esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 3 Theoretical Framework of the IoT OS Platform
### Structured Organization Mechanism for IIoT
#### 3.1.3 Platform Layer  
As previously defined, the IoT OS platform's role is to manage various entities downwards and provide service support for IIoT applications upwards. By implementing core access capabilities, the platform layer can act as a key infrastructure for data carrying.  
However, naive IoT access capabilities are not sufficient to support the business needs of an entire IIoT solution. Currently, under the push of Industry 4.0, more scenarios are presenting application demands for intelligence, security, real-time, etc. As the carrier of IIoT applications, the design style and capability boundaries of the platform layer directly affect the quality of the overall solution.
## 2 Method
### Experimental Setting  
The ECG-SPP-net was evaluated by comparing the overall accuracies of the proposed method and the other two methods. We used the same denoising method for the three network structures and the same classifier (Softmax). Three network structures are shown in Table 3, where \"Y\" indicates adoption and \"N\" indicates none.  
Method 1: Heartbeats with different sizes were unified into 300 sampling points [14]. In the MIT-BIH arrhythmia database, the ECG signals were bandpass filtered at 0.1-100 Hz and digitized at 360 Hz. Then, a beat of ECG was resampled to 300 sample points by downsampling or upsampling according to the duration of a heartbeat. The processed heartbeat was sent to the CNN for training. The parameters of layers 1, 2, 3, and 5 of the network were the same as in Table 1. The SPP in layer 4 was removed and alternated to the largest pooling strategy, setting both the pooling size and the step size to be 2.  
Method 2: The unified size of this method was the same as in Method 1. The processed heartbeat was sent to ECG-SPP-net. The parameters in each layer were the same as in Table 1.  
Proposed method: The number of sample point for each heartbeat was not considered, and as such, the heartbeat was sent to the ECG-SPP-net directly after pre-processing.
## 4 Life Cycle Analysis (LCA) Comparison between Batteries and PHES
### LCA of Batteries  
When conducting a Life Cycle Analysis (LCA) of batteries, the assessment encompasses a multifaceted exploration of their environmental impact, commencing with the sourcing of critical minerals like lithium, cobalt, and nickel. This scrutiny extends to the energy-intensive manufacturing processes integral to battery production. Moreover, the LCA delves into the carbon footprint associated with battery operation and underscores the complexities linked to recycling or disposing of batteries at the end of their life cycle, typically spanning 10 to 20 years for rechargeable variants. Special emphasis is placed on identifying potential environmental hazards posed by specific battery chemistries during their end-of-life phase, ensuring comprehensive evaluation and mitigation strategies to address these challenges effectively.
## 4 Results
### Performance Evaluation of the DQN-Based Strategy
#### 4.4.2 Statistics on the MC Configuration  
As explained previously, the goal of the proposed algorithm was to find the MC configuration that maximizes the reward under any experienced conditions by the UE. If we look at the evolution of \(\beta_{m,n}\) in the detected cells by the UE during the analyzed period in Figure 6b we see that at some particular moments the DQN-based algorithm decided to send all the user traffic through a single cell. This means that during these moments, the algorithm decided that, based on the current state, not doing MC would result in a better  
Figure 5: CDF of the throughput achieved by the UE of interest.  
Figure 6: (**a**) SINR evolution of the LTE and NR cells in the analyzed period; (**b**) evolution of \(\beta_{m,n}\) in the detected cells by the UE during the analyzed period and (**c**) reward in the analyzed period.  
performance, and this is confirmed through the reward observed in Figure 6c. Based on this, in the following we assess the decisions made by the algorithm in relation to when to apply MC and which are the technologies involved.  
If we consider that the evaluation scenario has two LTE cells (C\({}_{1,1}\) and C\({}_{1,2}\)) and four 5G NR cells (C\({}_{2,1}\), C\({}_{2,2}\), C\({}_{2,3}\), C\({}_{2,4}\)), with \(N_{max}=2\), this means that the MC configuration is can be done with five possible connection modes, namely _no MC with LTE; no MC with NR; MC with LTE-LTE; MC with LTE-NR and MC with NR-NR_. Figure 7 presents the percentage of time that the algorithm selected each one of these modes for the same 100 trajectories studied in Section 4.4.1. It is observed that during around 56% of the time the algorithm decided not to do MC and to connect instead to only one cell of LTE or one cell of 5G NR. In contrast, during the remaining 44% of the time it decided to do MC, particularly being the combination LTE-NR, the most used approach selected in around 38% of the time. This is logic given that LTE cells have a bigger coverage area than the NR cells, but these have more bandwidth. Therefore, this connection mode could be helpful at the time of fulfilling; for example, the UE data rate requirements while the UE is moving. Given the statistics presented in Figure 7 and after observing previously that the DQN-based solution performs much better that the SINR-based one, the importance of deciding the correct connection mode and the multi-connectivity configuration when required is clear.
