esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we propose a frame aggregation with a simple block acknowledgement (FASBA) scheme. The FASBA extends the capabilities of MP-EDCA by accommodating an increased number of emergency nodes during emergency times. The FASBA's throughput and packet delays are improved by significantly reducing transmission overheads. The overheads are reduced by aggregating frames with a simple block acknowledgement mechanism before transmission. Consequently, FASBA offers a guaranteed service delivery in highly loaded networks.  
Riverbed Modeler and MATLAB 2024a-based simulation models validate the system performance. The results obtained have shown that FASBA achieves about 30% lower packet delays, 17% higher throughput, and 60% lower retransmission attempts than MP-EDCA. Therefore, FASBA can be used to provide a strict QoS guarantee for life-saving emergency nodes. The findings reported in this paper provide insights into QoS guarantee for life-saving emergency traffic that can help network researchers and engineers contribute further towards developing next-generation wireless networks. In our future works, we will further investigate the impact of different traffic types and transmission protocols on FSBA performance. Moreover, the number of frames that can be aggregated is still an open issue to investigate to identify the optimum solution for frame aggregation. However, incorporating FASBA into the design of the Internet of Things to save human lives in emergency times is suggested as future work.  
Conceptualisation, S.K.M.; methodology, S.K.M. and M.A.H.; validation, S.K.M. and M.A.H.; investigation, S.K.M. and M.A.H.; resources, S.K.M. and N.I.S.; data curation, S.K.M.; writing--original draft preparation, S.K.M., N.I.S. and M.A.H.; writing--review and editing, M.A.H. and N.I.S.; visualisation, S.K.M. and M.A.H.; supervision, N.I.S.; project administration, N.I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable to this article as no new data were created or analysed in this study.  
The authors declare no conflicts of interest.  
Figure 11: Packet retransmission attempts versus the number of emergency nodes.
## 1 Introduction
To do so, first, an appropriate model for each security level (steady-state, dynamic, and transient security) is developed. Notably, the security of modern power systems can be affected by failures at the device level, as mentioned in Figure 1. According to [33], around 50% of power outages for 140 worldwide outage dates are attributed to equipment failure. Moreover, power converters are failure-prone components in power systems, e.g., in wind power systems and photovoltaic applications, according to field experience [34]. Among converter components, power modules and capacitors are the main sources of failure leading to shutdown of the specific converters. Then, based on the system model, a general guideline for the security assessment is discussed. The necessity of security assessment of PE-based units for power systems is shown through simulation results. Thus, the component failure may cause system failure depending on its security. However, a device level security assessment is not the focus of this paper, though it is a matter of importance for a PE-based power system's security assessment. The main contributions of this work can be summarized as follows:  
Figure 1: Overall schematic of the PE-based power system security assessment concept. * To review and discuss some events in some electrical grids that are derived by PE-based units, which lead to major blackouts. * To discuss electrical power system security challenges caused by PE-based units; specifically, the grid-feeding power converters. This includes system frequency support, small-signal stability challenges, and the PE-based units' synchronization issue. * To present the static, dynamic, and transient security assessment models of PE-based power systems having high levels of wind turbine penetration with the grid-feeding control mode. For each level of security assessment, e.g., static, dynamic, and transient security assessments, an appropriate model of PE-based units is needed. The rest of the paper is organized as follows: in Section 2, the security assessments of the conventional power systems are discussed. Section 3 investigates the power system's security challenges introduced by the PE-based units. Here, some major real-world events are presented and discussed. The proposed guideline used for the security assessment of the system is introduced at the same part. The effectiveness of the proposed guideline for different levels of studies, such as static, dynamic, and transient security, is verified using the simulation results in Section 4. Section 5 concludes this paper.
