esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of a Mesh-Topology LoRa Network
## 7 Results
### A QoS Comparison  
In this subsection, we compare the PDR of LoRaWAN vs. a multi-hop network. To that aim, the results shown in Figures 11 and 12 show the PDR vs. the distance of a LoRaWAN vs. a multi-hop network, respectively. In this comparison, 1000 devices are uniformly randomly placed in an 8.5 km radius around a single gateway. Ten messages are transmitted to the gateway.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**SF** & **No of** & **Average Power** & **Increased \% Due to** \\  & **Child Nodes** & **Consumption (J)** & **Additional Child Node** \\ \hline
7 & 0 & 0.143111 & \\
7 & 1 & 0.287213 & 100.6922 \\
7 & 2 & 0.398114 & 38.61287 \\
7 & 3 & 0.534921 & 34.36384 \\
8 & 0 & 0.4  
The results showed a 96.9% PDR for nodes located in the first hop, 57.87% for nodes in the second hop, and 73.78% for nodes located in the last hop. This highlights the high rate of collisions in a dense network. However, it also highlights how nodes located further from the gateway can still achieve a high PDR, compared to LoRaWAN. The nodes located beyond 5.8 km in the LoRaWAN had a PDR of 40.2% (nodes using DR0), which is significantly lower than the performance of any hop in the LoRaMesh network.  
In the LoRaMesh PDR analysis, shown in Figure 12, it is clear that the nodes typically either have a successful up-link or not. This can be attributed to the static nature of the network configuration. Nodes are set to always transmit on a specific SF, time, and channel; therefore, this results in certain packet collisions being repeated for every transmission cycle.  
Figure 11: Packet delivery ratio of LoRaWAN vs. distance from the GW.  
Figure 12: Packet delivery ratio of LoRaMesh vs. distance from the GW.  
This highlights the need for a reinforcement-learning-based approach, which should assist nodes in adapting their transmission parameters dynamically to optimise the network QoS.
# Natural Language Description of Videos for Smart Surveillance
## 5 Experiments and Results  
This section details the results of the TRECViD, UETVS and AGRIINTRUSION datasets. We compare the results of this multitask learning-based framework with two baseline models--an LSTM semantic compositional network (SCN-LSTM) and multimodal stochastic RNN (MS-RNN)--and video descriptions using deep neural networks.  
A long short-term memory semantic compositional network (SCN-LSTM) [29] was originally proposed for image description and later extended for videos. The spatial and temporal features of the video clips were characterized using 2D and 3D dimensional CNN visual features for every video frame in an input clip. Mean pooling was used for the features and then put together. The semantic concept was fed into the network, which was extracted using a semantic detector. This produced a detailed description of a video.  
The multimodal stochastic (MS) RNN [30] model generated different video descriptions using multimodal LSTM. This mined visual and text features using backward stochastic LSTM.  
Figure 2 shows the results of two randomly selected videos from the TRECViD dataset. It shows the video descriptions generated by the multitask deep learning framework, SCN-LSTM, MS-RNN and human annotations.  
Similarly, the results from the UETVS dataset are shown in Figure 3. The video descriptions generated from our multitask feature learning (MFL) framework showed a more accurate and improved version in comparison with the other approaches.  
The results from the AGRINTRUSION dataset are shown in Figure 4. Again, the video descriptions generated from our framework were more detailed and better in comparison with the other approaches.
# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
## 4 Results  
### Descriptive and Inferential Statistics and Data Distribution  
To assess the normality of the data, the Kolmogorov-Smirnov (K-S) test was employed. The K-S test evaluates the maximum distance between the cumulative distribution function (CDF) of the sample data and the CDF of a specified theoretical distribution--in this case, the normal distribution. The parameters of the normal distribution (mean and standard deviation) were estimated directly from the dataset. The K-S test statistic was calculated as 0.0245, with an associated \(p\)-value of 0.9282. Since the \(p\)-value is significantly greater than the conventional threshold of 0.05, we rejected the null hypothesis, suggesting no statistically significant evidence of deviation from normality.  
Analysis of Variance (ANOVA) and post hoc tests were deployed to examine the differences in drying efficiency across months and seasons, providing insights into the influence of seasonal variation on the biodrying process. ANOVA is a statistical technique used to compare means across multiple groups by analyzing the variability within groups relative to the variability between groups.  
