, the time variation of the sum traffic in this area is depicted in Figure 2. Since the evaluated area is the periphery of an urban station, it is easy to observe from the figure light traffic at midnight while extremely heavy traffic at daytime inversely. The measurement data reveal dynamicity of traffic in both time and space, thus necessitates dynamic formation of meshed backhauling for efficient operation. Such method will be described in the next section, assuming the measured traffic is scaled by a factor of 1000\(\times\)[16] as predicted in 5G era.
## 4 Results
### Reduction of Energy Losses for 24 h
#### 4.2.2 Distribution System of 69 Nodes  
Figure 5 shows the results obtained for the scenarios set out in Table 5, in this test system, with a solution vector in terms of location of {17, 61, 64} and sizing of {0.7735, 2.0000, 0.5835} MW. It displays that, as in the 33-node system, in the hours in which there is active power generation (7:00-20:00) by the PV systems, there is a reduction in energy losses. Meanwhile, in the hours where there is no active power injection generated (1:00-7:00 and 20:00-24:00), scenario (3) has similar behavior scenario (1).  
On the other hand, as in the 33-node system, from 11:00-15:00, scenario (3) presents a higher reduction in energy losses than scenario (2). Due to searching for the optimal energy reduction vector, it may be oversized for those hours since the capacity of the PV system changed according to the demand hour by hour. This may not be beneficial because the losses could increase beyond expected-- not as the demand increased but as a surplus generation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & \(\mathbf{E_{\text{loss}}}\) **(kWh/day)** \\ \hline (1) & 2508.6343 \\ (2) & 1199.4596 \\ (3) & 1922.5098 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Power losses for 33-node system scenarios.  
Figure 4: The behavior of power losses for each hour of the study period in the 33-node system.  
Table 7 shows the results of energy losses for the three scenarios proposed. With the DCVSA algorithm, a reduction in energy losses of 24.3863% was obtained when considering the up-and-down behavior of the PV systems concerning scenario (1). It is also possible to analyze that scenario (2) had a reduction in energy losses of 54.8861 % regarding scenario (1); however, this result is hypothetical because, as mentioned above, the production availability of PV systems is not ideal.
## 2 Background
### Related Work
The DCR technique is used to re-order at run-time the cluster scheduling. The clusters involved in the data streams are re-ordered, in order to minimise the traffic latency. This re-scheduling is performed based on the priority of the data streams, in order to decrease end-to-end communication delays. In turn, the DBR technique is used to change the superframe duration of the clusters, increasing the bandwidth of the clusters involved in the message stream. This technique uses the free space (not used to allocate clusters) and distributes it among the involved clusters. If there is no available free space, it tries to reduce the bandwidth of the non-involved clusters, in order to re-distribute it among the involved clusters. Unfortunately, this communication approach only considers the use of cluster-tree paths. While TDBS and TDCS approaches just address upstream communication traffic (convergecast pattern), [PERSON] and [PERSON] [16] proposed an efficient beacon scheduling approach to support low-latency upstream and downstream traffic. The authors formulate the Low-latency Two-way Beacon Scheduling (LTBS) problem for ZigBee networks. In this problem, nodes try to get slots for upstream and downstream traffic, while avoiding interferences from other clusters. LTBS modifies the IEEE 802.15.4 original superframe structure, in order to allow each cluster-head to broadcast two beacons: one for upstream direction and another for downstream direction. The authors propose two algorithms (centralised and distributed) to assign interference-free upstream and downstream slots to reduce the network latency. Basically, the difference between centralised and distributed approaches is the sequencing order. The centralised algorithm assigns slots using a bottom-up approach. On the other hand, the distributed algorithm assigns slots using a top-down approach. This proposal minimises the network latency without considering any message stream prioritisation. Although improving the performance of cluster-tree networks, the efficiency of the proposed communication schemes is upper-bounded by the cluster-tree routing scheme and its inherent problems, such as: network congestion near the PAN coordinator [19], poor bandwidth allocation to the different clusters, and higher energy consumption of crucial nodes.
