esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison in Terms of Analog/RF Performance  
As we know, it is very necessary to research the frequency characteristics of the integrated circuits, which is profoundly affected by parasitic capacitances of devices. Apparently, the characteristics of Cgg (gate capacitance), Cgs (capacitance of gate to source) and Cgd (capacitance of gate to drain) is of great significance to evaluate the frequency characteristics and analog application ability of devices [38]. Figure 10a shows the capacitance of the HJLTFET versus Vgs and Figure 10b shows the capacitance of the DMGE-HJLTFET versus Vgs. As can be observed from Figure 10a,b, the trend of capacitance curve versus Vgs was similar for the HJLTFET and DMGE-HJLTFET, Cgg and Cgd both remained at a small value when Vgs \(<\) Vds and increased rapidly with the increasing Vgs when Vgs \(>\) Vds, and Cgs maintained a very small value at this condition. In addition, the curve of Cgg and Cgd was highly adjacent due to the existence of a polar gate which separated the control gate electrode far from the source electrode, as a result, coupling of the control gate electrode and source electrode was relatively slight, so, Cgg was mainly determined by Cgd.  
The cut-off frequency (\(f_{T}\)) and gain bandwidth (GBW) are important indicators for evaluating RF performance of devices. Based on Figure 10a,b, the cut-off frequency (\(f_{T}\)) and the gain bandwidth (GBW) of the HJLTFET and DMGE- HJLTFET are discussed in next section.  
As shown in Equation (3), cut-off frequency (\(f_{T}\)) can be expressed as ratio of gm to Cgg:  
\[f_{T}=\frac{g_{m}}{2\pi\text{C}_{\text{gs}}\sqrt{1+2\frac{C_{\text{gd}}}{C_{ \text{gs}}}}}\approx\frac{g_{m}}{2\pi(\text{C}_{\text{gs}}+\text{C}_{\text{ gd}})}=\frac{g_{m}}{2\pi\text{C}_{\text{gg}}}. \tag{3}\]  
Figure 10: (**a**) Capacitance of HJLTFET versus Vgs; (**b**) capacitance of DMGE-HJLTFET versus Vgs.  
Figure 9: (**a**) Transfer characteristics of the DMGE-HJLTFET with different auxillary gate workfunction (\(\Phi_{\text{M1}}\)); (**b**) the potential distribution with different \(\Phi_{\text{M1}}\); (**c**) the distribution of electric field with different \(\Phi_{\text{M1}}\); (**d**) the ON-state energy band diagram with different \(\Phi_{\text{M1}}\).  
The GBW can be expressed as a ratio of gm to Cgd for the DC gain value equal to 10, as shown in Equation (4) [39; 40]:  
\[\mathrm{GBW}=\frac{g_{\mathrm{m}}}{2\pi 10C_{\mathrm{gd}}}. \tag{4}\]  
Figure 11a,b respectively show the characteristic curves of \(f_{T}\) and GBW of the HJLTFET and DMGE-HJLTFET. Benefitting from a large transconductance induced by a lightly doped source and gate-engineered heterostructure, the DMGE-HJLTFET could achieve a maximum \(f_{T}\) of 423 GHz at Vgs = 0.92 V and a maximum GBW of 82 GHz at Vgs = 0.88 V, respectively. However, the maximum \(f_{\mathrm{T}}\) of the HJLTFET was only 63 GHz at Vgs = 0.98 V and maximum GBW of the HJLTFET was only 72 GHz at Vgs = 1.2 V. Therefore, the DMGE-HJLTFET could be used in future low power and high frequency applications.
## 2 Overview of Privacy Challenges and Disclosure Risks in Organisational Data Sharing
### Challenges in Organisational Data Sharing
However, they are not always consistently followed, often due to factors like data breaches or unauthorised access by malicious actors. For example, in October 2016, a significant data breach occurred at the well-known ridesharing company Uber, resulting in the exposure of 57 million customer and driver personal records. Shockingly, Uber chose not to inform the affected individuals or regulatory authorities about the breach. Instead, they paid a ransom to the hackers to cover up the incident. This breach only came to light a year later, in November 2017, raising substantial concerns about Uber's lack of transparency and failure to comply with data breach notification regulations [24]. Privacy policies offer numerous advantages beyond merely collecting personal data. They also provide crucial information about partners and affiliates with whom data is shared. One of the key benefits of privacy policies is their ability to inform end users about the specific personal data collected. However, these policies often fall short of providing detailed information about the types of personal information shared with affiliates or third-party organisations. An illustrative example is the acquisition of the messaging app WhatsApp by Facebook in 2016. At that time, the app's privacy policies did not explicitly mention that Facebook would collect certain user data, such as phone numbers, for targeted advertising purposes [25]. This situation raised regulatory concerns, suggesting that the information provided to users was not sufficiently clear. Even when organisations comply with privacy regulations by implementing privacy policies to protect personal data or share data with affiliates or third-party organisations, it still presents privacy risks. These risks can potentially empower malicious actors to identify and exploit the data, thereby increasing the risk of privacy breaches [26]. The interaction between privacy policies, data sharing, and the potential for privacy breaches highlights the current challenges and the need to implement alternative data-protection methods within organisations.
## 6 Simulation and Case Study
### Case Study for Generation and Reserve Schemes
Under different penetration levels, a comparison of the results is shown in Table 9.  
From Table 9, it can be seen that the exploitation of renewable power can reduce the generation cost with growing penetration level. However, high penetration of MPPT-based renewable generation may not be feasible under certain reliability criteria. When IBG is under VSG control, it satisfies the operation constraints, and economical dispatch can be achieved.