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 The Structure of the AoT-DCGAN Model  
In this study, we introduce an AoT-DCGAN model, composed of a two-dimensional DCGAN model and an asynchronous optimization (AO) strategy. As illustrated in Figure 4, the AoT-DCGAN's generator includes six two-dimensional deconvolutional layers, while the discriminator is composed of five two-dimensional convolutional layers. The generator inputs a 100-dimensional random noise vector \(z\) and up-scales it through a series of deconvolutional layers to generate data of the same size as the real samples [34]. The discriminator's process is similar to that of a convolutional neural network performing a classification task. It down-samples and extracts features from both real and synthetic samples, and the output layer produces a scalar of dimension \(1\), indicating the input sample's probability of being real. A higher output value close to \(1\) suggests a more realistic sample.  
Figure 3: Workflow of proposed hybrid deep learning model.  
Figure 2: Typical PTCs curves under different make-up conditions: (**a**) standard PTCs curve; (**b**) misalignment PTCs curve; (**c**) thread damage PTCs curve.  
For the generator and discriminator to reach a Nash equilibrium state, in which the generator produces realistic samples indistinguishable by the discriminator from real specimens, and in which the discriminator performs well in distinguishing the generated samples, the generator and discriminator's weights require separate optimization using different algorithms. The discriminator's objective is to minimize the loss function calculated using the binary cross-entropy function for both real and synthetic samples, as shown in Equation (1). Conversely, the generator aims to minimize the negative logarithm of the probability of fake samples classified as real by the discriminator, as shown in Equation (2). During the training process, these results are used to update the weights of the corresponding neurons in the network through backpropagation.  
\[L_{discriminator}=-\log D(real\_sample)-\log(1-D(G(fake\_sample))) \tag{1}\]  
\[L_{generator}=-\log(D\Big{(}G\Big{(}fake\_sample\Big{)}\Big{)}) \tag{2}\]  
where \(D\) and \(G\) represents the discriminator and generator; \(x\) is the real sample from the training dataset, and \(z\) is a random noise input. \(G\) (\(z\)) maps \(z\) to the data space to fit the statistical distribution of the real sample, while \(D\) (\(x\)) obtains the probability of the input data.  
where, the _loss_(_i_) represents the loss function of the discriminator in the _i_-th iteration, and _l_(_i_) represents the weight parameters in the _i_-th iteration. The variable \(n\) represents the number of iterations for weight updates, while we set \(n\) as 4 in this study.
## 5 Results
### Queue Length  
Under conditions where \(\lambda\) is set to 0.2 veh/s, 0.3 veh/s, and 0.4 veh/s, with \(V_{\text{max}}\) of 60 km/h, the simulated queue lengths for left-turn and straight lanes per signal cycle with \(t_{y}\) of 3 s, 4 s, and 5 s, corresponding to Groups 1, 2, and 3, are shown in Figure 8a-c. Similarly, under conditions where \(V_{\text{max}}\) is set to 50 km/h, 60 km/h, and 70 km/h and \(\lambda\) is 0.3 veh/s, the statistical results for queue lengths in left-turn and straight lanes per signal cycle with \(t_{y}\) of 3 s, 4 s, and 5 s, corresponding to Groups 4, 5, and 6, are shown in Figure 8d-f. As the simulation includes 30 signal cycles, each box plot contains 30 data points with each representing the queue length (veh) within an individual signal cycle.  
As shown in Figure 8c, under high traffic volume conditions (\(\lambda\) = 0.4 veh/s), the queue lengths per signal cycle when \(t_{y}\) is set to 4 s or 5 s is slightly longer compared to that when \(t_{y}\) is set to 3 s in most cycles. That is, for \(t_{y}\) of 3 s, 4 s, and 5 s, the average queue lengths per cycle in the left-turn lane are 4.6 veh, 5.1 veh, and 6.6 veh, respectively, while in the straight lane, the average queue lengths per cycle are 4.6 vehicles, 5.4 vehicles, and 7.0 vehicles, respectively.  