## 2 Methodology  
The proposed methodology is designed into four steps: (1) the AE data acquisition from a multisensory testbed, (2) feature extraction from AE, time, and frequency domain to create the HBoF, (3) feature selection by Boruta, and (4) k-NN-based multiclass-classification. Figure 1 demonstrates the total proposed approach.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## Appendix D Proof of Theorem 4  
Denote a random algorithm as \(\Theta\). We prove the optimality of the DTEM algorithm by comparing \(\Delta_{V}(t)\) with the algorithm \(\Theta\). Furthermore, variables generated by each algorithm will be tagged by DTEM and \(\Theta\), respectively. According to the Lyapunov optimization theory proposed by [PERSON] [42], the upper bounds of algorithm \(\Theta\) are as follows,  
\[\begin{cases}\mathbb{E}\bigg{[}\sum_{n\in\mathcal{N}}U(\varphi_{i}^{\Theta}(t) )\bigg{]}&\leq\mathcal{H}^{*}+\zeta,\\ \bigg{|}\mathbb{E}\bigg{[}\sum_{n\in\mathcal{N}}\left(\varphi_{i}^{\Theta}(t) -\sum_{j=1}^{N}\delta_{ij}^{\Theta}(t)\gamma_{i}^{\Theta}(t)\right)\bigg{]} \bigg{|}&\leq\eta_{1}\zeta,\\ \bigg{|}\mathbb{E}\bigg{[}\sum_{n\in\mathcal{N}}\left((E_{i}^{h})^{\Theta}(t) -(E_{i}^{c})^{\Theta}(t)\right)\bigg{]}\bigg{|}&\leq\eta_{2}\zeta,\end{cases} \tag{12}\]  
where \(\zeta\) is positive and infinitesimal, and \(\eta_{1}\), \(\eta_{2}\) are constants.  
According to (12) and (12), we minimize \(\Delta_{V}(t)\) in (14) and have  
\[\begin{split}\Delta(t)-V\mathcal{H}(t)&\leq\mathbb{ E}\Big{[}\Gamma_{V}^{DTEM}(t)|\Pi(t)\Big{]}+B\\ &\leq\mathbb{E}\Big{[}\Gamma_{V}^{\Theta}(t)\Big{]}+B\\ &\leq(\eta_{1}+\eta_{2}+1)\zeta-V\mathcal{H}^{*}+B.\end{split} \tag{13}\]  
Then, by setting \(\zeta\) to a minimum value, (13) can be simplified to  
\[\Delta(t)-V\mathcal{H}(t)\leq B-V\mathcal{H}^{*}. \tag{14}\]  
Take the expectation (14), (14) can be deduced as  
\[\frac{1}{T}(L(T-1)-L(0))-\frac{1}{T}\sum_{t=0}^{T-1}\mathbb{E}[\mathcal{H}(t) ]\leq B-V\mathcal{H}^{*}. \tag{15}\]  
Since \(L(t)\) is finite, by \(T\to\infty\), we have \(\overline{\mathcal{H}}\geq\mathcal{H}^{*}-B/V\).
## 6 Performance Analysis
### Performance Metrics  
For a proper evaluation of the proposed methods, three commonly used performance metrics are used:  
* **Miss rate probability:** This metric is the ratio between the number of failed devices or devices exceeding the maximum re-transmission limit or time constraint and the total number of devices \(N\) attempting to access the network via a RACH procedure.
* **Total energy consumption:** This metric is the total energy consumed for \(N\) devices performing a two-step RACH mechanism attempt.
