esa-sceva/satcom-chunk-collection huggingface dataset:
# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 7 Limitations  
Several limitations are inherent in our work. Firstly, the absence of external context poses a significant challenge. Relying solely on the published dataset resulted in a lack of contextual information for each post, thereby complicating the annotation process for annotators. Providing additional context could facilitate a more accurate assignment of posts as sarcastic or non-sarcastic. Additionally, our work is limited to the English language, neglecting potential insights from other languages.
## 5 Simulation Results and Analysis  
In this section, we simulate a squint side-looking SAR with the stripmap imaging mode for the hypersonic platform, where the simulation parameters are listed in Table 1. Specifically, the DPCAM azimuth spectrum reconstruction algorithm is first implemented, and the azimuth pulse response of the reconstructed result is compared with the result of the single channel sampling. Furthermore, as shown in Figure 8, several point targets at different locations are distributed according to the platform characteristics and the wide-swath imaging requirement; then, the imaging algorithm is implemented. Finally, the imaging quality of the targets is analyzed to verify the effectiveness of the ETF algorithm.  
As shown in Figure 8, to achieve a wide swath of 70 km on the ground, 15 point targets are distributed in the SAR imaging scene, including the far and the near edge regions. Here, the illuminated central targets are marked as the dark circle points, and the chroma of the edge targets are weakest. Meanwhile, according to the constraints of the ETF imaging algorithm and the SAR system simulation parameters, the wide swath of 70 km is divided into three imaging scenes, each of which will be overlapped.  
Figure 7: The flowchart of the ETF imaging algorithm for hypersonic platform SAR.
# HydroLab: A Module for the Investigation of Fertigation Strategies in Hydroponics
###### Abstract  
In recent times, hydroponics has gained popularity as a viable solution to the challenges facing traditional agriculture, as it offers an environmentally friendly option with higher crop yields and improved quality, eliminating the need for soil. Despite these benefits, hydroponics still encounters significant difficulties, particularly in optimizing fertigation strategies. The literature contains several studies focusing on test benches for investigating technological recommendations related to fertigation strategies. However, there has been no proposed test bench capable of simultaneously exploring various types of fertigation strategies. In this work, HydroLab is introduced: a hydroponic module that allows for the simultaneous comparison of two types of fertigation strategies and enables remote monitoring and control through IoT functionality. Additionally, HydroLab's potential to generate technological recommendations has been validated through a case study comparing time-based and solar radiation-based fertigation in lettuce cultivation. The case study successfully showcases the capability of HydroLab to investigate and compare different fertigation strategies. Given the global importance of food security challenges, this work is expected to contribute to the investigation of fertigation strategies in hydroponics.  
hydroponics; fertigation; test bench; test bed; automation; IoT Article
## 3 Results
### Round 1 Training and Testing (Model Development and Verification)
On average, from Level 2 to Level 4, sensitivity decreased from 99% to 67%, and precision decreased from 97% to 86%, as shown in Figure 2. This trend indicated a reduced efficiency of the models in accurately identifying true positive cases and in the precision of positive identifications in this setting.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
###### Abstract
The main characteristic of electricity demand is that it is mostly (if not completely) influenced by human behavior patterns [6]. In this regard, human behavior follows a certain tendency with cycling patterns. This is, we humans do mostly the same things (e.g., the time to wake, the time to go to sleep, job schedule, etc.) on a day-to-day basis around the same time (e.g., wake up early in the morning). For instance, most productive human adults work on a working week basis. Hence, a weekday electricity consumption pattern is not only different from weekend patterns, but also different from holidays patterns (which may fall within the working week). This means that every period of time has different needs in terms of electricity demand forecasting and those needs are also different for each type of day. Regarding a more technical aspect, electricity load-forecasting can be performed by using only historical measurements or forecast predictors (i.e., future period loads). For instance, load forecasting can be done 1 h ahead in 15 min interval using