A multiple linear regression model (Equation (3)) was employed to explore the relationships between drying efficiency (the dependent variable) and three key predictor variables, as follows: the amount of waste loaded into the bioboxes, the operational month (representing seasonal variations), and the biobox number. This statistical technique is used to quantify how each predictor influences the outcome variable, drying efficiency, while controlling for the effects of the other predictors in the model. By isolating the contribution of each variable, regression analysis allows for a deeper understanding of the factors driving variability in the biodrying process.  
The descriptive statistics on biodvig parsed per bioboxes are shown in Table 1.
## 3 Results and Discussion
### Numerical Results
#### 3.2.3 Vertical Stress Distributions  
Figure 15 displays the simulation results for the axial stress distributions for models with various isotropy plane inclinations \(\beta\) and interface friction angles \(\varphi\) considering the  
Figure 14: Exemplary numerical results for the deformed meshes of models with various isotropy plane inclinations \(\beta\) and interface friction angles \(\varphi\) of \(0^{\circ}\) and \(15^{\circ}\) (scaling factor = 80).  
Figure 13: Exemplary numerical results for the plastic points at the interfaces between sample and platens for various isotropy plane inclinations \(\beta\) and interface friction angles \(\varphi\) of \(0^{\circ}\) and \(15^{\circ}\).  
\"basis\" parameter set acc. to Table 2. It is apparent from this figure that stresses close to the uniaxial state are only reached for the models with an interface friction angle of \(\varphi=0^{\circ}\). The small divergences from the uniaxial stress state are mainly related to difficulties of the finite element solution in approximating the solution at re-entrant corners of the domain [42]. At such locations, singularities arise. It is also noticeable that, as a result of the skewing of the sample with \(\beta=45^{\circ}\), slightly asymmetric stress distributions within the depicted planes arise.  
Selecting an interface friction angle \(\varphi\) of \(15^{\circ}\) introduces forced stresses into the specimens as the lateral movements at the top and bottom surfaces get restrained. Stress concentrations are visible at the top and bottom edges of the samples as already observed by [PERSON] and [PERSON] [43], who derived an analytical solution for stresses and displacements of transversely isotropic elastic cylinders under compression with end-effects. The biggest difference in comparison with the non-restrained cases (\(\varphi=0^{\circ}\)) is recognizable for the resulting \(xz\)-plane stresses from the model with \(\beta=45^{\circ}\). In this case, larger stress inhomogeneities, especially in the inner third of the sample, are more visible than in other models. Ideally, however, homogeneous local stresses are desired in this region of the specimens, since this is where the local axial and radial strain measurements are usually carried out.  
Figure 15: Numerical results for the vertical stresses \(\sigma_{z}\) along the planes \(xz\) and \(yz\) for models with various isotropy plane inclinations \(\beta\) and interface friction angles \(\varphi\) of \(0^{\circ}\) and \(15^{\circ}\).  
In general, planar symmetry of the developing stresses with respect to the plane \(xz\), which is the samples' symmetry plane (see Figure 1), is visible. A different visual observation can only be made for the model with \(\varphi=0^{\circ}\) and \(\beta=45^{\circ}\), although the stress variability in this model is marginal and the cause is rather related to inaccuracies in the finite element approximation.
# A Survey of Low-Rank Updates of Preconditioners for Sequences of Symmetric Linear Systems
## 3 Implementation and Computational Complexity  
We sketch below the most time-consuming computational tasks when using a low-rank update of a given preconditioner. **Notation**: with the symbol \(O(f(n))\) we mean that there are two constant \(c_{1}\) and \(c_{2}\) independent of \(n\) such that \(c_{1}f(n)\leq O(f(n))\leq c_{2}f(n)\).  
We first evaluate the preliminary cost, which has to be done before starting he iterative process:  
1. All low-rank updates: Computation of \(A_{W}=AW\quad(O(np)\) operations).
2. Broyden and SR1 only: Computation of \(Z=P_{0}A_{W}-W\) (\(p\) applications of the initial preconditioner).
3. All low-rank updates: Computation of \(\Pi=W^{T}A_{W}(O(p^{2}n)\) operations).  
The cost of this tasks is likely to be amortized during the PCG iterations of the various linear systems to be solved.  