Similarly, as seen in Figure 8f, when the speed limit is relatively high (\(V_{\text{max}}\) = 70 km/h), the queue lengths per signal cycle are slightly longer at \(t_{y}\) of 4 s and 5 s than at 3 s in most cycles. Specifically, for \(t_{y}\) of 3 s, 4 s, and 5 s, the average queue lengths per cycle in the left-turn lane are 3.2 veh, 4.0 veh, and 4.4 veh, respectively, while in the straight lane, the average queue lengths per cycle are 3.1 veh, 3.6 veh, and 4.9 veh.  
Additionally, as shown in Figure 8a,b,d,e, when the traffic volume is low, and when \(V_{\text{max}}\) is set to 50 km/h and 60 km/h, yellow light duration has no significant impact on queue length.
## 2 Terrain Model
### Material Classification  
The materials layer (\(M\)) stores information about distribution of terrain materials and its basic form is correlated to the model geometry (Figure 3). If \(mat\) is a given number of materials assigned to the model, such that \(1\leqslant mat\leqslant\infty\), and \(mat\in\mathbb{N}\), then each point \(p\in M\) can be assigned to a material index where \(M(p)\) means element in the point \(p\):  
\[M(p)=\Big{\lfloor}\frac{\big{\lceil}2(mat-1)G(p)\big{\rceil}}{2}\Big{\rfloor} \tag{5}\]  
The material layer obtained by the CFF method enables smoother transitions between areas occupied by the individual materials. In contrast, the LFF method is characterised by high irregularity and fraying of the results. The comparison of materials' distribution for both methods is presented in Figures 8 and 9.  
Materials classification is obtained by a quantification of the height values stored in the geometric layer. As an alternative solution, the end-user can choose separately generated fBs as the foundation for the materials classifier. With this approach, materials will not directly correlate with the geometric layer but in relation to the purpose of the article this correlation is preferred.  
Figure 8: Materials layer generated by the Circular Fault Formation (CFF) method and interpreted as a grey-scale map (grid resolution: \(1024\times 1024\); fault radius (each): \(r=256\); fault height (max in centre): \(0\leqslant\Delta h\leqslant 1\); total faults: 9999). (**a**) Geometric layer. (**b**) Initial map. (**c**) \(mat=3\). (**d**) \(mat=5\). (**e**) \(mat~{}=~{}7\).
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Overall Structure of the Network  
The aim of the network proposed in this paper is to estimate the 6D pose of objects from RGB-D images. The 6D pose of an object refers to the rigid transformation from the object's coordinate system to the camera coordinate system. This transformation can be intuitively expressed as a combination of a rotational and translational transformation. It is represented using a homogeneous transformation matrix \(P=[R,t]\), where \(R\in SO(3)\) is the rotational transformation and \(t\in R^{3}\) is the translational transformation.
## 4 Conclusions and Outlook  
In this review, we discussed nanostructured gasochromic WO\({}_{3}\) for the detection of hydrogen gas. The effects of substrate, crystallinity, preparation method, and morphology on the sensing properties of these sensors were discussed and explained. Gasochromic WO\({}_{3}\) gas sensors need noble metals such as Pd, Pt, and Au for the catalytic dissociation of hydrogen molecules and rapid fast detection of hydrogen gas. Most gasochromic sensors work at low or room temperatures and, therefore, significantly decrease the risk of explosion of hydrogen gas. Furthermore, they can be fabricated on flexible substrates and, hence, can be used for detecting the leakage of hydrogen gas from pipelines. However, gasochromic sensors generally exhibit a poor performance at low H\({}_{2}\) concentrations, under which conditions the color change is very slow or cannot be detected by a naked eye. Therefore, for very low concentrations of hydrogen gas, resistivity-based gas sensors may be a better choice. For practical applications of gasochromic sensors, color changes in gasochromic WO\({}_{3}\) gas sensors should be visible to the naked eye. To achieve this objective, high-performance gasochromic sensors with a high surface area, engineered design, and appropriate amount of noble metals should be synthesized using novel protocols. Such gas sensors not only exhibit color changes in the visible light range, but also show fast dynamics, which are vital for the detection of hydrogen, an explosive gas.  
