esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Conceptual Framework  
Previous studies in activity classification [30; 31; 32], have predominantly utilized omni-directional antennas, concentrating on the direct line of sight between these antennas for monitoring human activities. We adopted this methodology as depicted in Figure 4. It is crucial to note that CSI is influenced not just by the human body, but by the surrounding environment as a whole. Consequently, identical human poses might yield significantly different CSI readings in dissimilar environments. Therefore, CSI is particularly adept at tracking moving targets by focusing on the changes they induce in the signal. References [33; 34] provide the concept of mapping CSI data to activity classifications. In the context of monitoring sleep, the participants are informed to stay in still position to measure RR using ESP32, and Vernier Respiration Monitor Belt is used to measure ground truth. The noise contained in raw CSI data from ESP32 are treated by a series of signal processing methods. However, noise may still be available due to external movements and elements that generate distortions in the rooms. Therefore, we implement noise detection method to remove the anomalies. Furthermore, local peak function is utilized to determine bpm, and compared to the ground truth value. The process of mapping CSI data to RR changes is detailed in Figure 5, and further described in subsequent sections.
# Towards a Universal Semantic Dictionary
## 5 Experimental Results
### Experimenting with SVD
#### 5.2.1 Not Using SVD  
This experiment was carried out without applying any SVD. Translation matrices were initialized with random numbers. Figure 1 shows that similarity values are monotone increasing, meaning that the system is learning. However, the learning process is relatively slow since even after 200 epochs the similarity score is still quite low, bearing in mind that the optimal value is 1.0.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
###### Abstract  
The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users' power consumption and improved the spectral efficiency.  
s _electronics_ 2023, 12, 1795. [[https://doi.org/10.3390/electronics12081795](https://doi.org/10.3390/electronics12081795)]([https://doi.org/10.3390/electronics12081795](https://doi.org/10.3390/electronics12081795))  
3
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Evaluation
#### 4.2.2 Network Lifetime Analysis  
In dynamic path routing, as against fixed path routing, multiple paths are created. In fixed path routing, a communication is carried out through a single path as long as that path exists. That path is as good as the first node on that path is depleted due to the energy level reaching zero a after certain number of communications through that path, let us say \(n\). Since, in dynamic path routing, \(k\) number of paths can be chosen in the case of dynamic path routing with \(kn\) networks lifetime, this means \(k\) times network lifetime can be extended. For example, if each have 1 joule of energy and one communication costs 0.1 joules of energy consumption on each node, then network lifetime of that path is \(n=10\) events. In case there are \(k=3\) paths available, which can be chosen alternatively, then the upper limit of network lifetime extension is 3 times as compared to fixed path routing, which is significant.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 4 Orthogonal Multiple Access (OMA)
### URLLC Rate  
To evaluate the per-UE URLLC rate under OMA and for a given URLLC target error probability \(\epsilon_{U}\), we follow the approach in [32], which is reviewed here. URLLC packets are generally short due to the strict latency constraints, and the maximum achievable rate can be computed by leveraging results from finite blocklength information theory. To this end, fix a given blocklength \(n_{F}\) and URLLC error decoding probability \(\epsilon_{U}^{D}\). Notice that this probability is different from the general URLLC target error probability \(\epsilon_{U}\), as detailed later in this section. According to [40], the URLLC rate can be well approximated by  
\[R_{U}=\log_{2}(1+\beta^{2}P_{U})-\sqrt{\frac{V}{n_{F}}}Q^{-1}(\epsilon_{U}^{D }), \tag{28}\]  
where  
\[V=\frac{\beta^{2}P_{U}}{1+\beta^{2}P_{U}} \tag{29}\]  
is the channel dispersion and \(Q^{-1}(\cdot)\) is the inverse Q-function (see Section 1.5).  
The error probability for URLLC packets is the sum of two contributions. The first represents the probability that an URLLC packet is discarded due to blockage, given that only one URLLC packet can be transmitted within the required \(L_{U}\) worst-case latency; while the second is the probability that the packet is transmitted but not successfully decoded. Accordingly, the overall error probability can be computed as  
\[\Pr[E_{U}]=\sum_{n}^{L_{U}-1}p(n)\frac{n}{n+1}+\sum_{n}^{L_{U}-1}p(n)\frac{1}{ n+1}\epsilon_{U}^{D}, \tag{30}\]  
where \(p(n)=\Pr[N_{U}(L_{U})=n]\) is the distribution of the binomial random variable \(N_{U}(L_{U})\sim\text{Bin}(L_{U}-1,q)\) representing the number of additional packets generated by the URLLC UE during the remaining minislots between two transmission opportunities. The decoding error probability \(\epsilon_{U}^{D}\) in (28) can be obtained from the URLLC reliability constraint in (13), i.e., \(\Pr[E_{U}]=\epsilon_{U}\).