only load data from the past hour, or to use the predicted 15 min load within the 1 h ahead horizon. In particular, it has been stated that the incorporation of recursive forecast predictors leads to better performance in time series prediction [7]. Furthermore, if for each forecast horizon (i.e., 15 min ahead) a different model is trained, performance deterioration related to more distant forecasting horizons can be avoided (for instance, by using individual forecasting models for every 15 min) [7]. Therefore, an effective load-forecasting model must consider such patterns. In this paper, we investigate a modeling approach based on association rules (association rules are useful to describe a model in terms of cause and effect). The proposed approach aims at predicting electricity demand for two hours ahead in 8 periods of 15 min. The dataset is from a representative load zone of Mexico, which is 15-min load demand measures. The prediction intervals are estimated using Artificial Neural Network models. Then, the prediction intervals are adjusted through association-rule mining algorithms.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 3 Theory
### Optimization Algorithms
Specifically, the iterative formula for [PERSON]'s method is as follows:  
\[x_{k+1}=x_{k}-\frac{f^{\prime}(x_{k})}{f^{\prime\prime}(x_{k})}, \tag{10}\]  
Here, \(f(x)\) is the objective function to be optimized, and \(f^{\prime}(x)\) and \(f^{\prime\prime}(x)\) are the first and second derivatives of \(f(x)\), respectively. \(x_{k}\) is the solution at the \(k\)-th iteration, and \(x_{k+1}\) is the solution at the next iteration. Compared to first-order optimization algorithms such as gradient descent, [PERSON]'s method can converge more quickly. However, it requires the computation of the first and second derivatives of the variables during the iterative process, which consumes a significant amount of computational resources. Therefore, in practical applications, it is necessary to choose the appropriate optimization algorithm based on the specific characteristics of the problem. (4) Subgradient Method: This algorithm is an iterative method used to solve nonsmooth convex optimization problems and is suitable for situations where the objective function is convex but not smooth. It is an extension of the gradient descent method for non-smooth cases. For a non-differentiable convex function \(f(x)\), at some points \(x\), \(f(x)\) may not have a gradient, but it has a subgradient. A subgradient is a lower bound on the subdifferentials in any direction at that point. Therefore, the subgradient method uses the subgradient in place of the gradient to optimize in the direction of function decrease. The iterative formula for the subgradient method is as follows:  
\[x^{k+1}=x^{k}-\alpha_{k}g_{k}, \tag{11}\]  
Here, \(x^{*}\) is the approximate solution at the \(k\)-th iteration, \(\alpha_{k}\) is the step size, and \(g_{k}\) is the subgradient of \(f(x)\) at \(x^{*}\). When \(f(x)\) is differentiable, the subgradient method degenerates into the gradient descent method. The subgradient method has convergence properties, but it converges slowly because it only uses subgradient information. Therefore, to accelerate the convergence rate, other optimization algorithms such as the quasi-Newton method can be employed.
## 4 Discussion  
Zn\({}_{2}\)TiO\({}_{4}\) with an inverse spinel crystal structure appears to be an interesting material for bioimaging applications, especially upon doping with Cr ion. Cr ions are expected to produce emissions around 700 nm due to d-d type transitions. It has a reported band gap of 3.30 eV (375 nm). In our case, the sample could be prepared at 800 \({}^{\circ}\)C with little noticeable unreacted ZnO, cf. Figure 1. We prepared all further samples at this temperature only. The 375 nm band overlaps exactly with the host absorption in ZnO, suggesting that upon changing the ZnO concentration, the band gap of the material can be modified. We decreased ZnO concentration (used as a precursor) firstly in a small step of a change of 0.02 mol in comparison to TiO\({}_{2}\) concentration. The pXRD pattern shows that the reflection due to Zn\({}_{2}\)TiO\({}_{4}\) remains unchanged while the signatures due to unreacted ZnO decreases slightly (Figure 2). Upon decreasing ZnO in larger step size (0.20 mol), we observe that Zn\({}_{2}\)TiO\({}_{4}\)-related reflections remain the same and sample A10 does not show any unreacted ZnO (Figure 3). We performed Rietveld refinement on this sample and observed a very good correlation between experimental and theoretical data (Figure 4). From the SEM results, we confirm that the smaller particles observed in micrographs were due to ZnO, as their concentration decreases with a decrease in the concentration of ZnO (Figure 5). The most interesting observation among all the studies is the change in ZnO-related band in these samples (Figure 11). A part of these results is also shown in Figure 6.  