## 4 Experiments
### Global Settings
#### 4.2.2 Concept Pairs and Special Words  
In experiments, people's bias on four concept pairs was investigated. To compare our results to well-known psychological observations, four concept pairs reported in IAT were selected including _\"Iusect vs. Flower\"_, _\"Weapon vs. Instrument\"_, _\"Afri-American vs. Euro-American\"_ and _\"China vs. America\"_. The selected concepts involve general social bias, controversial social bias, and cultural differences between China and America to make the experiments more representative. In English corpora, concept words were selected to name the concept directly. The exemplar words and attribute (_pleasant_ and _unpleasant_) words were selected following IAT [5], WEAT [12], and Chinese version of Harvard's online IAT [[https://implicit.harvard](https://implicit.harvard)]([https://implicit.harvard](https://implicit.harvard)). edu/implicit/china/ (accessed on 12 March 2024). We manually translated the special words used in English experiments into Chinese in Chinese experiments. The detailed list of selected special words for each pair of concepts is as follows:  
**(1) Concept pair: Insect vs. Flower**  
**Concept words of 'Insect':** insect, insects  
**Concept words of 'Flower':** flower, flowers  
**Exemplar words of 'Insect':** ant, caterpillar, flea, locust, spider, bedbug, centipede, fly, maggot, tarantula, bee, cockroach, gnat, mosquito, termite, beetle, cricket, hornet, moth, wasp, blackfly, dragonfly, horsefly, roach, weevil  
**Exemplar words of 'Flower':** aster, clover, hyacinth, marigold, poppy, azalea, crocus, iris, orchid, rose, bluebell, daffodil, lilac, pansy, tulip, buttercup, daisy, lily, peony, violet, carnation, gladiola, magnolia, petunia, zinnia  
**Attribute words of 'Pleasant':** caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond, gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family, happy, laughter, paradise, vacation  
**Attribute words of 'Unpleasant':** abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink, assault, disaster, hatred, pollute, tragedy, divorce, jail, poverty, ugly, cancer, kill, rotten, vomit, agony, prison  
**(2) Concept pairs: Weapon vs.
## 3 Ubiquitous Tourist System Based on MCDM and AR  
We developed a ubiquitous tourist system based on MCDM and AR using GIS data and analysis, which we called UTS-mAR. This system a hybrid system for tourism; it consists of two components: a hotel recommender, and AR visualization. The hotel recommender selects a hotel in the vicinity of the user based on their preferences. The AR visualization applies AR technology to visualize information related to visited objects. UTS-mAR offers significant capabilities, as follows: First, the system uses context data, which include information related to the user and their environment. This information is used to enable the system to manage different types of users and changes in the test environment. Second, the system operates based on the intelligent models at its core. These models facilitate adequate analysis of the context to deliver services.  
The hotel recommender model applies SF and MCDM; it filters hotels in the vicinity of the user and recommends the final hotel based on user preferences. In addition, an improved version of this model with added clustering is available in the system. This version is useful when the amount of input data is large. The AR visualization model defines the information properties during visualization using the necessary rules, distance calculations, fuzzy functions, and AR technology. Furthermore, this system is a spatial system, which allows the use of geospatial data and spatial functions in intelligent models. In this regard, geospatial databases that contain hotels, hotel items, and spatial functions--such as the closest distance during SF--can be used.
## 3 Results and Discussion
### 400-1800 cm\({}^{-1}\) Soil SERS Analysis  
In this experiment, SERS data for 120 soil samples were collected. Figure 5 shows the 400-1800 cm\({}^{-1}\) SERS of 120 soil samples, and Figure 6 shows eight water-soluble nitrogen concentrations (22.7, 36.4, 50, 63.7, 77.3, 91, 104.6, and 118.3 mg/L) SERS. The abscissa is the wavenumber and the ordinate is the SERS intensity.  
Figure 4: The enhancement effects of different substrates: (**a**) silver nanoparticle; (**b**) OTR 202; (**c**) gold nanoparticle.  
Figure 5: The SERS spectra of soil water-soluble nitrogen from 120 soil samples.  
