esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Methods
### Ozone Treatment of Herbs
#### 3.3.2 Negative Aspects of the Ozonation of Plant Raw Materials  
The conducted experiments [48; 49] prove that in case of some species and parts of plants, the impact of ozone causes permanent tissue damage.  
The seedlings of common basil (_Ocimum basilicum_) and sage (_Salvia officinalis_) subjected to a 2-week ozonation showed no noticeable changes; also, the chlorophyll fluorescence parameters (indicating a deterioration of the physiological state) of the leaves of both of these plants, compared with the parameters of seedlings of the control lots, not treated with ozone, did not differ significantly. However,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Gaseous Ozone**} & \multicolumn{2}{c}{**Aqueous Ozone**} \\ \hline
**Temperature (\({}^{\circ}\)C)** & **Half-Life (min)** & **Temperature (\({}^{\circ}\)C)** & **Half-Life (min)** \\ \hline \(-\)50 & 3 months & 15 & 30 \\ \hline \(-\)35 & 18 days & 20 & 20 \\ \hline \(-\)25 & 8 days & 25 & 15 \\ \hline \(20\) & 3 days & 30 & 12 \\ \hline \(120\) & 1.5 h & 35 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ozone half-life based on temperature [37].  
in case of lemon balm (_Melissa officinalis_), a species related to those mentioned above (all the three belong to the _Labiatae_ family and have similar morphology and life cycle), whose seedlings were provided with identical conditions as in case of basil and sage, noticeable changes were observed after 6 days of exposure to the same doses of ozone. Apart from significant discrepancies in the chlorophyll fluorescence values in the lemon balm leaves treated with ozone, in relation to the control samples, a visible damage took place. Necrotic spots up to 2 mm in diameter appeared on both sides of the leaves, resembling the symptoms of hypersensitive-like response. The symptoms of ozone poisoning were particularly bright in older leaves that turned yellow and withered. Both lemon balms, sage and basil, belong to common and important herbal raw materials, but only the former showed significant sensitivity to ozone. The mechanism of resistance of basil and sage to the toxic impact of ozone should probably be explained by the rapid reaction of the antioxidant system of these plants, because ozone, after having penetrated through stomata, creates various reactive oxygen forms that damage the inside of cells. From among the plants of herbal importance, among others, plantain, dandelion, nettle, and soap dish are ozone resistant [23]. The described experiment refers to naturally living plants--a separate issue, requiring thorough examination, is to determine whether ozone has a negative impact, e.g., on the level of terpenoids in oil obtained from lemon balm.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 2 Related Work  
It is difficult to detect eavesdropping attacks in WSNs since eavesdroppers passively snoop the confidential communications with concealment of their presence. Encryption is one of the most commonly used techniques to protect confidential communications, which is shown to work effectively in WLANs (e.g., WEP [10], WPA and WPA2 [11]), in cellular networks (e.g., CMEA [8] and KASUMI [9]) and in wireless personal area networks (WPANs) [18]. However, applying such cryptography-based techniques help hiding the meaning of the information being transmitted, but not the existence of the information itself. In addition, the techniques are designed to make it computationally difficult for the adversary to understand the true meaning of the information while the adversary is still able to access all the information [19]. Furthermore, it is quite challenging to apply the conventional ciphers (encryption algorithms) to WSNs due to the following inherent constraints of WSNs [12]: (a) the inferior computational capability of wireless nodes; (b) the limited battery power of wireless nodes; (c) the difficulty of managing the distributed sensor nodes in the centralized manner. In addition, the cryptographic authentication and identification in higher layer will introduce a significant computational overhead [20].  
There are a number of anti-eavesdropping counter-measures in WSNs. We roughly categorize them into three types: (i) lightweight encryption schemes [21; 22; 23; 24]; (ii) generating artificial noise to limit the amount of information that can be extracted by eavesdroppers [25; 26; 27]; and (iii) mitigating the eavesdropping risk by controlling the transmitting power [28]. Table 1 summarizes these schemes.  
