esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Experience Results
#### 5.2.2 Performance for Different Bandwidth  
In this part, we increase the system bandwidth from 20 M to 40 M to validate the performance of the method with different bandwidths. As shown in Table 8, we also design scenes 2.1-2.4. The performance gains are ranging from 18-31%.  
Figure 14: Performance comparison for each user in scene 1.4.  
Figure 15: Average experience rate for different methods in scene 1.  
Compared with Table 7, we can find that under the same load and user distribution, the network-level user experience improves with the increase in bandwidth, and in the case of large bandwidths, the advantages of our approach are further amplified. Because more frequency-domain RB can be used in this situation, more RB combinations can be selected by different users under multi-user arrangement, which is equivalent to an increase in freedom in resource allocation. Figure 17 shows the average experience rate for different methods and scenes. Although the available RB of PRB utilization-based methods also increases, our method can better coordinate time-frequency-space three-dimensional resources, leading to higher gain. Additionally, the average experience rates for different methods and scenes are shown in Figure 17.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development
#### 5.2.2 Emergence of Nonlinear Control  
As computing power increased, researchers began to explore nonlinear control techniques for more accurate and agile control of UAVs. Techniques such as adaptive control and model predictive control started gaining attention [96].
# Functional Analysis of Two Terpene Synthase Genes Isolated from the Flowers of _Hosta_ 'So Sweet'
## 2 Materials and Methods
### Expression of Recombinant Hs_TPS1_, _Hs_TPS2_ in _E. coli_ and _In Vitro Enzyme Assay_  
The full-length sequences of Hs_TPS_ genes without terminator were amplified using specific primers and then inserted into the pET-28a expression vector with a T7 promoter. The recombinant expression vector was inserted into Rosetta (DE3) competent _E. coli_ cells by heat shock and sequenced to confirm its identity. The _E. coli_ was grown in Luria-Bertani (LB) medium according to the instructions for competent cells. The recombinant protein was induced by 0.5 mM isopropyl-\(\beta\)-d-thiogalactopyranoside (IPTG), incubated for 16-20 h at 16 \({}^{\circ}\)C with shaking at 185 rpm. The soluble protein was purified using a 6\(\times\)His-Tagged Protein Purification Kit (Cown Biosciences, Beijing, China) following the manufacturer's instructions. The reaction mixture used for enzyme activity consisted of 50-100 \(\upmu\)g of recombinant HsTPS protein, 1 mM GPP (Sigma) or FPP (Sigma) as the substrate, 25 mM Tris-HCl (pH7.8), 5 mM dithiothreitol as the buffer, and 10 mM MgCl\({}_{2}\) as the cofactor in a total volume of 2 mL. Enzyme activity was measured after 2 h incubation in hexane at 30 \({}^{\circ}\)C.
## 3 Cascaded Fuzzy Reward System (CFRS)  
One crucial aspect of DRL algorithms is the reward function, which fundamentally shapes the agent's learning strategy and the direction for network optimization. Crafting an ideal explicit reward function to meet long-term goals is a formidable task, chiefly because mapping relationships from complex state spaces to reward values can be nonlinear, making the manual description of the relationships between reward components highly challenging [33]. Initial research focused predominantly on single-objective optimization, primarily centered on position control [34; 35], simplifying the reward function as follows:  
\[r_{pos}=-e_{pos}=-\big{|}\big{|}p_{c}-p_{t}\big{|}\big{|} \tag{2}\]  
Here, \(||\cdot||\) represents the Euclidean norm, \(p_{t}=(x_{t},y_{t},z_{t})\) is the position vector of the target yarn spool, \(p_{c}=(x_{c},y_{c},z_{c})\) denotes the current position vector of the Tool Center Point (TCP), and \(e_{pos}\) refers to the position error of the TCP. However, solely using the reward function defined in Equation (2) to guide the Critic's assessment of the current strategy proves insufficient [2]. In specific scenarios like path planning for textile robotic arms, multiple factors such as energy consumption and safety need consideration, often entailing conflicts and trade-offs. Therefore, a flexible approach is required to balance these aspects. Researchers have proposed multi-objective optimization methods [36], balancing multiple factors through the linear combination of different objective functions. However, this approach can render the model complex and difficult to interpret, and computational efficiency becomes a concern in large-scale problems. To address this, [PERSON] et al. introduced a method known as the FRS [13]. Integrating prior expert knowledge into the reward system, FRS can comprehensively evaluate various aspects of robotic assembly tasks. Not only does this system enhance learning efficiency, but it also prevents the agent from getting trapped in local optima. However, this method might face difficulties in handling nonlinear and conflicting objectives.  