## 1 Introduction
Although only one unit of the photoacoustic cell is irradiated by the laser, at Helmholtz resonance frequency, both units in the DHR generate acoustic signals of the same size and opposite phase. It reduces non-resonant noise and has low requirements for light beam manipulation [29]. In particular, the two arms of the resonance tube can be used as measuring channels and/or reference channels and share a locking amplification module, which is particularly beneficial to multicomponent detection. The simultaneous detection of trace ethylene and low carbon alkanes has many important applications. Examples include exhaled gas [30], agriculture, and dissolved gas analysis (DGA) in transformer oil [10]. In this paper, we used a differential Helmholtz resonance (DHR) [27; 31; 32; 33; 34; 35] photoacoustic cell configured with condenser microphones and an ICL source to detect traces of CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{6}\) under ambient pressure. These three components could be well quantified simultaneously by using a multiwavelength fitting algorithm to eliminate cross-interference.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### Controllers
#### 4.4.3 Model Predictive Control  
The concept of Model Predictive Control encompasses a range of control methods that explicitly utilize a process model to generate control signals through the minimization of an objective function. These methodologies share common structural elements, including the following:  
* A predictive model for determining process evolution at future time instants.
* An objective function minimization process.
* Implementation of a finite and sliding control horizon, which involves computing the control sequence across the entire horizon, executing only the first signal, and iterating the process at each sampling instant.  
Figure 14 presents the general structure for predictive control implementation. The system employs a model to forecast process output or state evolution based on known input and output signals. An optimizer calculates future control actions while considering the cost function and applicable constraints.  
In the same way, the parameter values used with the MPC controller were selected from [31].  
For the simulation, the following considerations were established, which are indicated in Table 9.  
Figure 14: General structure of an MPC.  
Figure 13: PD-PI + fuzzy control.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Simulation Conditions** \\ \hline Sample time & 0.001 [seg] \\ Total simulation time & 6 [seg] \\ External forces & Step function \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulation conditions for MPC.
## 3 Results and Discussion
### Relationship among Permeability, Number of Nematodes, and Biomass Concentration  
Figure 8 presents the relationships among stabilized permeability, the number of nematodes, and active biomass concentrations. As shown in Figure 8a, the number of nematodes increased at higher biomass concentrations on the membrane surface (\(R^{2}\) = 0.8), indicating that biomass concentration in the fouling layer could be responsible for the higher number of nematodes. Abundant biomass could provide not only a favorable habitat for nematodes but also main food sources [10; 41]. In Figure 8b, permeability increased with increasing biomass concentration in the fouling layer (R\({}^{2}\) = 0.74). This result was contrary to reports that the fouling layer deposited on membrane surfaces decreased permeability due to pore blocking and/or pore constriction [42; 43; 44]. However, the presence of eukaryotes (i.e., nematodes in this study) inhabiting the fouling layer increased the permeate flux by 50-170% during GDM filtration in a previous study [9]. Similarly, permeability was found to increase with the increasing number of nematodes in this study (\(R^{2}\) = 0.72). Metazoan organisms (e.g., nematodes) can graze and feed on a wide range of food sources (bacteria, algae, organic detritus, and protozoa) within the fouling layer. Moreover, an improved population of nematodes results in a significant reduction of the basal layer [9]. The predation of eukaryotes could have decreased the density and thickness of the fouling layer, resulting in the formation of heterogeneous and porous fouling layer structures. Therefore, a rougher membrane surface appears to accumulate more biomass in the fouling layer, which is favorable for eukaryotes to inhabit, thereby enhancing permeability in GDM filtration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Membrane** & **Closest Match (Accession Number)** & **Identity Score (\% Similarity)** \\ \hline PES-UF & N.D. a & - \\ \hline \multirow{4}{*}{PVDF-UF} & _Eumolntystera_ cf. _hungarica_ 1 JH-2014 (KJ636237.1) & 98 \\  & _Monltystera_ cf. _paludicola_ _JH-2014_ (KJ636247.1) & 98 \\  & _Plectus minimus_ (KC206040.1) & 99 \\ \hline \multirow{4}{*}{PTFE-MF} & _Monltystera_ cf. _paludicola_ _JH-2014_ (KJ636247.1) & 95 \\  & _Plectus aquatilis_ (GQ892827.1) & 100 \\  & _Plectus minimus_ (KC206040.1) & 99 \\  & _Plectidae_ sp. (AJ966508.1) & 100 \\  & _Rhabdolaimus aquatic_ (AY284729.1) & 93 \\  & _Rhabdolaimus terrestris_ (AY284729.1) & 99 \\  & _Rhabdolaimus_ cf. _terrestris_ _JH-2004_ (KJ636250.1) & 96 \\ \hline \end{tabular} a  
\end{table}
Table 3: Nematode species observed on the membranes after GDM filtration.