* **Overall RACH access delay:** This metric is the average time for N successful data packet receptions started by the first RACH attempt. We consider the maximum re-transmission to be 20 and the back-off time to be 20 ms.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & Number of devices & 1000 to 5000 \\ \hline \(K\) & Number of different tasks & 10 and 40 \\ \hline \(w\) & Number of preambles & 54 \\ \hline - & Tasks per application run & 1 to 5, uniform distribution \\ \hline - & Tasks per device support & 1, 3, 5, uniform distribution \\ \hline \(M\) & Number of applications & 20 \\ \hline \(\tau_{m}\) & Time constraint of an application & 2\% of inter-arrival time \\ \hline \(\frac{1}{\lambda_{m}}\) & Mean of application inter-arrival time & 30 min (5\%), 1 h (15\%), 2 h (40\%), 24 h (40\%) \\ \hline \(t\) & Device task execution time & uniform (0.1, 0.2) s \\ \hline - & Targeted simulation time frame & 1 RAO \\ \hline \(R_{max}\) & Maximum re-transmission & 10 \\ \hline \(T_{back}\) & Back-off time & 20 ms \\ \hline \(C\) & Energy consumption per RACH attempt & 264 \(\mu\)J [58] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### Q-learning Algorithm Convergence  
To demonstrate the effectiveness of our Q-learning algorithm, we observed the average reward of Q-learning with respect to trading episodes. The Q-values are stored in the Q-table and updated during the training process of the algorithm. The Q-table is initialized with zeros. We considered that the Q-learning algorithm is implemented by the tenant with \(\alpha=0.1\) and \(\gamma=0.9\). To speed up the convergence rate, \(\alpha\) can be set closer to 1; however, a higher value of \(\alpha\) would lead to local optimization. We also employed the \(\epsilon\)-greedy strategy to make sure that all state-action pairs are explored enough before converging to a particular action. Since the value of exploration was high at the beginning, we defined \(\epsilon\) as a linear function of the episode. In other words, we set the initial value of \(\epsilon\) to 1, and \(\epsilon\) approached 0 as the episode number increased. This was only done for the first 50% of the episodes, whereas \(\epsilon\) was 0 for the rest of the episodes. This was to ensure that every action was performed a sufficient number of times before converging to a particular action.  
Figure 3 illustrates the convergence speed of the Q-learning algorithm with the average reward and standard deviation (red region) obtained per episode during the learning process. We assumed that the system had reached convergence when the mean of all Q-values did not change over time. It was observed that Q-values started from a low initial reward level but quickly converged to a competitive trading strategy with a satisfying reward performance within hundreds of episodes. This result shows that Q-values are successfully learned after a certain number of episodes and can achieve better resource trading than that without any prior training.  
Figure 3: The average reward over episodes using Q-learning with the parameters, \(\alpha=0.1\), \(\gamma=0.9\).
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 3 Proposed Lightweight Model for Intrusion Detection
### Feature Selection  
To assess the suggested model in the paper without encountering overfitting, important characteristics from the input dataset were added. However, selecting a large number of features for a traditional ML classifier increases the risk of overfitting. To address this, a novel feature selection approach called ExtraTreeClassifier was developed to include key features in the model development. Twenty critical features were selected from both datasets, and the remaining features were discarded. The ExtraTreeClassifier algorithm is significant for detecting DDoS attacks due to its ability to handle high-dimensional data with both categorical and numerical features without requiring feature scaling. It effectively addresses imbalanced datasets, which is crucial in DDoS attack detection where attack instances are typically fewer than normal instances.  
The algorithm's computational efficiency makes it suitable for real-time or high-speed network environments, enabling prompt detection of DDoS attacks. It excels at capturing complex non-linear relationships between features and enabling accurate identification of DDoS attack patterns amidst normal traffic. The algorithm's resistance to overfitting minimizes false positives and negatives in detecting DDoS attacks. With the capacity to handle large feature sets, the ExtraTreeClassifier enables comprehensive analysis of datasets with numerous network traffic attributes, enhancing the effectiveness of DDoS attack detection. Its ensemble nature and randomness in feature selection make it robust against noisy or irrelevant features, thereby improving overall performance and reliability.  