In particular, a number of lightweight encryption schemes based on physical layer features of wireless networks have been proposed [21; 22; 23; 24]. The main idea of physical-layer encryption schemes is to exploit the inherent randomness of communication channels so that the amount of information that can be extracted by an eavesdropper is mitigated. However, the encryption schemes are still computational intensive and power-consuming.  
Some recent studies [25; 27] exploit the artificial noise generated by RFID readers to alleviate the eavesdropping capability of malicious nodes. However, these schemes can only be applied to the scenarios of Internet-of-Things (IoT) based on RFID. Besides, a transmitting power control method is proposed in [28] to mitigate the eavesdropping risk while to adjust the transmitting power may deteriorate the legitimate communications [29].  
Although [15; 16; 17] also proposed an approach similar to our Fri-Jam schemes, their methods are mainly designed for the IoT scenario, in which jammers are placed outside the fence surrounding the boundary of the network. These schemes are not feasible to WSNs since eavesdroppers can appear at any location in WSNs. Besides, most of the studies on protective jamming schemes [15; 16; 17] are mainly focused on a specific scenario.
# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
## 3 Proposed Method
### Training Model  
This part describes a 3-step meta-updating technique for training the student model and the meta-weigher. Figure 2 shows the workflow. Using this technique, MMS can weight-matching data instances using the most recent feedback (\(\mathit{Loss}_{\mathit{Stu}}\)) from the student model. The calculated weights may coordinate three sub-tasks in ABSA and reduce the effects of noise in \(D_{\mathit{mix}}\).  
According to \(\mathit{Loss}_{\mathit{Stu}}\) loss sequence, Once updated, the student model from \(ts=0\) to \(ts=1\) is based on the following Equation.  
\[\theta^{1}_{\mathit{stu}}=\theta^{0}_{\mathit{stu}}-\lambda_{\mathit{stu}}\ \
abla_{\theta_{\mathit{stu}}}\ \propto_{m}\ \mathit{Loss}_{\mathit{Stu}}z \tag{12}\]  
_TimeStep_ presents by \(ts\). \(\propto_{m}\) is a hyperparameter. The learning rate is shown by \(\lambda_{\mathit{stu}}\) for the SM. However, the meta-weigher is not updated at this stage. Then, the student model applies data with gold labels to compute \(\mathit{Loss}_{\mathit{Lab}}\). Its assistance with the meta-weigher leads the student model to the suitable gradient way. Utilizing the revised student model parameter \(\theta^{1}_{\mathit{stu}}\) and gold-labeled data, \(\mathit{Loss}_{\mathit{Lab}}\) is computed. Next, the meta-weigher is updated by the following Equation.  
\[\left(\theta^{1}_{\mathit{mix}}=\theta^{0}_{\mathit{mix}}-\lambda_{\mathit{ mix}}\ \
abla_{\theta_{\mathit{mix}}}\ (\propto_{m}\ \mathit{Loss}_{\mathit{Stu}}\Big{|}\theta^{1}_{ \mathit{stu}}\right) \tag{13}\]  
The learning rate for the meta-weigher is presented by \(\lambda_{\mathit{mix}}\). \(\mathit{Loss}_{\mathit{Lab}}\) is only applied for training meta-weigher at this stage, without modifying other methods.  