## 4 Related Work
There have also been other attempts to compare the impact of different RPL routing attacks on RPL networks. In [47], a comparison of the VN, WP, and DIS flooding attacks was presented considering both single- and composite-attack scenarios in relatively large-scale setups. The simulation results showed that the RPL network was more affected by the VN and DIS flooding attacks than the WP attack in terms of QoS performance, power consumption, and network overhead. Other comparison studies were presented in [48; 49] to show the impacts of the single and composite VN, rank, and hello flooding attacks. The results indicated noticeable network performance degradation in terms of QoS measures, energy consumption, and network overhead. The impact of certain attacks including hello flooding, selective forwarding, clone ID, sybil, and local repair attacks on network performance was analyzed in [50]. The experimental results showed RPL networks under these attacks experienced low network throughput. The evaluation study presented in [51] highlighted the ability of single and composite rank, local repair, neighbor, and DIS flooding attacks to degrade QoS performance and increase network overhead. However, there are still certain aspects and considerations that have not yet been effectively studied and analyzed as indicated in Table 1. The focus has been mainly on experimenting with RPL networks under certain routing attacks in relatively simple and small-scale setups. There have been few attempts towards investigating composite RPL attacks in more complex scenarios. Challenging RPL networks with combinations of different attacks in varying-scale hybrid-attack scenarios has not been effectively analyzed yet. Considering all these aspects in a multidimensional study that sheds empirical light on the security performance of RPL networks is the main aim of this research work. It provides a reference study for addressing advanced security support against more complex RPL attack scenarios of large-scale setups and hybrid routing attacks.
## 4 Experimental Results and Analysis
### Analysis of Generalization Performance Experiment Results
#### 4.4.1 Analysis of Experimental Results of Different Data Sets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data II** & **mAP (\%)** & **Parameters (M)** & **FLOPs (G)** \\ \hline YOLOv8n & 93.6 & 3.0 & 8.2 \\ Improved YOLOv8n & **94.4** & **1.7** & **4.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of Data II before and after improvement. Figure 13: Data II recognition result diagram. (**a**) Original image; (**b**) YOLOv8n recognition result; (**c**) improved YOLOv8 recognition result. represents mild processing, the central column signifies moderate processing, and the rightmost column denotes severe processing). The improved network model demonstrates outstanding generalization performance when faced with the new dataset, including detection results in adverse environments such as dusty haze, low illumination, and mild blurring. However, it still exhibits relatively weak resistance to severe motion blur. From our perspective, this phenomenon is considered normal, as human eyes or vision behave similarly: as motion blur increases, human judgment capability tends to decline.
## 4 Experimental Results and Simulation  
In this section, we obtain the performance of the proposed control algorithm in three parts. Firstly, we set the target value and simulation parameters. We use the 24-h wind power output data as the target value. Secondly, according to control algorithm in Section 3, we use a real wind power trajectory within 15 min to simulate in detail, and a tracking trajectory over the period of a day is provided. Finally, this paper analyzes the simulation error and the performance of the control algorithm.
