esa-sceva/satcom-chunk-collection huggingface dataset:
# A Laser-Based Method for the Detection of Honey Adulteration
## 3 Results  
### LIBS Spectra of Honey and Adulterated Honey Samples  
Figure 1a shows some representative LIBS spectra of honey, glucose syrup, and a honey-glucose syrup mixture. The spectra exhibited the characteristic spectral features which have been reported previously in the LIBS spectra of honey and in other organic materials [13; 14]. Among them, the most prominent features are the atomic lines of neutral carbon (C (I)) at 247.9 nm and the Balmer series spectral lines of hydrogen (H\({}_{\text{a}}\) and H\({}_{\text{g}}\)) at 696.3 and 486.1 nm, respectively, as well as the molecular bands of CN extending from 350 and 400 nm. However, as shown in Figure 1a, these features are quite similar, and they cannot be correlated with the degree of adulteration as they are present both in the honey and the adulterants. Figure 1b,c, show the spectral lines of some of the inorganic elements present in honey, e.g., the calcium ion lines (Ca (II)) at 393.3 and 396.8 nm and the sodium D lines (Na (I)) at 589.0 and 589.6 nm, as well as the potassium (K (I)) lines at 766.5 and 769.9 nm. As discussed in the next paragraph, this last group of spectral lines is of particular interest in this work. As can be seen from the comparison of the LIBS spectra of honey and the glucose syrup, the spectral features of the inorganic components are absent from the spectrum of glucose syrup. In addition, from the comparison of the honey LIBS spectrum (black line) with that of an adulterated honey (red line), it is evident that the adulterated sample exhibits notably lower intensity spectral lines of these inorganic species. These qualitative observations indicate that the spectral lines of these inorganic elements can be, in principle, employed for the detection of honey's adulteration. In Figure 1d, the spectral lines of Ca (II), Na (I), and K (I) obtained from honey samples mixed with glucose syrup at various ratios are presented in more detail. From this figure, it becomes evident that the relative intensities of these spectral lines are reduced as the glucose content (i.e., the honey-glucose ratio) is increasing.  
Figure 1: **(a)** LIBS spectra of honey, adulterated honey (50% \(w\)/\(w\)), and glucose syrup samples. **(b)** CN band and Ca (II) spectral lines, **(c)** Na (I) and K (I) spectral lines in a honey, an adulterated honey, and a glucose syrup sample. **(d)** Ca (II), Na (I), and K (I) spectral lines of different % adulterated honey samples.
# From 5G to 6G--Challenges, Technologies, and Applications
## 4 Current Research
### Numerical Discussion  
An extensive and thorough presentation of published works fully (or partially) performed in the context of B5G and/or 6G networks over the course of six years is laid out in Figure 7. We note that the search was performed on multiple online libraries and search engines using different keywords and filters, and each individual choice was verified manually by skim reading. At first glance, it is evident that the last two years, compared to any other years, have witnessed a significant increase in the amount of research pledged to exploiting B5G and 6G networks. We can consider 2018 to be the year when research on B5G networks caught momentum and officially began. Furthermore, this pace of research is only expected to grow faster in the upcoming years. For instance, there were over 300 works--related to 6G --published in 2020 alone. We anticipate that in a few years, the standardization process of 6G networks will be complete, and the market place will grow enough to support their roll out by the late 2020s. The discussion of our results carries on to characterize the nature of the papers published in each individual year. In Table 2, we selected a few papers from each year that describe generally how the next generation of networks will look and that are popular among researchers based on the number of citations from Google Scholar at the time of writing. While it is easy to expand the discussion about papers relating to 6G, the written discussion in Table 2 suffices for the objectives of our paper. We rather spend time here to contemplate a trait of the available literature concerning future networks, i.e., the depth of writing on B5G/6G networks over the years. It is appropriate to infer that the early years of research on B5G/6G have seen works that have only glimpsed at the characteristics of such networks. The papers of 2015 and 2016 presented an obscure to no qualitative analysis of the implementation of B5G/6G networks, with the exception being [99]. Advancing to more recent times, papers published in 2017 and 2018 expanded the discussion about future networks and presented a more comprehensive picture with key performance indicators and numbers compared to older works. Most recent years, 2019 and 2020, contain works that aimed to scale up the discussion of future networks by conducting empirical analyses of certain technologies that are thought to be at the core, such as IRS and ultra-massive MIMO performances [100; 101; 102; 103; 104; 105]. We can say with high confidence that the majority of the available survey papers on future networks in the literature do not discuss many of the points laid out in this section of the paper, such as the characterization and sorting of papers in this topic and the global efforts made toward actualizing beyond 5G networks (Tables 3-6). Figure 7: A bar graph of the number of papers published fully (or partially) in the context of B5G and/or 6G networks over the last few years.
