esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data  
#### 2.1.1 Eddy Covariance Measurements  
The eddy covariance observations used in this study were downloaded from the global flux network (the FLUXNET2015 dataset, [[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 17 May 2023)), the Integrated Carbon Observation System (ICOS warm winter 2020 ecosystem eddy covariance flux product, [[https://www.icos-cp.eu/data-products/2G60-ZHAK](https://www.icos-cp.eu/data-products/2G60-ZHAK)]([https://www.icos-cp.eu/data-products/2G60-ZHAK](https://www.icos-cp.eu/data-products/2G60-ZHAK)) (accessed on 29 January 2024)), and the Ameriflux network ([[https://ameriflux.lbl.gov/about/about-ameriflux/](https://ameriflux.lbl.gov/about/about-ameriflux/)]([https://ameriflux.lbl.gov/about/about-ameriflux/](https://ameriflux.lbl.gov/about/about-ameriflux/)) (accessed on 29 January 2024)). Based on the eddy covariance observations, cropland ET and various meteorological variables are originally observed at high frequency (typically 10 or 20 Hz) and are then integrated at half-hourly to yearly temperature resolution after standard processing. In this study, half-hourly observations from 2000 to 2020 were used, and the variables included the timestamp (TIME), air temperature (TA), water vapor pressure deficit (VPD), longwave incident radiation (LW), friction velocity (USTAR), ecosystem respiration from the nighttime partitioning method (RECO), sensible heat flux (H), and latent heat flux (LE) (Supplementary Table S1). Referring to some previous studies [28; 30; 31], we performed data filtering and quality control procedures during the data processing and finally selected 16 cropland sites with multi-year crop rotations mainly located in the United States and Europe for analysis (Figure 1, Supplementary Table S2) [39].
## 2 Materials and Methods
### Release Tests  
The ability of the produced active packaging to release the bioactive compounds with antioxidant properties was evaluated by release tests in a food simulant. Sample disks of 9 cm diameter were submerged in 20 mL of ethanol 10% \(v/v\) as a food simulant (food simulant A), gently stirred by an orbital shaker at 250 rpm at room temperature [27]. Samples of the food simulant were collected over time, centrifuged at 35,273\(\times\) g, 4 \({}^{\circ}\)C, for 10 min (Alliance Bio Expertise, MF 20-R, Seine-Port, France) and analyzed by High Performance Liquid Chromatography (HPLC) for caffeine concentration analysis and by UV-vis spectrophotometry to assess the scavenging ability of the active agent released. An HPLC Agilent 1100 Series (Palo Alto, CA, USA), coupled with a Diode Array Detector and equipped with a C18 reverse-phase column (Vydac, Hesperia, CA, USA) was used for caffeine detection. Samples of 100 \(\upmu\)L of the supernatant of the centrifuged food simulant were injected without dilution for each run. Mobile phases water/acetic acid (99:1%, \(v/v\)) (solvent A) and methanol/acetonitrile (50:50%, \(v/v\)) (solvent B) were used and the solvent gradient was set as follows: 100% A for 5 min, from 5% to 30% B for 25 min, from 30% to 40% B for 10 min, from 40% to 48% B for 5 min, from 48% to 70% B for 10 min, from 70% to 100% B for 5 min, isocratic at 100% B for 5 min, followed by returning to the initial conditions (10 min) and column equilibration (12 min). A flow rate of 1.0 mL/min was used at 30 \({}^{\circ}\)C.  
Caffe concentration in the samples was determined from the area under the caffeine peak on the chromatograms by the calibration curve obtained from standard solutions of caffeine (0.0256-0.256 mg/mL), reported in Equation (1) (R\({}^{2}\) = 0.9995).  
\[C\ \left(\frac{mg}{mL}\right)=\frac{Area}{105644} \tag{1}\]  
The radical scavenging capacity of the SCG extract released from the polymeric support was determined by the ABTS\({}^{\star+}\) assay and expressed as percentage inhibition [28]. The ABTS\({}^{\star+}\) working solution was prepared by adding K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) to an aqueous ABTS 7 mM solution up to a final concentration of 2.45 mM. The absorbances of the working solution (A\({}_{\text{blank}}\)) and of 50 \(\upmu\)L of sample mixed for 2 min with 1 mL of working solution (A\({}_{\text{sample}}\)) were measured by an UV-vis spectrophotometer (Lambda 25, Perkin Elmer, Wellesley, MA, USA) at 734 nm. The percentage inhibition was calculated according to Equation (2).  