According to Figures 5 and 6, the SERS absorbance intensity decreased gradually with the decrease in soil water-soluble nitrogen concentration at the characteristic peaks of 994, 1028, 1370, 1436, 1554, and 1636 cm\({}^{-1}\). It is indicated that those peaks could be determined as water-soluble nitrogen Raman characteristic peaks, and the SERS absorbance intensity and soil water-soluble nitrogen concentration was positively correlated.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
###### Abstract  
In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved status of the sample), which helps guide the experiment. The two major spectral changes what we aim to capture are either the change in intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. This study aims to develop deep learning (DL) classification frameworks for one-dimensional (1D) spectral time series. In this work, we deal with the spectra classification problem from two different perspectives, one is a general two-dimensional (2D) space segmentation problem, and the other is a common 1D time series classification problem. We focused on the two proposed classification models under these two settings, the namely the end-to-end binned Fully Connected Neural Network (FCNN) with the automatically capturing weighting factors model and the convolutional SCT attention model. Under the setting of 1D time series classification, several other end-to-end structures based on FCNN, Convolutional Neural Network (CNN), ResNets, Long Short-Term Memory (LSTM), and Transformer were explored. Finally, we evaluated and compared the performance of these classification models based on the High Energy Density (HED) spectra dataset from multiple perspectives, and further performed the feature importance analysis to explore their interpretability. The results show that all the applied models can achieve 100% classification confidence, but the models applied under the 1D time series classification setting are superior. Among them, Transformer-based methods consume the least training time (0.449 s). Our proposed convolutional Spatial-Channel-Temporal (SCT) attention model uses 1.269 s, but its self-attention mechanism performed across spatial, channel, and temporal dimensions can suppress indistinguishable features better than others, and selectively focus on obvious features with high separability.  
end-to-end machine learning; spectra classification; deep learning (DL); convolutional neural network (CNN); transformer; long short-term memory (LSTM); self-attention +
Footnote †: journal: Applied Sciences
# Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study
## 3 Theoretical Background
### Examined Distributed Consensus Algorithms
In general, OW is the best-performing algorithm among the distributed consensus algorithms [62]. These weights are, therefore, often referred to as the fastest distributed linear averaging algorithm and are designed in such a way that their weight matrix \(\mathbf{W}\) is optimized according to eigenvalues, i.e. :  
\[ \begin{split}\text{minimize}&\rho(\mathbf{W}-\frac{1}{n} \mathbf{1}\mathbf{1}^{\mathbf{T}})=max\{\lambda_{2}(\mathbf{W}),-\lambda_{n} (\mathbf{W})\}\\ \text{subject to}&\mathbf{W}\in\mathbf{S},\mathbf{1}^{ \mathbf{T}}\mathbf{W}=\mathbf{1}^{\mathbf{T}},\mathbf{W}\mathbf{1}=\mathbf{1} \end{split}  \tag{16}\]  
Figure 6: Comparison of evolution of inner state in regular bipartite/non-regular non-bipartite graph. Here, \(\mathbf{W}\in\mathbf{S}\) represents the constraint on the sparsity pattern of the weight matrix \(\mathbf{W}\), where \(\mathbf{S}\) is defined as follows:  
\[\mathbf{S}=\{\mathbf{W}\in\mathbf{R}^{n\times n}\mid[W]_{ij}=0\text{ if }\{i,j\}\
otin \mathbf{E}\text{ and }i\
eq j\} \tag{17}\]  
In this paper, we also examine CW, an approach with the Perron matrix provided in (18), where the mixing parameter \(\epsilon\) takes value from the interval (19) [51]:  
\[[\mathbf{W}]_{ij}= \begin{cases}\epsilon,&\text{if }e_{ij}\in\mathbf{E}\\ 1-d_{i}\epsilon,&\text{if }i=j\\ 0,&\text{otherwise}\end{cases}  \tag{18}\]  
\[0<\epsilon<\frac{1}{\Delta} \tag{19}\]  
The last investigated algorithm is GMH, whose weight matrix \(\mathbf{W}\) is defined as (20) [51]. Its mixing parameter \(\epsilon\) is a variable again, taking the values from (21):  
\[[\mathbf{W}]_{ij}= \begin{cases}\frac{1}{max\{d_{i},d_{j}\}+\epsilon},&\text{ if }e_{ij}\in\mathbf{E}\\ 1-\sum_{j}\left[\mathbf{W}\right]_{ij}\prime,&\text{if }i=j\\ 0,&\text{otherwise}\end{cases}  \tag{20}\]  
\[0\leq\epsilon\leq 1 \tag{21}\]  
At the end of this subsection, we provide the spectrum of the weight matrix \(\mathbf{W}\) for all the examined algorithms in 3-regular bipartite graphs with \(n=30\). As seen from Figure 7, the smallest eigenvalue \(\lambda_{n}(\mathbf{W})\) of the weight matrix \(\mathbf{W}\) is greater than minus one for each examined algorithm, except MD and LD, as stated above in this paper. Figure 7: _Cont._