Regarding the most important cost per iteration, the deflated, Broyden, SR1 and spectral preconditioners behave in the same way, being all based on the following main tasks, in addition to the application of the initial preconditioner \(P_{0}\) to a given vector.  
1. Multiplication of an \(n\times p\) matrix times a vector (\(O(np)\) operations)
2. Solution of a small linear system with matrix \(\Pi\). (\(O(p^{3})\) operations).
3. Multiplication of a \(p\times n\) matrix times a vector (\(O(np)\) operations)  
The BFGS preconditioner, as it needs to correct \(P_{0}\) by a rank-(\(2p\)) matrix, roughly doubles tasks 1-3 and hence the total updating cost at each iteration.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## Appendix A  
To achieve the final sparsity prior, hierarchical priors about the hyperparameters \(\mathbf{\alpha}\), \(\sigma^{2}\) and the sparse vector \(\mathbf{s}\) are given in this section. The hyperprior of \(\mathbf{\alpha}\) is defined as a Gamma prior distribution:  
\[\begin{split} p(\mathbf{\alpha})&=\prod_{i=1}^{N}\Gamma( \alpha_{i}|a,b)\\ &=\prod_{i=1}^{N}\int_{0}^{+\infty}x^{a-1}e^{-x}dxb^{a}\alpha^{a-1} e^{-bx}.\end{split} \tag{1}\]  
It can be seen that the prior information is not sufficient because the prior distribution of the variance \(\sigma^{2}\) is unknown. The hyperprior of \(\sigma^{-2}\) as a Gamma prior distribution is defined as follows:  
\[p(\sigma^{-2})=\prod_{i=1}^{N}\Gamma(\sigma^{-2}|c,d). \tag{2}\]  
If \(a=b=c=d=0\) holds, the hyperprior distribution becomes uniform distribution. In this paper, this assumption is kept. It is convenient to discuss the estimation of hyperparameters.  
The posterior distribution of the unknown parameters \(\mathbf{s}\), \(\mathbf{\alpha}\), and \(\sigma^{2}\) is given by [PERSON]'s rules  
\[p(\mathbf{s},\mathbf{\alpha},\sigma^{2}|\mathbf{y})=\frac{p(\mathbf{y}|\mathbf{s},\mathbf{\alpha}, \sigma^{2})p(\mathbf{s},\mathbf{\alpha},\sigma^{2})}{p(\mathbf{y})}. \tag{3}\]  
Considering that it is difficult to calculate \(p(\mathbf{y})\), which is the integral of \(p(\mathbf{y}|\mathbf{s},\mathbf{\alpha},\sigma^{2})p(\mathbf{s},\mathbf{\alpha},\sigma^{2})
## 3 Methodology
### Physics-Informed DeepONet
Then, the loss function, in the form of the mean square error computed by physical constraint residuals, can be expressed as follows:  
\[L_{p}(\theta)=\frac{1}{Nm}\sum_{i=1}^{N}\sum_{j=1}^{m}\left|R_{p}(\theta) \left(x^{(j)},\mathbf{\varepsilon}_{i}\right)\right|^{2} \tag{22}\]  
\[L_{B_{i}}(\theta)=\frac{1}{NQ_{i}}\sum_{k=1}^{N}\sum_{j=1}^{Q_{i}}\left|R_{N_{ i}}(\theta)\left(x^{(j)}_{B_{i}},\mathbf{\varepsilon}_{k}\right)\right|^{2},\;i=1,\ldots,H \tag{23}\]  
\[L_{B_{H+1}}(\theta)=\frac{1}{NQ_{H+1}}\sum_{k=1}^{N}\sum_{j=1}^{Q_{H+1}}\left| R_{D}(\in which \(L_{p}(\theta)\) and \(\left\{L_{B_{i}}(\theta)\right\}_{i=1}^{H+1}\) are the losses of the strain-displacement relation and boundary conditions, \(\left\{x^{(j)}\right\}_{j=1}^{m}\) are \(m\) locations at which the strain is used for the expression of the strain function \(\epsilon\), and \(\left\{x_{B}^{(j)}\right\}_{j=1}^{Q_{i}}\) are sets of collocation points sampled from the corresponding boundary region. The \(L_{p}(\theta)\) and \(\left\{L_{B_{i}}(\theta)\right\}_{i=1}^{H+1}\) are computed in the complete absence of paired strain-displacement observations, called physics-driven losses. By adding physics-driven losses to the data-driven loss of the DeepONet, PI_DeepONet is constructed. In fact, the solution operator mapping the strain function into the displacement function can be completely defined by the GDEs. Thus, the PI_DeepONet can converge to the correct solution operator by minimizing only the physics-driven losses. As such, the aggregate loss of the PI_DeepONet we constructed can be expressed as follows:  
\[L(\theta)=\lambda_{p}L_{p}(\theta)+\sum_{i=1}^{H+1}\lambda_{i}L_{B_{i}}(\theta) \tag{25}\]  
in which \(\lambda_{p}\) and \(\lambda_{i}\) are the weights used to equalize the losses. The weights of the losses can be chosen empirically or adjusted as hyperparameters of the network.