Then, MMS gains an updated meta-weigher \(ts=1\). Next, the student model's principle was previously changed using the data from the current batch, and the meta-weigher may direct the gradient more appropriately. The meta-weigher takes the feedback of the existing batch from the student model \(\mathit{Loss}_{\mathit{Mix}}\) as input, the model of the original student \(ts=0\), in the following Equation, officially updated with the supervision technique of the upgraded meta-weigher.  
\[\left(\theta^{1}_{stu}=\theta^{0}_{stu}-\lambda_{stu}\ \
abla_{\theta_{stu}}\ \left( \alpha_{m}\ \ Loss_{Stu}\Big{|}\theta^{1}_{mix}\right)\right. \tag{14}\]  
where \(\ Loss_{stu}^{2}\) is correspondingly calculated with \(\ Loss_{Lab}\) that relies on \(\theta^{0}_{stu}\) and \(\theta^{1}_{mix}\). Algorithm 2 shows the three-step meta-updating complete process. Algorithm 2 is used to compute the time costs without/with, and the results show that conducting a three-step meta-update will cost an additional 0.40 times the run cost.  
Figure 2: Demonstrates how the student model and meta-weigher are updated by MMS from the first-time step \(t=0\) to the second-time step \(t=1\).
## 3 Key Technologies Achieved by the System
### Sensor Data Compensation and Correction
#### 3.1.1 Nh\({}_{3}\) Concentration Compensation and Correction  
The output voltages of the NH\({}_{3}\) sensors are 0-3 V and converted to NH\({}_{3}\) concentrations through AD conversion. The acquisition node controller uses an STM32F103 VET6 MCU (Shenzhen Spring Electronic Technology CO., LTD, Shenzhen, China) whose ADC reference voltage requires an external connection. Affected by mains voltage fluctuation, AD acquisition is not accurate. Therefore, two paths of AD acquisition are designed: One directly acquires ADC reference voltage, and the other acquires the output voltages of the NH\({}_{3}\) sensors. NH\({}_{3}\) concentration is computed proportionally and accuracy of NH\({}_{3}\) concentration acquisition is raised. At the same time, filtering processing is performed using a median average filter to eliminate the effects of randomly arriving impulse interference on the accuracy of measuring results.
## 5 Simulation Results
### Impact of \(M\)  
As analyzed in Section 4.3, system availability is affected by the number of accessed UTs. In Figure 10, we provide the BER performance with \(M=3\). It can be clearly noticed that the performance degradation of the proposed method is marginal at UTs compared with the spectrum isolation scheme. In addition, the BER results are more promising than those in Figure 8. Consequently, we can derive satisfactory results by adjusting the number of accessed UTs.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Genetic Algorithm  
The Genetic Algorithm (GA) is the most popular among the Evolutionary Optimization algorithms and it is based on the three basic operators: selection, crossover, and mutation.  
The GA implemented in this work is real-coded due to the intrinsic continuous nature of the variables of this problem. For what concerns the selection operator, one of the two parents is selected with the stud selection, which extracts the best individual of the population at every iteration. The GA with this operator is often considered as a separate algorithm called Stud-GA [29]. This modification of the GA has been used because it has been proved to be more effective than the GA itself on antenna applications [30]. The other parent has been selected with a roulette wheel selection with pressure ration equal to 1.  
The crossover operator implemented is an arithmetic crossover that is specifically designed for real-coded problems. The crossover probability is set to 1. A Gaussian mutation has been used for creating the offspring generation, with a mutation rate equal to 0.1. The mutation amplitude, defined as the standard deviation of the Gaussian random distribution, is equal to 0.06.
## 2 Theory
### Opto-Acoustic Method
#### 2.2.3 Proof of Principle
After all, the harmonic response evaluated by the Fourier spectrum, like Figure 3 (2), represents the behavior of the substrate-film interface. According to Equation (5), the peak value of a Fourier spectrum, like Figure 3 (2), is proportional to \(J_{0}(\delta)=J_{0}(kd)\). To validate this proportionality, an additional experiment was conducted. In this experiment, we varied the oscillation amplitude of the transducer surface by changing the amplitude of the electric input and measured the peak value of the resultant Fourier spectrum. Here, since the square of the voltage is proportional to the electric power and the square of the oscillation amplitude is proportional to the mechanical power, the input voltage and the oscillation amplitude are proportional to each other. Figure 3 (3) plots the spectral peak value as a function of the input voltage to the transducer. The three plots are for the untreated and treated Ti-Au specimens and the bare silicon specimen. The driving frequency is 10.42 kHz. Also plotted in Figure 3 (3) is a theoretical curve computed for each specimen based on Equation (5). Since the transfer function from the amplitude of the input voltage to the amplitude of the film-surface displacement is unknown, the theoretical curve is adjusted in the form of \(J_{0}(aV)\), where \(V\) is the input voltage and \(a\) is a fitting parameter used for each curve. Physically, the parameter \(a\) corresponds to the transfer function from the transducer voltage to the film-surface oscillation. Reasonable agreement is seen between the experiment and theory.