## 5 Experimental Results
### Analysis of Prediction Results
Additionally, the number of companies, government institutions, and social organizations around Zhonglou Station is notably higher than around Hangtiancheng Station, indicating a larger daily population flow. Moreover, 10 March 2019 was a Sunday, a typical rest day for most people. As expected, the entry and exit flows at the target stations significantly decreased on this day. In contrast, from 11 March to 15 March, which fall within the workweek, the entry and exit flows exhibit similar patterns. Additionally, the residential areas around Hangtiancheng Station are noticeably more abundant than around Zhonglou Station. This indicates that Hangtiancheng Station is located in a residential area, resulting in lower entry and exit counts on weekends compared to weekdays. In contrast, Zhonglou Station, as a tourist destination, experiences higher entry and exit counts on weekends compared to weekdays. Finally, a few predicted values deviated from the actual values, which could be attributed to prediction errors caused by uncertain events. This observation aligns with the real urban traffic conditions, where traffic congestion during peak hours and unexpected traffic incidents can irregularly impact travel demand data, thus reducing the model's predictive accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**POI** & **Zhonglou Station** & **Hangtiancheng Station** \\ \hline Food and beverage services & 884 & 792 \\ \hline Shopping services & 838 & 882 \\ \hline Life services & 888 & 811 \\ \hline Sports and leisure services & 256 & 53 \\ \hline Health care services & 160 & 188 \\ \hline Accommodation services & 900 & 166 \\ \hline Scenic spots & 35 & 7 \\ \hline Government agencies and & & \\ social organizations & 216 & 115 \\ \hline Science and education cultural services & 149 & 114 \\ \hline Transportation facilities services & 263 & 69 \\ \hline Financial and insurance services & 81 & 39 \\ \hline Incorporated businesses & 302 & 81 \\ \hline Public facilities & 91 & 10 \\ \hline Industrial parks & 0 & 2 \\ \hline Residential areas & 71 & 105 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: POI data within an 800 m radius around Zhonglou Station and Hangtiancheng Station.
## 3 Cost Model and Problem Description
### Cost Model
#### 3.1.3 SLAV Penalty  
In CDCs, once SLAV emerges, then the CSP must compensate the relevant users in some form. This is an effective method for CSPs to guarantee QoS after users pay for cloud services. This part of the overhead needs to be included in the cost of CDC.  
We expand the CPU-based SLA definition [13] to CPU SLAV, memory SLAV, disk SLAV, and network SLAV, which are denoted as \(SLAV_{cpu}\), \(SLAV_{mem}\), \(SLAV_{disk}\), \(SLAV_{net}\), respectively.  
\[SLAV_{cpu}=\frac{1}{N}\sum_{i=1}^{N}\frac{T_{i}^{s,cpu}}{T_{i}^{s,cpu}}\times \frac{1}{M}\sum_{j=1}^{M}\sum_{k=1}^{L}\frac{ud_{i,k}^{d,cpu}}{s_{i,k}^{r,cpu}}, \tag{22}\]  
where \(T_{i}^{s,cpu}\) is CPU SLAV duration caused by CPU overloaded on \(h_{i}\), and \(T_{i}^{a,cpu}\) is the working duration of \(h_{i}\), and \(d_{i}^{d,cpu}\) is the size of the unsatisfied demand for CPU resources as a result of \(v_{j}\) migration in \(t_{k}\).  
Similarly, we formally define \(SLAV_{mem}\), \(SLAV_{disk}\) and \(SLAV_{net}\) as follows:  
\[SLAV_{mem}=\frac{1}{N}\sum_{i=1}^{N}\frac{T_{i}^{s,mem}}{T_{i}^{d,mem}}\times \frac{1}{M}\sum_{j=1}^{M}\sum_{k=1}^{L}\frac{ud_{i,k}^{d,mem}}{s_{i,k}^{r,mem }}, \tag{23}\]  
\[SLAV_{disk}=\frac{1}{N}\sum_{i=1}^{N}\frac{T_{i}^{s,disk}}{T_{i}^{a,disk}} \times\frac{1}{M}\sum_{j=1}^{M}\sum_{k=1}^{L}\frac{ud_{i,k}^{d,disk}}{s_{i,k}^ {r,disk}}, \tag{24}\]  
and  
\[SLAV_{net}=\frac{1}{N}\sum_{i=1}^{N}\frac{T_{i}^{s,net}}{T_{i}^{a,net}}\times \frac{1}{M}\sum_{j=1}^{M}\sum_{k=1}^{L}\frac{ud_{i,k}^{d,net}}{s_{i,k}^{r, net}}, \tag{25}\]where the definitions of corresponding parameters are similar to those of \(SLAV_{cpu}\), and no further explanation will be given here.  