## 5 Implementation Steps
### Rank and Blackhole Attacks  
In our previous work [12], we extensively studied the impact of IoT-specific DoS attacks, namely DIS flooding. This attack is based on the RPL routing protocol and affects the availability of the network. In this work, a combination of rank and blackhole attacks is implemented in Contiki-NG. Contiki provides an implementation of RPL, called ContikiRPL [36], whereas Contiki-NG implements a lighter version called RPL-lite.  
According to [14], in rank attacks usually a malicious node may intentionally advertise a lower rank in order to attract neighbouring devices to select it as the preferred parent. A parent node is needed in order to form the DODAG network and allow creation of routes reaching the BR. In cases where networks are small, the best parent is the BR itself. In other cases, metrics such as rank and ETX are used to select the best parent. If a malicious node manages to be chosen as the best parent of several nodes, it can attack the network, affecting its availability and integrity. As a result, the malicious node is the single point of failure of the network. In this work, rank attack is implemented in RPL-lite protocol of Contiki-NG. The implementation is shown in Algorithm 1. Rank attackers advertise a fake rank with value 129, whereas the root node has a value of 128.  
Another interesting attack is the blackhole. A blackhole attacker can degrade the performance of the IoT network by dropping all incoming packets. As a result, no packets are forwarded from this node. Algorithm 2 shows our implementation in Contiki-NG. All incoming packets are dropped only if they need to be forwarded. The attack begins after 2 min of simulation and lasts until the end of the simulation. Dropped packets by malicious attackers can be easily distinguished from the packets dropped due to interference because blackhole attackers first receive a packet and, then, the algorithm decides if it will be dropped or not. In the case of interference, nodes are unable to receive packets, and thus packets are not considered in the calculations.  
A combination of blackhole and rank attacks is implemented and tested in our experiments. They are combined to achieve the highest impact in the network by attracting neighbouring nodes and dropping all packets. A special node, called _udp-client-malicious_, is running these attacks in our scenarios.  
```
1:Function\(rpl\_icmp6\_dio\_output(ip\_address)\) :
2:\(dio\_packet\leftarrow create\_dio\_packet(ip\_address)\);
3:\(dio\_packet.dag\_rank\leftarrow\) 129;
4:Send packet
```  
**Algorithm 1** Rank attack implementation  
```
1:Input:Packet\(Pkt_{ij}\)
2:Function\(uip\_process(Pkt_{ij})\) :
3:\(next\_hop\gets Pkt_{ij}\_next\_hop\);
4:\(time\_attack\gets\) 2;
5:if\(next\_hop\
eq NULL\) and \(current\_time\geq time\_attack\)then
6: drop packet
7:endif
```  
**Algorithm 2** Blackhole attack implementation  
```
1:Input:\(Pkt_{ij}\)
2:Function\(uip\_process(Pkt_{ij})\) :
3:\(next\_hop\gets Pkt_{ij}\_next\_hop\);
4:\(time\_attack\gets\) 2;
5:if\(next\_hop\
eq NULL\) and \(current\_time\geq time\_attack\)then
6: drop packet
7:endif
```  
**Algorithm 3
## 3 Methods
### Output Module
#### 3.3.2 Analysis of Performance Indicators  
Offline evaluation is one of the most commonly adopted evaluation methods for recommendation systems. The holdout test is a basic offline evaluation method, which uses indicators of the precision rate, recall rate, F1 score, and mean average error (MAE) [14, 18, 19] to analyze and compare the performance of the recommendation results.  