## 4 Compressed Sensing Enabled Network Tomography
The results of the performed simulations demonstrate that only low degree nodes are necessary to initiate random walks (measurements).
## 7 Open Issues and Challenges
### Secure Data Transmission  
The CH is in-charge of data gathering and compilation. Because clustering in WSN captures extremely sensitive data from a hostile environment, it must be conveyed without any malicious intent, attack, or change. It is important to avoid hostile attacks on the network and critical to use stringent and powerful authentication procedures. WSNs are vulnerable to a variety of attacks, including denial of service and manipulation, which can cause nodes, CHs, or whole networks to be disconnected [69; 70].
## 2 Related Works
### Duty Cycling
ASCENT limits the packet loss due to collisions because the node density is taken into account as a parameter and has good scalability properties. Sparse topology and energy management (STEM) is a technique proposed by [PERSON] et al. (2011). It was noticed that (most of the time) the network only monitors the environment in anticipation of an event occurring. Therefore, STEM uses two different radio signals. Each node periodically turns on its wakeup radio for a short time to listen if there is any neighbor attempting to communicate with it. When a source node needs to communicate with a neighboring node, it sends a stream of beacons on the wakeup channel. Upon receiving a beacon, the target node sends a wakeup acknowledgment and turns on its data radio. The actual data packets are transmitted through this radio. After the data transmissions have ended, the node turns its data radio off again. Results show that STEM can reduce the energy consumption of the network; however, it also increases the setup latency. A pipelined tone wakeup (PTW) scheme for WSN is proposed by [PERSON] et al. (2011). Similar to STEM, PTW works with two different channels for transmitting wakeup beacons and the actual data packets. The difference relies on what happens after receiving wakeup beacons and the wakeup procedure is pipelined with the packet transmission. When a node receives a beacon, it sends a wakeup acknowledgment and turns on its data radio; at the same time, the receiver node will send a wakeup beacon to wake up all its neighbors. The pipeline process reduces the wakeup latency and, consequently, the overall message latency. The main difference between the proposed ECKN and the existing works found in the literature is the fact that it is a joint localization, routing, and energy-aware mechanism. Other existing works take each of these issues separately, or at most two, simultaneously, e.g., localization and routing.
## 7 Applications and Future Research Directions of RIS
### RIS as Reconfigurable Reflectors to Deal with Non-LOS Phenomenon  
In most wireless communications technologies, the router's transmitters direct a signal to ensure that it reaches receivers. The larger the size of said receivers, the better the reception. However, connected nodes represent a challenge when it comes to integrating such components. The complexity of a mobile radio environment makes it difficult to predict the propagation in this environment simply. Knowing the signal level at any point of the coverage is essential to assess the quality of coverage in this environment. This quality will sometimes be degraded, even for a good level of the signal received, particularly for the frequency selectivity of the propagation channel. Several models, methods, and commercial tools are offered to model electromagnetic propagation in complex environments and whose improvements are constantly evolving. In an indoor environment, the fundamental phenomena of the propagation of electromagnetic waves depend on the nature and dimensions of the materials encountered (electrical and magnetic properties) and the characteristics of the incident wave (frequency, polarization, angle of incidence).  