Easy implementation and integration into existing network security systems make the ExtraTreeClassifier convenient for deployment in real-world scenarios, facilitating the utilization of DDoS attack detection. By evaluating the qualities based on their Gini importance from least to most important, the top K attributes are chosen by the user. The relevance of the top 15 features, as shown in Figure 4, from the TON-IOT and BOT-IOT datasets is compared using ExtraTreeClassifier from the sklearn.ensemble module.  
Figure 3: (a) Correlation matrix of BOT-IOT; (b) correlation matrix of TON-IOT.
## 3 System Model and Problem Formulation
### Transmission Model of CB
According to Equation (12), the received signal at the intended BS\({}_{0}\) can be expressed as  
\[s(\varphi_{0},\textbf{cP})=\sum_{k=1}^{O}m_{k}c_{k}\sqrt{P_{k}}h_{k0}+\sum_{i=1 }^{F}m_{i}c_{i}\sqrt{P_{i}}h_{i0}+\omega. \tag{14}\]  
For the unintended BSs, the received signal can be written as  
\[s(\varphi_{l^{\prime}},\textbf{cP})=\sum_{k=1}^{O}m_{k}c_{k}\sqrt{P_{k}}h_{il^{ \prime}}e^{i\frac{2\pi}{\lambda}(A_{l^{\prime}}-r_{k}\cos(\varphi_{l^{\prime}} -\varphi_{k}))}+\sum_{i=1}^{F}m_{i}c_{i}\sqrt{P_{i}}h_{il^{\prime}}e^{i\frac{2 \pi}{\lambda}(A_{l^{\prime}}-r_{i}\cos(\varphi_{l^{\prime}}-\varphi_{l}))}+\omega \tag{15}\]  
where \(l^{\prime}\
eq 0\). Correspondingly, the received interference power at the unintended BSs is given by  
\[I(\varphi_{l^{\prime}},\textbf{cP})=|\sum_{k=1}^{O}m_{k}c_{k}\sqrt{P_{k}}h_{ k^{\prime}}e^{i\frac{2\pi}{\lambda}(A_{l^{\prime}}-r_{k}\cos(\varphi_{l^{ \prime}}-\varphi_{k}))}+\sum_{i=1}^{F}m_{i}c_{i}\sqrt{P_{i}}h_{il^{\prime}}e^{ i\frac{2\pi}{\lambda}(A_{l^{\prime}}-r_{i}\cos(\varphi_{l^{\prime}}-\varphi_{l}))}|^{2} \tag{16}\]  
According to Equation (15), the signal-to-noise ratio (SNR) at the intended BS\({}_{0}\) can be calculated as  
\[\Psi_{k}=-\frac{2\pi}{\lambda}d_{k}(\varphi,\theta) \tag{9}\]0  
The interference-to-noise ratio (INR) at all unintended BSs can be written as  
\[\Psi_{k}=-\frac{2\pi}{\lambda}d_{k}(\varphi,\theta) \tag{9}\]1
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology  
### Data  
The data used in our research consist of cry audio recordings obtained from the donate-a-cry Corpus [19]. It comprises baby cries recorded by volunteers using their cell phones through a mobile application and made publicly available through a GitHub repository. The dataset initially consisted of 1128 audio recordings, divided into nine classes (hungry, needs burping, belly pain, discomfort, tired, lonely, cold/hot, scared, don't know). However, due to labeling inconsistencies caused by volunteers, the research group performed data cleaning, reduced the dataset in size, and used the Dunstan Baby Language (DBL) [17] categories as a reference. The dataset includes a total of 462 recordings covering five classes of baby cry analysis: belly pain (17 recordings), burping (9 recordings), discomfort (28 recordings), hungry (383 recordings), and tired (25 recordings). Moreover, each record also contains information about the gender (male, female) and age of the baby (0-4 weeks, 4-8 weeks, 2-6 months, 2 months-2 years, or more). The recordings vary in length from 3 to 6 s and are available in wav format. The sampling frequency is 8000 Hz, and the bit rate is 128 kbps.