\[\text{Inhibition }(\%)=\frac{\left(\text{A}_{\text{blank}}-\text{A}_{ \text{sample}}\right)}{\text{A}_{\text{blank}}} \tag{2}\]
# An Optimization Framework for Wind Farm Design in Complex Terrain
## 2 Wind Farm Modelling
### Wake Model
Note that \(A^{ol}_{ij}(\theta^{\infty})\) can be calculated based on the wake zone radius, \(R_{wake}=R_{i}+\ \alpha\cdot d^{down}_{ij}(\theta^{\infty})\), the rotor radius, \(R_{j}\), and the relevant crosswind distance, \(d^{cross}_{ij}(\theta^{\infty})\), using the method described in [14]. Additionally, \(\alpha=0.075\) is used in this study. Denoting the set of indexes of all the turbines upwind of turbine \(j\) as \(I^{up}\), and using the energy deficit balance assumption [14], we can then calculate the effective wind speed of turbine \(j\) under the far field inflow wind condition, \(v^{\infty}\) and \(\theta^{\infty}\), as:  
\[\overline{v}_{j}(v^{\infty},\theta^{\infty})=v_{j}(v^{\infty},\theta^{\infty })-\sqrt{\sum_{i\in I^{up}}\big{(}\Delta v_{ij}(v^{\infty},\theta^{\infty}) \big{)}^{2}}. \tag{5}\]  
A more detailed description of the wake model governed by Equations (3)-(5) is referred to in [14]. Figure 2: Schematic of the adapted Jensen wake model for a wind turbine in complex terrain.
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 3 Cluster Validity and Visualization Techniques
### Problem of Number of Clusters  
Choosing the right number \(k\) of clusters, especially in the \(k\)-means algorithm, is very difficult. Because clustering is part of exploratory data analysis [12], there is no right answer as to which \(k\) is useful in the respective case. Therefore, this problem is also present in addition to actually solving the clustering. The right choice of \(k\) is often equivocal because it depends on the distribution of the points within the data set and the idea of the user for the desired output. A larger number of k leads, namely, to a reduced error; however, it is not expedient if each data point is assigned to its own cluster [13]. Then the error is zero, and \(k\) is equal to \(n\) as the number of data points. For the \(k\)-means algorithm, there are several approaches available.  
One method is an extension of \(k\)-means, where the determination of the optimal number of clusters is achieved by using many iterations of the algorithm and a stopping rule based on the Bayesian information criterion (BIC). The method chooses the optimal number of clusters based on information theory [14]. Another method is using the clusters as silhouettes. These silhouettes are based on the comparison of their separation and tightness, which leads to an evaluation of whether objects lie well within their cluster or not [15]. A further heuristic method is the elbow method. The method shows the explained variation as a function of the number of clusters [16]. The \"elbow\" is the cutoff point where the optimal number of clusters is identified. One more cluster does not lead to much more accurate results.  
In this paper, the different methods will not be applied or evaluated for certain data sets. However, different cluster-centered visualization (CCV) techniques from Section 4 show the problem of the right choice of the cluster number.
## 4 Discussion
### Water Management
#### 4.2.2 Agriculture  
Agricultural areas that rely on meltwater for irrigation will be affected by the redistribution of water resources caused by changes in the timing of snow and ice melt [18, 52, 56, 57]. _Edible sunflower_, which is planted in early May and harvested in late September, is the most important agricultural crop in the SAM. Our field investigations indicate that the _Edible sunflower_ is irrigated nine times during the growing season (May-September), with the highest irrigation demand occurring between late June and early August (Figure 7a). A forward shift in the water distribution caused by an earlier melt (Figure 5) requires an additional increase in the storage capacity of reservoirs to store more meltwater in spring to meet the irrigation demand in summer. Furthermore, the rapid expansion of agricultural acreage in the Ulungur River Basin (Figure 7b) will lead to additional increases in water stress.