The presence of other radiant sources may interfere with the useful (transmitted) signal and generate a degradation of the signal received from the electromagnetic waves. These interferences mainly come from other radio equipment. Therefore, it is important to study this parameter to predict the reception quality in terms of signal to interference ratio. One of the most interesting use cases for using RISs in wireless networks is to use them as reconfigurable reflectors when the LOS route is either blocked or not powerfulenough to help cell-edge users--solving non-LOS scenarios. RISs, for example, can be easily connected to indoor walls or floors and can be built into outdoor building.
## 2 Materials and Methods
### Field Trial: Experiment 1  
Field trials were performed in an agricultural field located in General Teran, Nuevo Leon, Mexico with a geographical location of 25\({}^{\circ}\)16\({}^{\prime}\)00.0\({}^{\prime\prime}\) N 99\({}^{\circ}\)41\({}^{\prime}\)00.0\({}^{\prime\prime}\) W. The first trial was started in mid-February 2019 as an early season corn. The main agricultural products of this region include citrus, corn, sorghum, wheat, livestock, among others. In this zone, maize crops are generally affected by _S. frugiperda_, and a phytosanitary alert program is implemented for the three corn cultivation cycles (early, intermediate and late) of the region (peaks of _S. frugiperda_ expected in March, May and September) [27]. Since farmers normally used _Bacillus thuringiensis_ to control them, no artificial inoculation of _S. frugiperda_ was done in any of the field experiments. One-hectare field was prepared using a tractor; 38 furrows of 100 m length and 25 furrows of 80 m length were made, with a row-to-row distance of 80 cm. Using the seed inoculation procedure, mentioned in Section 2.2, 3300 _Z. mays_ seeds of \"criollo maize\" race (kindly donated by the field owner, without any insecticides or fungicides) were inoculated with _B. bassiana_ strain GHA, with a concentration of 1 \(\times\) 10\({}^{6}\) blastospores/mL and methylcellulose as adherent; 2500 seeds were used as a negative control without any fungal or adherent treatment. Seeds were planted during mid-February 2019 on the furrows, with a separation of 25 cm between each seed, the distance between each row was 80 cm, and in each row, 100 seeds were planted, which were monitored every week. The first 33 furrows were used to plant _B. bassiana_-treated seeds, then five furrows were left without seeds, and the remaining 25 furrows were used to plant negative control treatments (Figure S3). In this experiment, the germination percentage at the third week after planting was recorded, and the presence of _S. frugiperda_ larvae between the fifth true leaf and the tenth true leaf period was monitored by visually scouting. Other than watering every day, neither fertilizer nor pesticides were applied in the field during the whole experiment.
## 3 Data Processing and Methodology
### Short-Term Traffic Speed Prediction Models
As MAE takes absolute values, it has a characteristic that it is relatively not affected by the outlier compared to MSE and RMSE.
## 8 Security Analysis
### Achieving Security Properties
#### 8.4.2 Preserving Accountability  
The use of blockchain ensures that the data are stored immutably and can be retrieved for verification. Once the root of the MHT is on the blockchain, it cannot be changed. Similarly, the address of the MHT stored on IPFS remains unchanged. This ensures that the integrity of the MHT is maintained and can be independently verified.  
Due to this, the broker cannot deny having sent a flow of messages once it has been included in the MHT. The cryptographic proofs provided by the hashchain and MHT (seeassumption **hash function properties**) ensure that the message was part of the published set, thereby achieving accountability. Additionally, due to the assumption **account ownership**, a broker cannot deny having published a certain root.  
Similarly, also, the publisher cannot deny having computed a certain root from the flows of messages sent. This is because once this root is computed independently from the broker, it has to send a transaction invoking a function of the smart contract with its computed root as input. This transaction is immutably stored on the blockchain and it can be publicly verifiable.  
Therefore, if a subscriber wants to provide proof of having received a certain flow of messages (in a given temporal round) from the publisher, it simply has to provide the path from the related hash chain to the root written in the transaction executed by the publisher.