This section will detail the additional factors considered, the philosophy of the FRS, and its construction process.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 2 Experimental Part
### Characterization of Pure and Zn-SnO\({}_{2}\) Nanoparticles  
X-ray diffraction (XRD) analysis was conducted using the PanAnalytic X'Pert Pro instrument from Malvern Instruments, Malvern, UK. XRD examination was utilized to ascertain the crystal structure and level of impurities in the synthesized NPs. The morphologies and distribution of these NPs were examined using FE-SEM (JSM-7600F, JEOL, Inc.) and FE-TEM (200 kV, 2100F, JEOL, Inc., Tokyo, Japan). The elemental composition of samples was determined using energy-dispersive X-ray spectroscopy (EDX). Fourier transform infrared (FTIR) (PerkinElmer Paragon 500, Waltham, MA, USA) spectroscopy was performed in order to investigate the functional groups present in the nanoparticles (NPs). The optical study was carried out by applying a UV-visible spectrophotometer (Hitachi U-2600, Tokyo, Japan) and a photoluminescence (PL) spectrometer (Hitachi F-4600, Hitachi, Tokyo, Japan).
## 2 Materials and Methods  
### Study Domain  
The study domain encompasses four separate regions across California and includes three individual wine grape vineyards being monitored as part of the USDA-ARS Grape Remote sensing Atmospheric Profile and Evapotranspirationpiration eXperiment (GRAPEX) [4] and three individual almond orchards being monitored as part of the USDA-ARS Tree crop Remote sensing of Evapotranspirationpiration eXperiment (T-REX) (Figure 1). Vineyards include BAR (Sonoma County near Cloverdale, CA, USA), SLM (Sacramento County near Galt, CA, USA), and RIP (Madera County near Madera, CA, USA). BAR is a 10-ha plot composed of Caberent Sauvignon grapes trained on a split trellis and drip irrigated. Vines were planted in 2010 with 1.8 m vine spacing and 3.35 m row spacing and have a southwest-northeast row orientation. SLM is a 35-ha plot composed of Caberent Sauvignon grapes (regrafted in the winter of 2020) trained on quadrilateral cordons with 3.35 m row spacing, 1.5 m vine spacing, west-to-east row orientation, and drip irrigation. RIP is a 31-ha block composed of Chardonnay grapes planted in 2009 and trained on a double vertical trellis with 1.83 m vine spacing and 2.74 m row spacing, west-to-east row orientation, and drip irrigation.  
Almond orchards include VAC (Solano County near Vacaville, CA), WWF (Yolo County near Woodland, CA, USA), and OLA (Madera County near Madera, CA). VAC is a 70 ha seventh-leaf orchard with 100% Independence variety and a southwest-to-northeast row orientation with drip irrigation. WWF is a 60 ha ninth-leaf orchard with 50% Nonpareil, and 17% Butte, Monterey, and Carmel varieties, north-south row orientation, and micro-sprinkler irrigation installed. OLA is a 20 ha eighth leaf orchard with 50% Nonpareil, 37% Wood Colony, and 13% Supareil, north-south row orientation, and two-line drip irrigation.  