Conceptualization, S.S.K., Writing-Original Draft Preparation, A.M. and J.-H.K., Writing-Review & Editing, H.W.K. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A6A1A03013422). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03935228). The authors declare no conflict of interest.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 6 Evaluation  
We demonstrate the capabilities of our system using a recording of six ceiling-mounted Azure Kinect DK cameras [70] arranged as a ring, using the NFOV_UMBINED mode on the depth camera and the maximum resolution of 640 \(\times\) 576. The recording captures a room of 5.88 m \(\times\) 7.15 m for 59 s, where three persons walk around a surgical table with a dummy head placed on top, and all persons enter and leave the captured domain multiple times. All performance measurements were recorded on an Ubuntu 18.04 server with 2\(\times\) Intel Xeon Gold 5120 14 core CPUs and an Nvidia RTX A6000 GPU. Our build was compiled using GCC 7.5 and CUDA 11.1.  
For our testing, the configuration parameters of the temporal filter [63] are \(\alpha=0.15\), \(\delta=3\) cm, and persistence at 3 (valid in last 2 of 4). For mesh extraction, we use 20 CPU threads and allow a maximum angle difference for vertex normals of 120\({}^{\circ}\), and the parameters to control vertex merging across a splitting plane are \(\delta_{n}=\delta_{d}=30^{\circ}\). While these parameters are constant, we will evaluate four different sets of voxelization and simplification settings. The parameter set _unsimplified-low-detail_ uses a 2 cm voxel size and reconstructs the surface samples using principal component analysis across a neighborhood radius of 1.5 cm. The preset _simplified-low-detail_ adds simplification with a QEF error threshold of \(1\times 10^{-3}\) and normal standard deviation \(\sigma_{n}=0.15\). The third preset, _simplified-low-detail_ adds simplification with a QEF error threshold of \(1\times 10^{-3}\) and normal standard deviation \(\sigma_{n}=0.  
high-quality_, changes the local surface estimation method to moving least squares [50], fitting a second-degree bivariate polynomial surface to the local neighborhood. Finally, the preset \(LOD\) uses MLS reconstruction with a neighborhood size of 1.5 cm only for the OR table in an axis-aligned box with dimensions of 95 \(\times\) 38 \(\times\) 180 cm and a simplification threshold of \(1\times 10^{-4}\). The region outside the box uses voxels that are twice as large with 4 cm and performs local surface estimation using PCA with 2.5 cm neighborhood radius and a simplification threshold of \(1\times 10^{-3}\).
## 5 Image Compression
Thus, based on the above investigation, we have used the modified PackBits algorithm for the bird nest deployment. The compression algorithms that we have used in our deployments are simple, yet satisfied the requirements of our users. Other schemes can be considered also. [PERSON] _et al._[13] explored the energy tradeoffs involved in JPEG compression on energy-constrained platforms, but their findings show that JPEG energy consumption is actually higher on low power platforms due to the longer times needed for these platforms to perform the computation tasks to the desired precision. Table 1 shows the power consumption of our more platforms and Cyclops camera. TiBS (tiny block-size image coding) [14] and Optimal Zonal 2 \(\times\) 2 BinDCT [15] are two recently proposed image compression schemes for wireless sensor networks that could be useful for our deployments. TiBS operates on blocks of 2 \(\times\) 2 pixels and is based on pixel removal. Furthermore, TiBS is combined with a chaotic pixel mixing scheme to reinforce the robustness of image communication against packet losses. However, in our deployments, the primary challenge for implementing these improved algorithms was the practical memory constraint on our motes. The Mica2 and MicaZ motes have only 4 kB of RAM, and the Tenet software, which includes the multihop networking stack, reliable transport protocol and the task library, consumed over 3.6 kB already. However, for example, TiBS requires 22,946 bytes of ROM and 1820 bytes of RAM [14] to implement. There was very little room to implement these sophisticated image processing algorithms on our devices. We agree that newer platforms with more memory can implement better compression algorithms on top of our system. Alternatively, hardware-based image compression, such as the one proposed in [16], can also be a solution if the cost permits. Figure 3: Simple RLE with simulated packet loss of 5%, 10%, 20% and 50%. Average packet loss in our bird nest deployment was around 18%.