In a given life cycle of CDC, the penalty cost caused by the appearance of SLAV is  
\[C_{SLAV}=\mathit{pun}_{cpu}\times SLAV_{cpu}+\mathit{pun}_{mem}\times SLAV_{ mem}+\mathit{pun}_{disk}\times SLAV_{disk}+\mathit{pun}_{net}\times SLAV_{net}, \tag{26}\]  
where \(\mathit{pun}_{cpu}\), \(\mathit{pun}_{mem}\), \(\mathit{pun}_{disk}\), and \(\mathit{pun}_{net}\) are the CPU, memory, disk, and network SLAV penalty price index, respectively.
# Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
## 4 Proposed Method: CausalMER
### Causal-Effect Look at CausalMER  
**Traditional MER Task.** A MER dataset with \(N\) samples can be expressed as \(\mathcal{D}=\{(\mathcal{T}_{1},\mathcal{A}_{1},\mathcal{V}_{1},\mathcal{Y}_{ 1}),\ldots,(\mathcal{T}_{N},\mathcal{A}_{N},\mathcal{V}_{N},\mathcal{Y}_{N})\}\). Let \((\mathcal{T}_{i},\mathcal{A}_{i},\mathcal{V}_{i})\) represent a pair of data in a MER dataset, where \(\mathcal{T}_{i}\), \(\mathcal{A}_{i}\), and \(\mathcal{V}_{i}\) correspond to the \(i\)-th text, audio, and video data sample, respectively. \(\mathcal{Y}_{i}\) is the emotion labels associated with this \(i\)-th data pair. The goal of MER is to train a multimodal model \(\mathcal{F}\), which jointly integrates and leverages the three modalities. Therefore, the model's prediction in MER can be represented as \(\hat{y}_{i}=\mathcal{F}(\mathcal{T}_{i},\mathcal{A}_{i},\mathcal{V}_{i})\), where \(\hat{y}_{i}\) is the predicted emotion label for the \(i\)-th sample.  
**Causal Graph in MER.** The causal graph of MER is illustrated in Figure 2(a). In this graph, there are five variables: text modality \(T\), audio modality \(A\), video modality \(V\), multimodal representation \(M\), and emotional prediction \(Y\), respectively. The effect of \(T\), \(A\), and \(V\) on \(Y\) can be broken down into two components: the single-modal impact and the multimodal impact. All causal relationships among these variables are explained as follows:  
* **Link \(T/A/V\to Y\)** represents the direct effect of each modality \(T\), \(A\), and \(V\) on the model's prediction \(Y\). This occurs because each modality may contribute salient features with spurious correlations to the prediction. As shown in the paper [12], the importance of a particular modality can vary at the sample level, and no single modality is consistently dominant in every situation. Therefore, it is crucial to avoid making predictions based solely on the direct effect of any one modality. Such direct effects can lead to negative outcomes, as the model relies on spurious correlations between each modality and the labels rather than capturing multimodal relationships.
* **Link \((T,A,V)\to M\to Y\)** represents the multimodal impact, which captures the indirect effect of \(T\), \(A\), and \(V\) on \(Y\) through the multimodal knowledge \(M\). The multimodal representation \(M\) is obtained by integrating information from the different modalities. In real-world environments, missing modalities can occur, making it difficult to predict which modality will be absent. Therefore, our model performs better by making predictions based on \(M\), trained on all modalities (\(V\), \(A\), and \(T\)) together. As a result, the indirect effect provides a beneficial impact on our model.  