Transmission power control is advantageous in many scenarios. In a multiple-cluster network, ACTOR is beneficial since it allows the clusters to limit their destructive interference to a smaller region. Figure 1 illustrates a multi-cluster topology in which the two clusters do not interfere with each other when using ACTOR (Figure 1b). When running RPL, the transmission ranges of nodes \(A3\) and \(B2\) collide with a lot of nodes in the neighboring cluster (Figure 1a). The figure also shows a sample of the table that ACTOR maintainsfor storing the action values and the number of times they have been explored. By using ACTOR, nodes can improve their throughput in dense or high-throughput scenarios while minimizing their energy consumption. This is achieved through optimal transmission power control, fast convergence, and keeping the topology stable. The recent efforts in the domain of deterministic networking in the IoT have led to the standardization of the _Time Slotted Channel Hopping_ (TSCH) and IPv6 over TSCH (6 TiSCH) framework [11]. The benefits of increasing spatial reuse in the network are not limited to tackling the hidden terminal problem in a CSMA network. Avoiding excessive transmission power settings reduces the interference incurred to the neighboring nodes. Hence, even with the proliferation of TSCH mode in the MAC layer, the transmission power control reduces the constraints of the scheduler, allowing higher throughput. In summary, this paper outlines the following contributions:  
* We experimentally analyze packet delivery ratio distribution with different transmission powers and distances for two nodes communicating with RPL, showing the need for intelligent transmission power control (Section 4). * We formulate the problem of transmission power control in RPL using the _multi-armed bandit_ paradigm and propose the ACTOR mechanism with two alternative algorithms--UCB and Discounted UCB (Section 5). * We implement and integrate the ACTOR mechanism in RPL in a way that is backward-compatible with the standard protocol using Contiki-NG, and we publish the code as open-source ([[https://github.com/iliar-rabet/ACTOR](https://github.com/iliar-rabet/ACTOR)]([https://github.com/iliar-rabet/ACTOR](https://github.com/iliar-rabet/ACTOR)), accessed on 3 April 2024 ) (Section 5). * We evaluate and compare ACTOR performance against the default RPL and two benchmarks, both through simulations and a real testbed, showing improved reliability and energy efficiency (Section 6). The remainder of the paper is structured as follows: Section 2 reviews some background regarding bandit theory and the standard RPL protocol. Section 3 addresses the related work. In Section 4, we analyze the upper bounds of the distribution of the PDR using different transmission power settings. Section 5 addresses the proposed method, which is then evaluated in Section 6. Finally, we conclude the paper in Section 7.
## 3 Results and Discussion
### Forecast of Solar Radiation
Conversely, results of wind speed offer bigger error in Tenerife, although it is still a good result versus the state-of-the-art. Meteorological parameters like solar radiation does not have high variability in both locations, they offer similar results. This capability to validate the system in two different geographical locations is also an innovation versus previous researches.
## 4 Results
### DEM Model Validation  
To validate the DEM model, a mixing test with a 3D mixer was carried out and the mixture obtained from the test was used to determine the repose angle. The same procedure was simulated by the DEM model, and the numerical results were compared with the experimental ones. From a qualitative point of view, the mixing homogeneity was compared, and from a quantitative point of view, the repose angle.  
The methodology consisted of filling the cup with the component mass fraction according to the formulation shown in Table 2, closing the cup with a lid, placing it in a 3D mixer and running for 60 s. In Figure 5 a screenshot taken during the mixing test is observed. Figure 6 shows the cup filled with the components totally segregated before the test, and the cup with the components mixed after the test.  
Once the mixing process was finished, a sample from the mixture was taken to determine the repose angle by the fixed funnel method according to [24]. Figure 7 presents a comparison between the powder piles obtained from test and DEM simulation. From a visual perspective, it is observed that a high grade of mixing has been obtained in both cases. The repose angle test was repeated twice and both times the result was 32\({}^{\circ}\), while a repose angle of 31.98\({}^{\circ}\) was obtained from the DEM simulation, as indicated in Table 5. Therefore, a great agreement between results from the experimental test and DEM simulation is observed.  
Figure 5: Image of the mixing test performed in a 3D mixer.