## 2 Materials and Methods
### Dataset Composition
Due to the high number of sensors available among UAVs and planes, two reference sensors were chosen for UAV and plane datasets to ease the process and to obtain spectral and radiometric features usable for comparison and benchmarking. Tetracam micro MCA Snap 6 (Tetracam Inc., Chatsworth, CA, USA) was used as the reference sensor for UAVs [43], while Leica DMC III [44] was the reference sensor for plane platforms. Although more commonly used cameras for UAVs exist, the Tetracam micro-MCA Snap 6 was chosen since, in terms of number of bands (six), it is in between professional cameras for UAVs and those used for research and development, which usually have 4-5 and 10-11 bands, respectively. The sample flights considered for this study were 2500 m and 100 m above ground level for planes and UAVs respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Platform Name** & \begin{tabular}{c} **Minimum Area** \\ \end{tabular}  & \begin{tabular}{c} **Price per** \\ **Hectare** \\ \end{tabular}  & \begin{tabular}{c} **Data Volume** \\ **per Hetare** \\ \end{tabular}  & \begin{tabular}{c} **Spatial** \\ **Resolution * \\ \end{tabular}  & \begin{tabular}{c} **Radiometric** \\ **Resolution * \\ \end{tabular}  & \begin{tabular}{c} **Revisit** \\ **Resolution** \\ \end{tabular}  & \ \begin{tabular}{c} **Data** \\ **Time** \\ \end{tabular}  \\ \cline{2-7}  & **ha min** &
\begin{tabular}{c} **EUR ha\({}^{-1}\)
## 7 Prospective Aspects of WebAssembly
### Need for Garbage Collection and Resource Management  
One of the significant future directions for Wasm involves integrating garbage collection and advancing its resource management capabilities. The absence of automatic garbage collection is a known limitation in the current Wasm specification, and addressing it will mark a significant milestone.  
* **The Impact of Garbage Collection** Garbage collection has a profound impact on software development. The automatic memory management provided by garbage collection removes the burden of manually tracking and freeing up unused memory from developers, prevents memory leaks, and reduces the likelihood of certain types of bugs. In the context of Wasm, the addition of garbage collection support could drastically simplify the process of compiling high-level garbage-collected languages to Wasm. Languages such as JavaScript, Python, and many others use garbage collection, and their direct compilation to Wasm would be more straightforward with the introduction of garbage collection support.
* **Resource Management Advancements** In addition to garbage collection, future improvements to Wasm's resource management capabilities are expected to evolve. Currently, Wasm operates with a linear memory model, which offers simplicity and performance advantages but presents limitations. As IoT devices continue to grow in complexity and capability, more sophisticated memory management techniques may be needed. Future developments could see the emergence of more advanced memory models in Wasm, including support for shared memory between modules or even between threads. This would allow for more complex multi-threaded applications and for modules that can communicate and share data more efficiently.  
* **Garbage Collection and IoT** In the specific context of IoT, the addition of garbage collection to Wasm could provide significant advantages. IoT devices often need to run for extended periods without manual intervention, and can ill afford memory leaks or other resource management issues that can disrupt their operation. Thus, the inclusion of automatic garbage collection would enhance the reliability and robustness of Wasm-based IoT applications.
# Absorption-Translocation of Veterinary Antibiotics in Rice Plants Introduced with Irrigation Water
## 3 Results and Discussion
### Absorption-Translocation and Bioconcentration of VAs into Rice Plants
The study showed that onions, cabbage, and corn take up chlortetracycline from soils mixed with VAs containing manure. Small concentrations of chlortetracycline (2-17 ng g\({}^{-1}\) fresh weight) accumulated in plant tissues in a dose-dependent manner. BCF values were calculated to evaluate the rate of uptake of antibiotics from the soil by plants. The mean BCF values for the brown rice, roots, and stems of rice plants grown in the experimental plots are presented in Table 3. BCF was calculated using the formula described by [PERSON] et al. [18]. The uptake and translocation of VAs by the rice plants varied significantly. Interestingly, CTC and OTC were more easily translocated in the rice plants than AMX. AMX, a member of the \(\beta\)-lactam class, accumulated at the lowest concentration compared to CTC and OTC across all treatments. However, the BCF values in this study were extremely low. The low BCF values for antibiotics suggest that rice plants have a low ability to absorb and retain antibiotics in their tissues. Even only at trace levels, the accumulation of VAs in brown rice may be harmful to human health through ingestion. The findings indicate that these widely used veterinary antibiotics might hamper crop production, leave residues in the soil, and pose risks to human health via bioaccumulation if introduced into the agro-ecosystem