All vineyard sites have a cover crop present during the winter season. Once conditions become dry in the early summer, the cover crop is either left to senesce or removed via moving so that water is used exclusively by vines. The northernmost site, BAR, typically has more cover crop biomass that also remains longer into the growing season in comparison with the other two vineyards. This is due to greater precipitation and higher quality grape grown at the site, which requires a cover crop to regulate soil water availability. Almond orchards OLA and VAC have native vegetation that grows in between rows that is also present during the winter season before either being moved or allowed to senesce during the early summer months. WWF has a cover crop consisting of a seeded mix of legumes and grasses, which is present from winter through spring and then moved after senescence.
## 3 Discussion
### Effect of Climatic and Geographical Characteristics on THC and CBD Content Variation  
In general, correlation analysis and PCA results revealed a positive correlation between temperature variables and the two target chemical metabolite contents, and a negative correlation between latitude as well as elevation and metabolites content (Table 1; Figure 4). It is clear that higher temperatures promote cannabinoid biosynthesis, as found in this research. We also found a negative correlation between THC and CBD concentrations, but it was not significant. The THC and CBD biosynthesis pathways have been elucidated and show that cannabinoid acid (CBGA) is a prerequisite for both CBD and THC biosynthesis and then follows two pathways to synthesize carboxylic acids (THCA or CBDA), and these acidic forms of cannabinoids, upon heating or smoking, decarboxylate to their neutral forms (THC and CBD) [13; 21]. THCA synthase and CBDA synthase enzymes that catalyze the reaction of THCA to THC and CBDA to CBD, respectively, compete with each other for CBGA and expedite neutral cannabinoids creation and their levels. The negatively correlation between latitude and THC and CBD contents supported by a study reported that latitude decreases can result in cannabinoids level reduction, and plants from high latitudes exhibited a low \(\Delta\)9-THC [78].  
Moreover, this study indicated that the populations at the high elevation showed a trend towards lower concentrations of THC and CBD. For example, Sam-01 at 1858 m above sea level (maximum value of elevation) had lower cannabinoids, and Dez01, Dez-02, and Bsh-01 at a low elevation had higher amounts of THC and CBD compared to other locations studied. The two populations located in the warmest state of Iran, including Dez-01 and Dez-02, had the highest concentrations of THC, consistent with the positive effect of temperature on cannabinoid and growth. Overall, these results showed that environment is also likely to play a role in cannabinoids concentration, suggesting that controlled environment studies or multi-year trials should be completed to further elucidate the importance of these G \(\times\) E interactions.
## 5 Experimental Data Analysis
### Dial Gauge Data Analysis
#### 5.1.2 Side Convergence Analysis  
From Figure 10, it can be observed that during the first 10 loading cycles, there was almost no displacement in the sides of the opening cut. The left side (Gauge 1) recorded a convergence of 1.222 mm, while the right side (Gauge 4) measured only 0.276 mm. At this stage, there was no significant damage to the rock mass on either side. During the subsequent 10 loading cycles, the convergence rates of the sides accelerated, with the left side experiencing greater convergence than the right side. The changes on the right side were more stable compared to those on the left. This difference can be attributed to the earlier onset of damage at the left shoulder of the opening cut, which led to accelerated deterioration of the rock mass on the left side. Additionally, the loosened area on the left side consistently remained larger than that on the right side. By the 20 th loading cycle, two significant cracks that penetrated the model appeared above the right side, resulting in severe deformation of the left side. The convergence for the left side increased by 5.113 mm in a single measurement. At the end of the loading, the total convergence of the roadway was recorded at 23.478 mm, which is comparable to the average roof subsidence of 24.071 mm recorded by the two gauges. It is important to note that the experimental model was not subjected to lateral pressure during testing. Considering that lateral pressure exists in actual field conditions, the convergence of the sides would likely be significantly greater than the roof subsidence, which aligns with on-site monitoring data regarding mining pressure.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 4 Recovery Procedure  
Consider that MS and BS entities operate in a SA mmWave network in LoS settings of Rician scattering. During the initial access stage, an iterative random search is conducted over all spatial directions at the MS and B. This process returns the best beamforming and combining vectors (best pointing directions) that yield the highest received signal level, modeled as  
\[(u_{\text{MS}},v_{\text{BS}})_{\text{bst}}=\ max(Y_{u,\,v}). \tag{12}\]  
where these best vectors \((u_{\text{MS}},v_{\text{BS}})_{\text{bst}}\) present the maximum principal directions of the primary beams at the MS and BS, which are selected for the data-plane transmission. Now, once the session starts, the spectral efficiency can take various levels based on the link quality. First, when the link is in LoS, it features high link quality without obstacles (blockage parameter is zero), \(\mathbb{I}\ (x)=0\), as well as high instantaneous spectral efficiency, \(\delta_{inst}\).  