# On the Kung-Traub Conjecture for Iterative Methods for Solving Quadratic Equations
## 5 Numerical Experiments
### Dynamic Behaviour in the Complex Plane  
Consider our **Test problem 2** (TP2) based on the quadratic function \(f(z)=z^{2}-1\) where \(z\) is a complex number. We let \(z_{1}^{*}=-1\) and \(z_{2}^{*}=1\) which are the roots of unity for \(f(z)=z^{2}-1\). We study the dynamic behaviour of higher order \((r+2)\)_thBQIM_ I.F.s (\(r=1,2,3,4,5,6\)). We take a square \(\mathbb{R}\times\mathbb{R}=[-2,2]\times[-2,2]\) of \(256\times 256\) points and we apply our iterative methods starting in every \(z^{(0)}\) in the square. If the sequence generated by the iterative method attempts a zero \(z_{j}^{*}\) of the polynomial with a tolerance \(|f(z^{(k)})|<1e-4\) and a maximum of 100 iterations, we decide that \(z^{(0)}\) is in the basin of attraction of this zero.  
If the iterative method starting in \(z^{(0)}\) reaches a zero in \(N\) iterations (\(N\leq 100\)), then we mark this point \(z^{(0)}\) with a blue color if \(|z^{(N)}-z_{1}^{*}|<1e-4\) or green color if \(|z^{(N)}-z_{2}^{*}|<1e-4\). If \(N>100\), we conclude that the starting point has diverged and we assign a dark blue color. Let \(N_{D}\) be number of diverging points and we count the number of starting points which converge in 1, 2, 3, 4, 5 or above 5 iterations.  
Table 2 shows that all 6 methods are globally convergent and as the order of the method increases, the number of starting points converging to a root in 1 or 2 iterations increases. This is the advantage of higher order methods.  
[PERSON] coined the term \"polynomiography\" to be the art and science of visualization in the approximation of roots of polynomial using I.F. [12]. Figures 1 and 2 show the polynomiography of the six methods. It can be observed as the order of the method increases, the methods behave more chaotically (the size of the \"petals\" become larger).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**I.F.** & \(\mathbf{N=1}\) & \(\mathbf{N=2}\) & \(\mathbf{N=3}\) & \(\mathbf{N=4}\) & \(\mathbf{N=5}\) & \(\mathbf{N>5}\) & \(\mathbf{N_{D}}\) \\ \hline \(3 rd\)_BQIM_ & 56 & 6536 & 28,736 & 16,240 & 5428 & 8540 & 0 \\ \(44 th\)_BQIM_ & 232 & 16,908 & 27,532 & 7780 & 3700 & 9564 & 0 \\ \(5 th\)_BQIM_ & 528 & 23,348 & 23,196 & 5928 & 3340 & 9196 & 0 \\ \(6 th\)_BQIM_ & 928 & 27,880 & 19,680 & 5272 & 3072 & 8704 & 0 \\ \(7 th\)_BQIM_ & 1392 & 31,304 & 16,736 & 4856 & 2864 & 8394 & 0 \\ \(8 th\)_BQIM_ & 1892 & 33,924 & 14,220 & 4564 & 2788 & 8184 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the quadratic function \(f(z)=z^{2}-1\) for the \(3 rd\)_BQIM_, \(4 th\)_BQIM_, \(5 th\)_BQIM_, \(6 th\)_BQIM_, \(7 th\)_BQIM_ and \(8 th\)_BQIM_ I.F.s.  
Figure 1: Polynomiographs of \(3 rd\)_BQIM_, \(4 th\)_BQIM_ and \(5 th\)_BQIM_ I.F.s. for \(f(z)=z^{2}-1\). (**a**) \(3 rd\)_BQIM_; (**b**) \(4 th\)_BQIM_; (**c**) \(5 th\)_BQIM_.
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### The Mathematical Tools and Techniques of OR Which May Support the IoT
#### 3.1.5 Fuzzy Systems Theory / Artificial Neural Networks  
Fuzzy systems use fuzzy logic where input variables can take on continuous values, in contrast to digital systems that operate on discrete values. [PERSON] et al. [55] developed a fuzzy information algorithm using multi-criteria decision analysis and applied it to spacecraft landing safety. This technique could also be applied to IoT research areas.  
Artificial Neural Networks represent an approach to Artificial Intelligence that uses a network of many interconnected units generally organized into layers. These units operate on inputs from units in lower layers using an approach that mimics the human brain.  
Fuzzy logic and artificial neural networks are important techniques in data fusion, in which the data from many sensors is combined in various ways [56; 57]. This is an important function in the IoT.  
[PERSON] et al. [58] developed an artificial neural network trust and reputation model and applied it to predict vulnerability of the IoT against malicious attacks.