unintentionally. \[\text{BCF}(\%)=\frac{\text{Concentration root}+\text{Concentration aerial part}}{\text{Initial concentration in external medium}}\] \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{3}{c}{**AMX**} & \multicolumn{3}{c}{**CTC**} & \multicolumn{3}{c}{**OTC**} \\ \cline{2-10}  & **Root** & **Stem** & **Brown Rice** & **Root** & **Stem** & **Brown Rice** & **Root** & **Stem** & **Brown Rice** \\ \hline A-1 & 0 & 0 & 0 & 0.181 & 0.200 & 0 & 0.148 & 0 & 0 \\ A-2 & 0 & 0 & 0 & 0.050 & 0.057 & 0 & 0.055 & 0.055 & 0.001 \\ A-3 & 0 & 0 & 0 & 0.046 & 0.052 & 0.007 & 0.050 & 0.049 & 0.002 \\ \hline B-1 & 0 & 0 & 0 & 0.043 & 0.049 & 0.010 & 0.046 & 0.045 & 0.002 \\ B-2 & 0 & 0 & 0 & 0.043 & 0.053 & 0.010 & 0.046 & 0.044 & 0.002 \\ B-3 & 0 & 0 & 0 & 0.048 & 0.063 & 0.012 & 0.053 & 0.052 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bioconcentration factor (BCF) of different organs of rice plants grown in the experimental plots. Figure 5: Concentrations (\(\mu\)g kg\({}^{-1}\)) of oxytracycline (OTC) detected in the roots, stems, and brown rice of rice plants grown in the experimental plots. The bars represent the standard error; A-1, A-2, A-3, B-1, B-2, B-3, C-1, C-2, and C-3 are described in Table 2.
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 4 Methodology
### Detection of Deepfakes  
For deepfake detection, a Voice Activity Detection (VAD) method is employed to select the segments that contain speech. Then, the well-known Mel-Cepstral Frequency Coefficients (MFCC) [39] of the audios are extracted. The classifiers used include SVM [40] with a linear kernel, RF [41], KNN [42] and, Naive Bayes [43].  
In this work, although most of our references employ deep learning techniques, these simple classifiers are chosen since the focus of this paper is to analyze the influential factors in both generation and detection in real-life situations and not to obtain the best classifier, despite the limited size of our dataset. Deep learning models, which rely on extracting complex patterns from large volumes of data, typically require a considerable number of samples to train effectively and generalize well to new inputs. Given that our dataset is small, these models could overfit, which would compromise their performance. Furthermore, simpler models already demonstrated satisfactory performance in our task, making the application of more advanced techniques unnecessary at this stage. Therefore, we leave the implementation of deep learning models for future work, for when we will have a larger dataset available.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 6 Performance Evaluation
### Performance Evaluation of the Proposed Defense System for the Normal Operations of SV Systems  
In our experiments, we first study the impact of the proposed defense methods, i.e., denoising and noise-adding, on the normal operations of an SV system. Here, the noise-adding method applies the Gaussian noise. Specifically, we use the EER as the performance metric. Recall that the EER is the rate of FAR or FRR when they are equal. A smaller EER reflects a better SV system. Moreover, the added plugin function to an SV system would slow down the processing of an input audio. As a result, we also record the processing time in our experiments. Among five legitimate speakers provided, we select two speakers with the identifier of 2830 and 61 as registered users in two separate experiments. To obtain the EER, 100 audio clips from a registered speaker and 100 audio clips from all illegal speakers were tested.  
Table 1 shows the average performance of a GMM SV system with or without the denoising or noise-adding function over speakers 2830 and 61, as well as with different values of the standard deviation of noise (i.e., \(\sigma=0.001\), 0.002, and 0.005). The results in the table are the averages over two speakers. Note that the original GMM SV system, which is without the defense plugin, can be regarded as the special case when \(\sigma=0\) for either denoising or noise-adding. It can be observed that for most cases, when \(\sigma\) increases, the EER increases for both denoising and noise-adding. However, the value of the EER only increases slightly, especially when \(\sigma\) is not large; for example, \(\sigma\leq 0.002\). It can also be observed from Table 1 that in a GMM SV system, the denoising function takes much longer time than the noise-adding function to process all 200 testing audios. The overhead of the noise-adding is very light, because the plugin of adding random Gaussian noise does not require too much computation. On the other hand, the denoising function needs to apply both STFT and iSTFT operations, which demand a lot of computation.  
We further summarize the performance of the proposed defense system for normal operations of an i-vector SV system in Table 2. Similar to the results in Table 1, it can be observed that the EER does not increase too much when \(\sigma\) increases, especially when \(\sigma=0.001\) or \(0.002\). Different from the results in Table 1, the processing time of an i-vector SV system with the defense is closer to that without the defense. The reason is that the i-vector SV system requires longer time to test all 200 audios than the GMM SV system, but the overhead of a plugin is fixed.  