When the obstacles in the propagating path become present in the direct link associated with the main beam, it starts to exhibit instantaneous low spectral efficiency, and then blockage mode is in effect. Here the indicator function is set as \(\mathbb{I}(x)=1\), to indicate the LoS-to-NLoS transition. The blockage threshold is set based on the spectral efficient level, as  
\[\delta_{inst}<min\Big{\{}\log_{2}(1+10^{0.1(\text{SNR}-\Omega)}),\delta_{\text {max}}\Big{\}}, \tag{13}\]  
where SNR stands for the signal-to-noise ratio, the variable \(\Omega\) denotes the loss factor (measured in dB), and \(\delta_{\text{max}}\) represents the maximum spectral efficiency [20]. Note that the SNR is expressed as,  
\[\text{SNR}=\frac{P_{\text{tr}}G_{\text{MS}}G_{\text{BS}}|u_{l}|^{2}}{\Psi \Gamma_{0}\bar{6}}, \tag{14}\]where \(\Psi\), denotes the Boltzmann constant, T\({}_{0}\) is the operating temperature, \(\bar{6}\) is the channel bandwidth, and \(G_{\text{MS}}\) and \(G_{\text{BS}}\) are the array gains at the MS and BS respectively. \(G_{\text{MS}}=\ g_{n}\mathbf{U}_{\text{MS}}\), and \(G_{\text{BS}}=\ g_{n}\mathbf{V}_{\text{BS}}\), where \(g_{n}\) is the gain for a single antenna element.  
In light of the above, when the main beam is blocked, the MS and BS initiate the beam-bundle as the backup beams to compensate for the signal losses associated with the main beam, see Figure 3. Therefore, session drops are avoided. The MS here performs maximal ratio combining (MRC) to amplify high beam signals and attenuate weak beam signals, i.e.,
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 4 Threat Model
### Speaker/Speech Sources  
We carried out our attacks using four different sources. The first dataset, RFP [66], is a partial fake audio dataset (it also includes entirely real and entirely fake speech), while the second [67], third [68], and fourth [69] datasets are public and entirely real recording datasets. The last dataset is a custom dataset that we created specifically for the experiments conducted in this study.  
* The RFP [66] dataset comprises three types of audio: real, fake, and partially fake. It includes a total of 127,862 utterances spoken by 354 speakers, of which 184 are male and 170 are female. Eleven different methods were used to produce the fake voices, including seven methods for generating TTS and four methods for VC. This dataset is one of the few publicly available that includes PF audio;
* The UK and Irish English Dialect speech dataset [67] consists of 18,779 spoken utterances by 118 individuals. We opted to use this dataset as it includes participants from six different regions in the UK and Ireland who spoke various English dialects, namely, Irish, Midlands, Northern, Scottish, Southern, and Welsh English;
* The VCTK dataset [68] consists of speech data from 110 English speakers with an age range of 18-38 and varied accents. Each speaker reads around 400 sentences picked from a newspaper, the rainbow passage, and an elicitation paragraph used for the speech accent archive;
* YouTube-8M [69] is a video dataset containing over 7 million videos, each labelled with one of 4716 classes using an annotation system. Our use case involves creating PF audio within a real video, which led us to use YouTube-8M;
* We created a custom dataset for our experiments. First, we collected Alexa commands spoken by 15 native male and female English speakers and used the recordings to perform Alexa attacks. Second, we created PF audio with different fake segment weights to perform AWS cloud and Resemblyzer attacks. Lastly, we created PF audio within an entirely real video and used it in the questionnaire.