In this paper, we propose _CausalMER_ to reduce modality bias in MER by eliminating the pure language, acoustic, and visual effects that cause certain modalities to dominate the prediction process.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results  
In this section, we conducted simulation results to validate the effectiveness of D-JERDG. Specifically, we provided numerical results and analyzed the convergence of D-JERDG by adjusting the number of CM nodes, CH nodes, and maximum charging radius. We compared D-JERDG with MODDPG and the random method. We analyzed the differences between the algorithms from five aspects: node death rate, data overflow rate, UAV throughput, average flight energy consumption, and time utilization. By comparing these metrics, we were able to evaluate and highlight the differences and advantages of D-JERDG over the other algorithms in terms of efficiency and performance.  
* D-JERDG: D-JERDG uses the K-means clustering algorithm to divide nodes into multiple clusters, designing the CH node selection mechanism based on an improved dynamic routing protocol. The SA algorithm determines the CH node access sequence, and the UAV flight actions are controlled by inputting the UAV and network states into the DRL model MODDPG.
* MODDPG (multiobjective deep deterministic policy gradient) [20]: This method selects the node with the highest data volume as the target node. By observing the relative positions of the UAV and the target node, as well as the node states, a DRL model is established to control the UAV learning of all node positions. Through accessing the nodes, data collection and range-based charging are achieved.
* Random: The method is based on the MODDPG [20] definition, with the difference being that the random algorithm randomly selects a node as the target node and uses the DRL model to optimize the UAV's flight trajectory.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 4 Data Collection III: Moving Targets  
In this section, we capture the response from two moving targets: one car and one drone. The block diagram for the setup is shown in Figure 9, and the corresponding photographs are shown in Figures 10 and 11, respectively. The maximum detectable range of a 0 dBm target for this test is \(R_{max}=8.34\) m, found by evaluating Equation (11) with \(N=2\) and the minimum receive power \(P_{r}=-55\) dBm. Target sampling occurs at an approximate range of \(R=6.1\) m and \(R=2.43\) m for the car and the drone, respectively. During the drone collection, the drone followed a direct line-of-sight trajectory along boresight during both collections. The drone was flown as close to a constant height as possible, yielding a radial velocity living mostly in the range-angle plane of the radar.  
Figure 8: MF response for binary target detection at \((R,\theta)=(6.70\) m, \(0^{\circ})\) using an FDA radar system with operational parameters \(f_{0}=2\) GHz, \(\Delta f=12\) kHz, and \(N=3\).  
Figure 10: Photograph of system for car test in Data Collection III.  
Figure 9: FDA radar block diagram for Collection III with arrows indicating direction of wave propagation. Blue arrows indicate propagation from the transmit antenna and red arrows indicate propagation towards the receive antenna.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Deep Learning Techniques Used in 5G Scenarios?
#### 3.3.1 Fully Connected Models  
Most of the works that used fully connected layers addressed problems related to the physical medium in 5G systems [2, 11, 15, 16, 17, 21, 22, 24, 26, 56, 60, 62, 63, 64, 65, 68, 72, 74, 76]. This can be justified because physical information usually can be structured (e.g., CSI, channel quality indicator (CQI), radio condition information, etc.). In addition, these works did not consider more complex data, such as historical information. It is understandable that the 5G physical layer receives such attention. It is the scene of a number of new technologies such as mmWave, MIMO and antenna beamforming. These are very challenging technologies that require real time fine tuning.  
However, although fully connected layers were not designed to deal with sequential data, some works found in this systematic review proposed models based on time series. In [10, 41], the authors considered real data of cellular networks such as Internet usage, SMS, and calls. Although the dataset has spatio-temporal characteristics, the authors extracted features to compose a new input for the deep learning model. In [52], the authors proposed a fully connected model to deal with user coordinate location data. In this work both fully connected and LSTM models were proposed for comparison and the temporal aspect of dataset was maintained. In [66], the authors adopted a dataset composed of historical data records for urban and rural areas. Unfortunately, the paper did not provide more details about the data used, but a deep learning model composed of fully connected layers was used to process this data.  
In [73], a fully connected model was used with a reinforcement learning algorithm. In this work, the open source public Mininet simulator was used to create a network topology (the environment) in order to train the agent. Subsequently, the deep learning model was used to chose the best action according with the environment.  
Figure 3: Most common deep learning techniques for 5G.