From both Tables 1 and 2, we can conclude that the proposed defense system only slightly degrades the performance of an SV system, especially when \(\sigma\) is small. Moreover, in a GMM SV system, the noise-adding function is preferred to the denoising function, as it adds on much shorter processing time.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Non-ML Detection  
This utilises a rule-based mechanism for bit detection in received signals, specifically designed for telecommunications applications. This function evaluates the real part of the received signal; if the real part is greater than zero, the bit is classified as '1'; otherwise, it is classified as '0'. This threshold-based approach enables efficient bit detection without the need for complex signal processing techniques.  
One of the primary advantages of this method is its capability for real-time detection. Non-ML methods provide rapid, real-time detection of anomalies or bit errors, which is crucial in telecommunications where low latency and high-speed processing are essential [30; 31]. Additionally, these methods are easy to implement, as they do not require the extensive data collection and labelling necessary for training machine learning models. This simplicity can be beneficial in scenarios where rapid deployment and operational efficiency are prioritised. However, the non-ML detection method has certain limitations. Threshold-based detection methods exhibit limited adaptability to varying signal conditions and sophisticated interference attacks. In dynamic and hostile environments, their performance may degrade since they are not designed to adjust to changes in signal characteristics. Moreover, the accuracy of these methods can be lower compared to ML-based approaches, particularly in complex environments with high noise levels and multiple interference sources. The fixed threshold may not account for nuanced variations in the signal, leading to potential misclassifications.
## 2 Overview of [PERSON]'s Theories for Non-Uniform Sediment Transport
### Critical Bed-Shear Stress of Non-Uniform Sediment
#### 2.2.2 Critical Bed-Shear Stress for Deposition  
Similar to the Krone formula [34], the deposition rate _D(k)_ for the \(k\)-th fraction of non-uniform sediment is as follows:  
\[D(k)=\left\{\begin{aligned} &\omega_{k}S_{k}\Big{(}1-\frac{\tau_{b}}{ \tau_{dk}}\Big{)}&\quad for\;\tau_{b}\leq\tau_{dk}\\ & 0&\quad for\;\tau_{b}>\tau_{dk}\end{aligned}\right. \tag{5}\]where \(\omega_{k}\) is the settling velocity; \(S_{k}\) is the sediment concentration by weight, and \(S_{k}=P_{k}S\); \(P_{k}\) is the percentage of non-uniform suspended sediment; \(S\) is the total sediment concentration, and \(S=\sum_{k=1}^{N}S_{k}\); \(N\) is the total number of sediment fractions; and \(\tau_{dk}\) is the critical bed-shear stress for deposition.  
The critical shear stress for deposition \(\tau_{d}\) is a key parameter for determining sedimentation rates. The research shows that the incipient velocity must reach approximately 1.2 times the ceasing velocity [35]. Thus, the critical bed-shear stress for erosion should be approximately 1.44 times the critical shear stress for deposition, and the critical bed-shear stress for deposition of the coarse particles can be expressed as follows:  
\[\tau_{dk}=0.022\frac{(\rho_{s}-\rho)gD_{k}}{\xi_{k}} \tag{6}\]  
Sun [6] suggests that flocculation of fine suspended particles is caused by molecular attraction, so the flocculation of fine particles can be parameterized by molecular attraction. Flow depth H in the cohesive force term can be neglected because the hydraulic pressure does not work for suspended particles. Therefore, the critical bed-shear stress for the deposition of cohesive non-uniform sediment can be expressed as follows:  
\[\tau_{dk}=0.022\left[\frac{(\rho_{s}-\rho)gD_{k}}{\xi_{k}}+1.5C_{0}\left(\frac {S}{S_{c}}\right)^{l}\left(\frac{D_{m}}{D_{k}}\right)^{0.5}\frac{\rho gh_{0} \delta}{\xi_{k}D_{k}}\right] \tag{7}\]  
where \(S_{c}\) is the critical sediment concentration for the transition from normal flow to hyperconcentrated flow. The value of \(S_{c}\) exceeds 200-300 kg/m\({}^{3}\) for riverine flow [36]. Formula (7) reflects the increase in critical bed-shear stress for deposition caused by molecular attraction between fine particles, which increases the settling probability of the cohesive fine particles, i.e., \(1-\tau_{b}/\tau_{dk}\).