# Detecting and Responding to Concept Drift in Business Processes
## 6 Evaluation of the Real-World Hospital Billing Data  
In this section, the HMDP approach is evaluated using a real-world hospital billing (HB) dataset [52]. The HB data were collected between December 2012 and January 2016, containing 18 process activities, 451,359 events and 100,000 cases [53]. There are eleven main operations, covering 99% process instances (98,515), with the discovered control-flow structure presented in Figure 1.  
We validate the HMDP approach in an online learning scenario. That is, we consider a new data batch as arriving in time windows such as a month or a week. In other words, the customer behaviour and environment representation are updated on a daily or weekly basis. Consequently, the observed process instances can be incomplete, i.e., truncated or censored [54]; for example, a process instance started within the observation but has not finished at the end of the observation. In this case, we have no information for its following activities at the time point we make decisions for concept drift detection and model refinement. Furthermore, the number of process instances in a new data batch is changeable depending on the number of applications in that observation window. This is different to the experiments on the artificially created loan applications. In that offline learning scenario, we sample a fixed number of process instances (i.e., the number of process instances is the same in every data batch) that are complete with a starting and ending activity. We split the HB event log into two sets where the first one started from December 2012 to July 2014 as the training stage to optimize the MDP action selection policy and determine the penalty. During the testing stage, data between August 2014 and January 2016 are used for performance evaluation.  
The performance of the five action selection policies is validated under different penalties on a weekly basis, as demonstrated in Figure 8. The HMDP approach performs the best under a penalty of 0.3 and 0.4, and therefore, we use 0.4 in the testing stage. The average reward of the five policies is demonstrated in Figure 9. The horizontal axis refers to the arrival of new data, and the vertical axis represents the reward for each new data. The average reward of each policy is illustrated in the legend area. In general, the HMDP approach can achieve the best performance with an average reward of 1.36, followed by the policy of 'random','retrain' 'keep' and 'adapt'. The HMDP is more stable than other methods with mild fluctuation.  
Figure 7: Performance measure under artificial datasets with a penalty of 0.5. (**a**) Sudden transition drift; (**b**) gradual transition drift; (**c**) sudden duration drift; (**d**) gradual duration drift.
# Food Processing with UHP Waterjets
## 2 Waterjet Characteristics
### Advantages  
There are many advantages, as stated by ref. [2], to using high-velocity waterjets for food cutting. Among these advantages are the following:  
* The waterjet cutting edge is renewable and thus there is no cross contamination.
* Long-life waterjets have diamond orifices and thus there is no need to clean or sharpen blades or disks.
* The waterjet is flexible for cutting a wide range of food products.
* Omnidirectional cutting capability allows for easy shape cutting.
* A waterjet allows for a higher yield of produce as kerf widths are narrow.
* No heat or chemicals are used, only pure water.
* They have the ability to cut through multiple layers of food contents such as cady with nuts.
* The waterjet tool lends itself to automation and intelligent control.
* No metal detection equipment is needed such as that used when cutting with blades.
* High productivity with multiple jets on the same machine is possible.
* They have the ability to cut very soft food products without distortion.
* They have the ability to cut small portions of any shape.
* The waterjet tool can be used inside factories and in the field for harvesting.
* It is highly capable of cutting both soft and hard food items.
* It can easily be integrated into existing production lines, replacing conventional methods.
* The waterjet is approved by regulators such as the U.S. Food and Drug Administration.  
Because of the above advantages, food cutting with waterjets was one of its early applications in the 1970s. Since then, the applications for food processing with waterjets have been significantly increasing and spreading worldwide. Before addressing some of the applications and systems used for cutting, we discuss the tools, i.e., the waterjet characteristics, that make them effective in the different applications.