Upon modulating ZnO concentration, we observe a decrease in the ZnO-related band, and for sample A10, the 375 nm band vanishes. The pXRD pattern of Zn\({}_{2}\)TiO\({}_{4}\) does not change drastically, and the only change is observed in the unreacted ZnO, which is completely removed now for sample A10. This suggests that the band gap of Zn\({}_{2}\)TiO\({}_{4}\) is modified to 4.40 eV (Figure 7). In some reports, the band is also mentioned to be 4.41 eV [18]. This value is in good agreement with our value. The FTIR results suggest that the samples are hygroscopic in nature, and chemistry-related tools should be used to improve performance in this direction (Figure 8). The prominent emission in the samples is in the UV-A region, which can find suitable applications in biomedical applications. The luminescence decay time of this emission is in the 'ns' regime (Figure 9). Sample A10 also shows PersL with a decay time of 15 min, while the TL glow curves indicate that the sample possesses a deep defect of E\({}_{ac}\) = 1.07 eV (Figure 10). This defect is capable of acting as a reservoir of charges for storage applications.  
Figure 10: (a) TL results of sample A10 after stopping the excitation and after measuring PersL in the sample, and (b) log–log plot of the PersL decay curve. The background signal in TL and PersL is also shown for comparison.
## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.2 Application of PUFs
Similarly, a hybrid approach that integrates blockchain and PUFs is used for device authentication and data integrity that uses PUFs to generate unique device fingerprints [251]. Applications of PUFs include [252; 253]
* Identification is an act of claiming identity with a set of attributes, both physical and perceptual, that uniquely define a specific entity. Similar to a biometric identification scheme, PUF response identification can be used to identify the ICs uniquely. A large range of CRPs is stored in the database along with the device ID implemented with the PUF during enrollment. The verifier chooses a CRP from the CRP database. The identification is considered successful if the obtained response and the CRP database output for a specific input are identical. * Authentication is an act of identity confirmation based on presented attributes. PUFs generate a secure key from intrinsic and inherent entropies created due to variations in the fabricating process. No standard non-volatile storage is needed as randomness is built inside a chip and assures extra protection against the side channel and probing attacks. * SRAM PUFs, RO PUFs, etc., can generate random numbers with slight modifications in their architecture and find their application in real, or cryptographically secure, random number generators. * Potential vulnerabilities like copying or reverse engineering can destroy devices' intrinsic and inherent characteristics and thus modify their output. PUFs are suitable for the generation of secrets in cryptography as they are not kept on the hardware and are generated dynamically at device reset.
applied sciences Article  
Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks  
[PERSON] and [PERSON],+  
Footnote †: journal: Applied Sciences  
The rapid development of technology has resulted in numerous sensors and devices for performing measurements in an environment. Depending on the scale and application, the coverage and size of a wireless sensor network (WSN) is decided. During the implementation, the energy consumption and life of the nodes in the WSN are affected by the continuous usage. Hence, in this study, we aimed to improve the lifespan of the WSN and reduce energy consumption by the nodes during the data transfer using a hybrid approach. The hybrid approach combines Grey Wolf Optimization (GWO) and Dragonfly Optimization (DFO) for exploring a global solution and optimizing the local solution to find the optimum route for the data transfer between the target node and the control center. The results show that the proposed approach has effective energy consumption corresponding to the load applied. Our proposed system scored high in the average residual energy by the number of rounds compared to other methods such as k-means, LEACH-C, CHIRON, and Optimal-CBR. The first dead node was found after 500 rounds, showing that the proposed model has nodes with better reliability. It also showed a comparative analysis of the transmission rate of a packet concerning mobility speed among various methods. The proposed method has the highest ratio at all mobility speeds, i.e., 99.3, 99.1, 99, 98.8, and 98.6, and our proposed system has the lowest computational time of all the evaluated methods, 6 s.  