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 2 System Modelling  
### IM Modeling  
The IM model in the stationary \(\alpha\beta\) coordinate system is defined by the following equations [27]:  
\[p_{s}^{\alpha}\ =\ \frac{1}{\sigma L_{s}}v_{s}^{\alpha}-\frac{1}{\sigma L_{s}} \bigg{(}R_{s}+\frac{L_{m}^{2}}{L_{r}t_{r}}\bigg{)}i_{s}^{\alpha}+\frac{L_{m}}{ \sigma L_{s}L_{r}}\bigg{(}\frac{1}{t_{r}}\Psi_{s}^{\alpha}+\omega\Psi_{r}^{ \beta}\bigg{)}, \tag{1}\]  
\[p_{s}^{\beta}\ =\ \frac{1}{\sigma L_{s}}v_{s}^{\beta}-\frac{1}{\sigma L_{s}} \bigg{(}R_{s}+\frac{L_{m}^{2}}{L_{r}t_{r}}\bigg{)}i_{s}^{\beta}+\frac{L_{m}}{ \sigma L_{s}L_{r}}\bigg{(}\frac{1}{t_{r}}\Psi_{s}^{\beta}-\omega\Psi_{r}^{ \alpha}\bigg{)}, \tag{2}\]  
\[p\Psi_{s}^{\alpha}\ =\ -\frac{1}{t_{r}}\Psi_{r}^{\alpha}-\omega\Psi_{s}^{ \beta}+\frac{L_{m}}{t_{r}}i_{s}^{\alpha}, \tag{3}\]  
\[p\Psi_{s}^{\beta}\ =\ -\frac{1}{t_{r}}\Psi_{r}^{\beta}+\omega\Psi_{s}^{\alpha}+ \frac{L_{m}}{t_{r}}i_{s}^{\beta}, \tag{4}\]  
where \(p\) represents the time derivative (\(d/dt\)); \(\Psi_{s}^{\alpha\beta}\) denotes the stator flux in the \(\alpha\beta\) orientation; \(i_{s}^{\alpha\beta}\) represents the stator current in the \(\alpha\beta\) orientation; \(t_{r}\) denotes the rotor time coefficient; \(L_{s}\), \(L_{r}\), and \(L_{m}\) represent the stator, rotor, and mutual inductances, respectively; \(R_{s}\) and \(R_{r}\) denote the stator and rotor resistances, respectively; and \(v_{s}^{\alpha\beta}\) denotes the stator voltages in the \(\alpha\beta\) orientation.  
The field-oriented control principle involves aligning the rotor flux vector (\(\Psi_{r}\)) with the \(d\)-axis of the reference frame. The flux angular position (\(\theta_{s}\)) is determined to perform the direct and inverse Park transformations, ensuring that the rotating reference frame is defined as  
\[\theta\ =\ \int\omega_{s}dt\ =\ \int\big{(}\hat{\omega}_{s}+\omega_{sl} \big{)}. \tag{5}\]  
where \(\theta\) is the angle of flux angular position, \(\omega_{s}\) is the stator speed of the ventilation system, \(\hat{\omega}_{s}\) is the reference speed, and \(\omega_{sl}\) is the slip angular speed.  
The slip angular speed (\(\omega_{sl}\)) can be calculated using the following equation:  
\[\omega_{sl}\ =\ \omega_{e}-\omega_{r}\ =\ \frac{1}{t_{r}}\frac{i_{s}^{\alpha}}{t _{s}^{\alpha}}. \tag{6}\]
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods
### Enumerating Evasion Paths
#### 2.4.1. Backwards Propagation  
Each region labelled _True_ in the final state corresponds to one group of path equivalence classes. As we replay the history of the algorithm from the end and backwards toward the beginning, every Merge event, where two regions \(A_{1},A_{2}\) labelled _True_ become a single region \(R\) by the removal of an \(\alpha\)-complex edge splitting all the path equivalence classes that pass through \(R\) into two path equivalence classes each--one that came to \(R\) from \(A_{1}\), and one that came from \(A_{2}\). We describe this in Algorithm 3.  
Given: A full history of an Evasion Detection using either Algorithm 1 or Algorithm 2.  
```
1:Build a set \(\{G_{i}\}_{i=1..N}\) of paths of height zero, one for each _Alive_ region at time \(T\)\(\triangleright\) Initialization
2:for timestep \(t=T..1\) in reverse do
3:for path \(G=[A_{j},A_{j+1},\ldots,A_{T}]\)do
4:if\(A_{j}\) is the product of merging two alive regions \(A\) and \(B\)then
5: Duplicate \(G\), prepending \(A\) to one copy and \(B\) to the other
6:if all other cases then
7: Prepend the _Alive_ region from time step \(j-1\) containing \(A_{j}\) to the path \(G\)
8: Return the set of paths
```  
**Algorithm 3** Evasion paths enumeration through backward propagation.