The precision or accuracy rate indicates the proportion of correctly predicted positive samples to the total number of samples predicted to be positive. In this study, this rate is shown in Equation (10). It describes the proportion of learners' rated courses in the list of recommended courses. The recall rate is the proportion of correctly predicted positive samples to the actual positive samples, as shown in Equation (11); it describes the proportion of learners' rated courses in the list of recommended courses to the overall learner-rated courses in this study. \(L_{u}\) denotes the list of recommended courses for learner \(u\); \(B_{u}\) denotes the list of courses rated by learner \(u\); \(n\) denotes the total number of learners; \(U\) denotes the set of learners; \(P_{L}\) denotes the overall recommendation precision rate; and \(R_{L}\) denotes the overall recommendation recall rate.  
\[P_{L}=\frac{1}{n}\sum_{u\in U}\frac{L_{u}\cap B_{u}}{L_{u}} \tag{10}\]  
\[P_{L}=\frac{1}{n}\sum_{u\in U}\frac{L_{u}\cap B_{u}}{B_{u}} \tag{11}\]  
To comprehensively reflect the recommendation performance, the F1 score reflects the precision rate and recall rate simultaneously, as shown in Equation (12).  
\[\text{F1}=\frac{2.P_{L}.R_{L}}{P_{L}+R_{L}} \tag{12}\]  
Moreover, the mean absolute value error (MAE) can describe the error between the learners' actual and predicted course ratings. As shown in Equation (13), \(y\) is the learner's actual course rating, \(\hat{y}\) is the predicted one, and \(N_{test}\) is the number of the actual rating courses.  
\[\text{MAE}=\frac{\sum\left|y-g\right|}{N_{test}} \tag{13}\]
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 6 Algorithm Performance Analysis  
### Algorithm Convergence Analysis  
From Section 4, we can see that in the downlink, the interference suppression matrix is designed by Equation (14) to maximize the ratio of the desired signal power to the intercell interference power, and the precoding matrix is designed by Equation (18) in the uplink. The SINR is maximized, and the process is repeated until convergence. In this process, both the interference space and the signal quality are improved. In order to further improve the signal quality, the 4-in-1 power allocation algorithm is used to optimize the mutual information of the system. On the other hand, the maximal ratio combined signal flow is achieved through the phase alignment in Section 5.2 to improve the received power of the target signal stream. It can be seen that through the iterative operations of the downlink and the uplink, the compressed interference space is gradually rotated, and the signal quality is improved through power allocation and phase alignment, and the signal to interference ratio is gradually improved.  
The convergence simulation is given in Section 7.4 of this paper to further verify the convergence and feasibility of the proposed algorithm.
# Impact of VR Application in an Academic Context
## 2 Materials and Methods
### EduAssistant Experiential Learning Instrument Based on VR
#### 2.1.3 Video Conference with Agora.io  
The video conference system was developed to give students the opportunity to attend online courses and to collaborate with the other participants. In this case, integration with Agora.io API was required. The module facilitates attendance of multiple users in virtual courses simultaneously. The access is based on the following set of credentials: channel name and password. For the initial setup, the application was defined in the Agora.io console and an access ID was generated. There are two ways to create video conference channels: automatically by calling the API or manually from the administration panel. Currently, the application allows the creation of channels through the administration panel, and users can receive the details of the channel and the access key from the platform administrator or from the teacher. A mandatory requirement for the video conference module is the Agora Video SDK library. The library should be imported into Unity.  
In addition, and as reflected in the Figure 10, the following Unity user interface (UI) objects were used:  
* Background--raw image object to set the background of the section where the images taken from the camera of the students participating in the video conference are displayed;
* Debug--text object to be able to display success or error messages;
* Channel--input field to be able to mention the access channel;
* Access password--input field to be able to enter the password that allows access to the video conference;  
Figure 9: Whiteboard and markers.  
* Video conference attendance button--provides access in the video conference room by generating a token through Agora API calls with an application ID and access credentials (channel name and password) as input parameters;
* Video--enables the viewing of video images.