energy efficient; wireless sensor networks; grey wolf optimization; dragonfly optimization (DFO)
## 4 Array Processing
### Data Independent Beamforming
On the other hand, multiple beams pointing towards a set of azimuth directions \(\mathbf{\Phi}=\{\phi_{sa,1},\phi_{sa,2},...,\phi_{sa,N\phi_{sa}}\}\) are required, so the final error function to be minimized is a function of \(p\) and \(\phi_{sa,i}\) as expressed in Equation (13), with: \(\mathbf{Q}_{i}(p)=\int_{\delta_{i}}-\mathbf{a}(p,\phi)*\mathbf{a}(p,\phi)^{H} \cos(\phi)d\phi\): \[\varepsilon^{2}(p,\phi_{sa,i})=\sum_{i=1}^{R_{\delta}}(\mathbf{w}_{SLL}(p,\phi _{sa,i})-\mathbf{w}_{t,i}(p,\phi_{sa,i}))^{H}\cdot\mathbf{Q}_{i}(p)\cdot( \mathbf{w}_{SLL}(p,\phi_{sa,i})-\mathbf{w}_{t,i}(p,\phi_{sa,i}))\] (13) The minimization problem to be solved is formulated in Equation (14) where \(\varepsilon^{2}_{max}\) is the imposed SLL constraint. Unfortunately, there is no closed-form solution, and an iterative procedure is required. In this paper, the solution proposed in [31] was implemented. \[\min_{\mathbf{w}_{SLL}} \mathbf{w}_{SLL}(p,\phi_{sa,i})^{H}\cdot\mathbf{A}(p)\cdot\mathbf{ w}_{SLL}(p,\phi_{sa,i})\] s.t. \[\varepsilon^{2}(p,\phi_{sa,i})\leqslant\varepsilon^{2}_{max}\] (14)
## 5 Emerging and Potential Opportunities for WUE
### Sensor and Communication Networks  
Sensors are equipment used to collect a range of data such as soil moisture and weather in order to improve agricultural management. Typical examples of sensors used in irrigation water management include soil moisture probes and weather stations. Traditionally, equipment for monitoring crop or soil water status were connected using cables and often required manual reading and the data used to schedule future irrigations. Apart from the inaccuracies that come with using historical data for future water management, such manual processes are time-consuming and often expensive. The use of wireless sensor technologies to improve WUE in irrigated agriculture is on the increase.  
More often, a series of wireless sensors are used to monitor various parameters in the field, for example soil moisture and weather data. This is especially driven by the fact that the recent advancement in technology and competition has led to the availability of cheap sensors. A wireless sensor network consists of a number of individual sensors (sensor nodes), a sink node or hub to receive and process data from the sensor nodes, and a communication technology [32]. The sensor networks may also have actuators that can be used to automate the irrigation system.  
The wireless communication technologies that are used for agricultural purposes including water management are discussed in many texts, for instance [PERSON] et al. [32]. The current communication technologies used in agriculture are ZigBee, Bluetooth, WiFi, GPRS/3G/4G, Long Range Radio (LoRa) and SigFox [33]. The ZigBee technology is commonly preferred in irrigation water management because of the range, low cost, energy efficiency and reliability [32; 34].  
The use of wireless sensors that measure soil moisture, temperature and humidity and relay the data over the 3G internet network is described in Reference [35]. The automation of such crucial data collection means that the irrigation system can be controlled in real time, thereby achieving higher WUE.  
It appears from the literature reviewed that research into water loss through leakage has concentrated on urban water supply distribution networks. The techniques used to detect leakages in urban water distribution networks can be applied in irrigation settings. Pressure sensors connected to a wireless sensor network can play a vital role in in detecting leakages, and thus facilitate faster repair and prevention of further losses [36]. There is also a potential for using smart water technology to detect losses in water pipelines [37].  
There are substantial ongoing research activities involving sensors and communication networks which are likely to lead to improved products and services in the future. It is also likely that communication networks will be used in a more integrated manner to achieve multiple objectives, for example irrigation and urban water supply using smart water meters.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CO Combustion  
#### 3.3.1 Effect of Proportion of NH\({}_{3}\) in Fuel Gas on NO Production  
In this section, we describe the effects of different excess air coefficients and the proportion of ammonia in NH\({}_{3}\)/CO on the amount of NO produced by combustion at different temperatures. The change curve of NO content in combustion flue gas with the proportion of NH\({}_{3}\) in fuel gas is shown in Figure 8. The excess air coefficients were set to 0.9, 1.0, and 1.1; the combustion temperatures were set to 900 \({}^{\circ}\)C, 1100 \({}^{\circ}\)C, 1300 \({}^{\circ}\)C, and 1500 \({}^{\circ}\)C; and the proportion of ammonia varied from 25% to 100%.  