## 4 Evaluation Criteria and Experimental Results
### Automatic Labelling (SentiWordNet)  
Sentiment classification is an indication of the task of sentiment analysis which is a sub-field of natural language processing. The lexicon approach is applied to extract the opinion of each aspect by using SentiWordNet, which determines whether the text content specifies a positive or a negative review. Opinion extraction and automatic labelling are carried out in three steps: (1) applying part-of-speech tagging to each sentence; (2) extraction of all the opinion words and detecting the polarity of each opinion word; (3) looking for a negation word that is close to any opinion word, and once it is found, the polarity is reversed. Opinion words are usually represented in the adjective, adverb, and verb forms such as \"like\" or \"really\" which affect the final result. For instance, the sentences \"I like pizza\" and \"I really like pizza\" both contain positive opinions, but the second sentence is more positive. Opinion words can be identified after applying POS tagging for each sentence, and it is typically found near the aspect. The accuracy of SentiWordNet performance was measured by applying SVM classifier and five-fold cross-validation. The overall results of the accuracy for each domain areshown in Table 5. The results are compared with the related work where SentiWordNet and SVM classifier have been used for different sentiment analysis tasks. The results indicate that the accuracy of 'Restaurant' scores has recorded the highest percentage with 69.4%, while 'Movie' comes second with 65%, and the lowest score is recorded by the 'Domestic Saudi Airline' with 63.2%. The percentage distribution of the sentiment polarity for each aspect of the three domains is presented in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline
**Domain** & **Accuracy (SentiWordNet)** & **Accuracy of Related Work** \\ \hline Movie & 65\% & 53.33\% [45], 79\% [46] \\ Restaurant & 69.4\% & 53\% [47], 56\% [48] \\ The Domestic Saudi Airlines & 63.2\% & 65.2\% [49], 46.41\% [50] \\ \hline \end{tabular}
\end{table} 
Table 4: The performance of the proposed model and standard model across three domains. \begin{table}
\begin{tabular}{c c c c} \hline
**Domain** & **Accuracy of the Proposed Model SA-LDA** & **Accuracy of the Baseline Model** \\  & **(LDA with Seed Words)** & **(LDA without Seed Words)** \\ \hline Movie & 83.3\% & 54\% \\ Restaurant & 86.7\% & 41\% \\ The Domestic Saudi Airlines & 80\% & 32\% \\ \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of reviews using SVM and comparison of related work results.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Sample Homogenization--Dry, Wet and Cryogenic Grinding
#### 2.1.3 Cryogenic Grinding
Diversity of food commodities that need to be analyzed for aflat makes it impossible to establish a universal milling protocol for all sample matrices. Instead, food commodities can be classified according to composition complexity including fat, protein, water, pigments and other interferences. Products that are similar in composition can often be homogenized by the same or slightly modified methods. Based on the class of the product, past experience and the literature record of milling applications, the most appropriate milling practice can be selected and developed in a practical manner.
## 4 Results and Analysis
### Rebar Stresses  
The observation results of the rebar stresses gauges and stress sensors of the tunnel surrounding rock and masonry lining are shown in Figure 12. Generally, if the data of rebar stresses were suddenly changed, the safety of the tunnel would be affected.  
Figure 11: Contrast curve of measured settlement and total residual settlement of the tunnel sidewalls.  
Figure 10: Subsidence of the side wall of the tunnel. (**a**) Subsidence of the left line side wall. (**b**) Layout of subsidence markers at the side walls. (**c**) Subsidence of the right line side wall.  
Figure 12: The observation curve of steer bar meters and stress box. (**a**) Force of the steel bar meter at the left line. (**b**) Stress of the stress box in surrounding rock at the left line. (**c**) Stress of the stress box in lining mould concrete at the left line. (**d**) Plane layout of stress sensors and rebar stress gauges. (**e**) Force of the steel bar meter at the right line. (**f**) Force of the steel bar meter at the right line. (**g**) Stress of the stress box in lining mould concrete at the right line.  
From the observed results of the left and right walls and the top of the tunnel, the tensile stress in most sections of the tunnel is at a low level, generally approximately \(-10\) to \(20\) kN on the left line and \(-5\) to \(15\) kN on the right line. The maximum value of the rebar stresses gauge was observed only at the left wall in the middle of the left line, and the value was \(45\) kN (equivalent to \(2.69\) MPa when converted based on a diameter of \(146\) mm).  
The observation results show that the tangential stress in all sections of the tunnel is at a low level, among which the maximum observed tangential stress in the right-line wall rock is \(-1.2\) MPa, and the maximum tangential stress observed in the lining of the left line is \(4.2\) MPa, which is far lower than the shear strength of rock and concrete.  
According to the numerical simulation results in the previous stability study, the calculation results show that the normal stress in the radial direction of the tunnel may reach \(2\) to \(4\) MPa, and the tangential shear stress may reach \(\pm\)\(1.5\) MPa, which is considered insufficient to cause damage to the surrounding rock and lining of the tunnel.  
As can be seen by comparing the stability research results and monitoring results of the stress distribution of the surrounding rock, they are both in the same magnitude range, and the difference between them is very small. The actual observed values are also much lower than the shear strength of the rock and the concrete itself. Therefore, the stress distribution of the surrounding rock of the tunnel is in the normal range at the present stage, and the tunnel is in safe operation.