## 3 Resource Allocation Considering NS and D2D
A resource block (RB) is the minimum resource allocation and includes resources in the time and frequency domains. The composition of an RB in 5G mobile networks is more flexible based on its numerology; in this work, we have assumed numerology 0, i.e., in the frequency domain, it is composed of 12 consecutive 15 KHz subcarriers, and in the time domain, it has a duration slot of 0.5 ms [15]. In our implementation, we consider a bandwidth of 10 MHz. In such a system, we have 50 RBs per slot. RBs are always scheduled in pairs composing a scheduling block (SB), with a duration of 1 ms [16]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Services** & **URLLC** & **Video** \\ \hline Slices & 4 & 3 \\ UEs & 4 & 8 \\ D2D pairs & Poisson distribution [mean = 5] & Poisson distribution [mean = 5] \\ Inter-arrival time distribution & Exponential [mean = 180 ms] & Truncated Pareto [exponential = 1.2, mean = 6 ms, max = 12.5 ms] \\ Packet size distribution & Truncated lognormal [mean = 2 MB, standard deviation = 0.722 MB, max = 5 MB] & Truncated Pareto [exponential = 1.2, mean = 100 byte, max = 250 byte] \\ SLA: Rate & 10 Mbps & 5 Mbps \\ SLA: Latency & 5 ms & 10 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings. Figure 1: Mobile communication system model.
# Image Structure-Induced Semantic Pyramid Network for Inpainting
## 2 Approach
### Loss Function
#### 2.4.1 Edge Inpainting Loss  
The input of the network consists of three parts: the mask, the greyscale of the missing image, and the edge information of the missing image. \(I_{gt}\) is used to represent the original image, and \(C_{gt}\) and are \(I_{grey}\) used to indicate the edge image and grey image. In the edge generator, we can obtain the following input:  
\[\left\{\begin{aligned} \widetilde{I}_{grey}&=I_{grey} \otimes(1-M)\\ \widetilde{C}_{gt}&=C_{gt}\otimes(1-M)\end{aligned}\right. \tag{7}\]  
In the above equation, \(\widetilde{I}_{grey}\) and \(\widetilde{C}_{gt}\) represent the grey image and edge image covered by the mask, and M represents the mask, which has only two values. When the value is 1, the information is missing; when the value is 0, the information is not missing; the symbol \(\otimes\) represents elementwise multiplication. \(G_{1}(\cdot)\) represents the edge generator operation, and \(D_{1}(\cdot)\) represents the edge discriminator operation. The edge graph of the mask-covered area predicted by the generator can be expressed as:  
\[C_{pred}=G_{1}\Big{(}\widetilde{I}_{grey},\widetilde{C}_{gt},M\Big{)} \tag{8}\]  
\(C_{gt}\) and \(C_{pred}\) are used as the inputs of the discriminator network to determine the veracity of the edge image. The loss of the network is composed of two parts, and the specific expression is as follows:  
\[L_{adj}=\lambda_{1}L_{adj1}+\lambda_{FM}L_{FM} \tag{9}\]  
In the above equation, \(L_{adj1}\) represents the confrontation loss, \(L_{FM}\) represents the feature matching loss, and \(\lambda_{1}\) and \(\lambda_{FM}\) are constant coefficients. The specific definition of \(L_{adv1}\) is as follows:  
\[L_{adv1}=E_{(C_{gt},I_{grey})}\log\big{[}D_{1}\big{(}C_{gt},I_{grey}\big{)} \big{]}+E_{I_{grey}}\log\Big{[}1-D_{1}\Big{(}C_{pred},I_{grey}\Big{)}\Big{]} \tag{10}\]  
The feature matching loss stabilizes the training process by comparing the activation graphs between the discriminator intermediate layers, and it is similar to the perceptual loss. The specific definition of \(L_{FM}\) is:  
\[L_{FM}=E[\sum_{i=1}^{L}\frac{1}{N_{i}}\|D_{1}^{(i)}\big{(}C_{gt}\big{)}-D_{1} ^{(i)}\Big{(}C_{pred}\Big{)}\|_{1}] \tag{11}\]  
where \(L\) represents the number of convolution layers of \(D_{1}\), \(N_{i}\) represents the number of elements in the \(i\)th activation layer of \(D_{1}\), and \(D_{1}^{(i)}\) represents the activation function in the \(i\)th activation layer of \(D_{1}\).  
In this paper, after many independent experiments, the final weight of each loss is set to \(\lambda_{1}=1\) and \(\lambda_{FM}=15\).