As shown in Figure 8, when the excess air coefficient \(\alpha\geq 1\), NO generation increased first and then decreased with the increase in the proportion of ammonia in fuel gas. When the proportion of ammonia was about 50%, NO generation reached the maximum. When the excess air coefficient \(\alpha<1\), the production of NO gradually decreased with the increase in ammonia proportion.  
Figure 7: NO production of NH\({}_{3}\)/CH\({}_{4}\) co-firing under different fuel gas concentrations at 1500 \({}^{\circ}\)C.  
When \(\alpha\geq 1\) and ammonia ratio was less than 50%, and the CO reaction played a very important role. At very high CO concentrations, carbon monoxide is oxidized almost entirely by reacting with OH radicals [38]  
\[\text{CO}+\text{OH}=\text{CO}_{2}+\text{H} \tag{18}\]  
The hydrogen atoms formed in this reaction can be used to oxidize ammonia,  
\[\text{NH}_{3}+\text{H}=\text{NH}_{2}+\text{H}_{2} \tag{19}\]  
Therefore, under these conditions, CO and NH\({}_{3}\) do not compete for the same free radicals. The presence of high levels of CO promotes, rather than inhibits, the conversion of NH\({}_{3}\) and NO to N\({}_{2}\).  
In addition, a high concentration of CO promotes the formation of HNCO through the NH\({}_{2}\)+CO reaction. However, due to the decrease in the concentration of O and H radicals, the reaction of NH\({}_{2}\) + O to generate HNO is inhibited, and the production of NO is further reduced [39]. [PERSON] et al. [38] also noted that adding CO to a lean NH\({}_{3}\)/NO/O\({}_{2}\)/N\({}_{2}\) flame at 1000 \({}^{\circ}\)C increased the O/H radical pool, thereby increasing the amount of NO produced via the HNO pathway.  
Figure 8: NO production in NH\({}_{3}\)/CO combustion under different ammonia ratios.
## 5 Analysis of Calculation Results
### Cracking Process and Cold Joint Damage  
Figure 11 depicts the entire process of fatigue cracking and cold joint damage states of cold joint components with a casting interval of 8 h (stress level of 1.8 kN, cyclic strength of 0.1 kN). From the figure, it can be observed that:  
1. The developed cohesive fatigue damage element and inverse parameters can effectively simulate the cracking behavior of cold joint components under cyclic loading conditions. When initial stress is applied to the model, no cracking occurs at the cold joint interface. At this stage, only static damage is present at the cold joint interface, and the total damage is less than 1. Subsequently, fatigue loading is applied to the model, and after reaching a certain number of cycles, fatigue damage starts to appear in the cohesive elements. When the accumulated value of static and fatigue damage reaches 1, the corresponding element fails and is automatically removed, resulting in the generation of macroscopic cracks in the model.
2. With an increase in the number of cycles, the cold joint interface elements first experience fatigue damage failure near the pre-existing crack, followed by gradual fatigue damage failure of subsequent elements. This is manifested in the model by a gradual increase in the length of macroscopic cracks. When the macroscopic crack extends to a certain length, the component can no longer withstand static and fatigue loads. At this point, the numerical simulation software indicates a computational error and automatically stops the calculation.  
Figure 9: Changes in fatigue life with cyclic strength. (**a**) 2 h. (**b**) 4 h. (**c**) 8 h.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Evaluating Parameters of Simulation  
#### 3.16.1 Average Throughput  
The level of effective packet delivery via a communication connection is known as throughput. The sum of bits transferred over a connection per amount of measure or the number of received packets is divided by the final packets received minus the very first transmitted packet in this calculation [52]. The number 8 in the equation denotes the number of packets received and translated to bits. It is commonly expressed in bits per second (bps), kilobits per second (Kbps), or megabits per second (Mbps). The higher the throughput, the greater the network performance.  
\[Th_{avg}=\frac{(P_{r}*8)}{(T_{l})-(T_{f})} \tag{18}\]