## 3 Control Algorithm Synthesis
### Adaptive Prescribed Performance Control
 \tag{40}\]  
By substituting Equation (31) into the above equation, one can obtain  
\[\dot{V}=-k_{1}\varepsilon_{1}r_{1}e_{1}-k_{2}\varepsilon_{2}^{2}+\overset{ \sim}{\theta}_{f}\Big{(}\gamma_{f}\dot{\theta}_{f}-e_{2}\Big{)}+\overset{\sim} {\theta}_{u}\Big{(}\gamma_{u}\dot{\theta}_{u}-e_{2}u\Big{)}+\overset{\sim}{ \theta}_{2}^{T}\Big{(}\mathbf{\Gamma}_{2}\dot{\theta}_{2}+\mathbf{e}_{2}\chi\Big{)}. \tag{41}\]  
Finally, by employing the adaptive law (38), we get the following equation:  
\[\dot{V}=-k_{1}\varepsilon_{1}r_{1}e_{1}-k_{2}\varepsilon_{2}^{2}-\frac{1}{2} \gamma_{f}\overset{\sim}{\theta}_{f}^{2}-\frac{1}{2}\eta_{u}\overset{\sim}{ \theta}_{u}^{2}-\eta_{z}\frac{1}{2}\overset{\sim}{\theta}_{z}^{T}\overset{ \sim}{\theta}_{z}+\Xi\leq 0, \tag{42}\]where \(\Xi=\frac{1}{2}\eta/\rho_{i}^{2}+\frac{1}{2}\eta/\rho_{u}^{2}+\frac{1}{2}\eta_{z} \theta_{z}^{T}\theta_{z}\) is a bounded term, which means the tracking errors \(\varepsilon_{1}\) and \(e_{2}\), and the estimation errors \(\hat{\theta}_{i}\) are bounded. According to Lemma 1, the vertical motion is retained within the prescribed bound. Furthermore, if the system is persistently excited (PE) in terms of \(\chi\) and \(u\), then the estimation errors \(\overset{\sim}{\theta}_{i}\) will converge to a small neighbor of zero. This is the end of the proof. **Remark 3**. : _Due to the change in driver's weight, the initial state of the seat suspension, i.e., suspension deformation, could vary in a large range. Compared to the adaptive controller using Barrier Lyapunov Function [19], the proposed controller can mitigate the actuating shock in the startup phase, which is desirable for the driver's comfort and system safety._  
**Remark 4**. : _The proposed controller is robust to modeling uncertainties, ensuring that its performance remains unaffected by changes in system parameters. The convergence rate of the system is guaranteed by the PPFs. Additionally, both transient and steady-state performance can be easily adjusted by tuning the PPF parameters._
## 2 Results and Discussion
### Wounding-Induced Alterations in the SGs Content
The accumulation of **5** and **6** started 2-4 HAW (hours after wounding) in both local and systemic leaves, while an increased production of **3** and **4** was observed 16 and 24 HAW. Accumulation of **5** reached a maximum 24 HAW, whereas 3, 4 and 6 reached their maxima 96 HAW (Figure 1). The accumulation of **2** was not time-dependent and wounding-induced and its amount varied in the range of 1-4 \(\upmu\)g 100 mg\({}^{-1}\) FW. In LL and SL, noteworthy changes in total iridoids (predominantly SGs) content were observed from 24 HAW until 96 HAW. Generally, these results are in accordance with some previous studies that demonstrated that wounding-induced accumulation of specialized metabolites occur in both LL and SL leaves, which further supported the hypothesis that wounding stress signals are transmitted throughout the whole plant [16; 25; 44; 48]. As previously mentioned, SGs biosynthesis in _C. erythraea_ goes through secologanin (**3**), which is a known precursor for numerous monoterenoid indole alkaloids (MIAs) [7]. Comprehensive studies in _C. roseus_ significantly contributed to the knowledge regarding MIAs biosynthetic pathway and its regulation [8; 10; 30; 31; 33]. It has been reported that the MIAs biosynthetic pathway in _C. roseus_ is wounding-inducible [25; 42; 49]. For example, wounding has been found to induce an increase in corynanthe-type MIA accumulation (ajmalicine), whereas iboga MIA (catharanthine) remained unaltered [49]. An enhanced production of specific MIAs in _C. roseus_ has been also observed upon mechanical injury by herbivores attack [25]. Thus, the largest increase of strictoidine was recorded 48 h and 72 h after leaves damage by _Manduca sexta_, while, after 72 h, in both local and distal leaves of _C. roseus_, a significant increase of ajmalicene, serpentine, and vindorosine was observed [25]. The accumulation of benzyl isoquinoline alkaloids (BIAs--nuciferine and N-nornuciferine), was significantly increased in the mechanically wounded lotus leaves, whereby fluctuating the accumulation of total alkaloids content in a temporal manner, which varied between wounded and non-wounded leaves, was observed [44]. Increased levels of certain BIAs were also recorded 3 h and 5 h after wounding in the leaves of _Papaver somiferum_ L. [50]. Previous results by [PERSON] et al. [51] showed that the highest induction of tropane alkaloid (TA-scopolamine) biosynthesis was recorded 24 h after artificial damage in _Brugmansia suaveoleens_, and subsequently decreased to the constitutive level.
# Beyond Spectrograms: Rethinking Audio Classification from EnCodec's Latent Space
## 3 Experiments
### Experimental Environment  
Several experiments have been conducted to evaluate the performance of the EnCodec-based audio classification model proposed in this work. These have been run on a hardware platform equipped with an AMD Ryzen 9 3900X 12-Core processor with 64 GB of DDR4 RAM, an NVidia RTX 3060 with 12 GB of GDDR6 VRAM, and CUDA version 11.8.  
The GTZAN music/speech [14], GTZAN musical genres [15], and ESC-50 environmental sound [16] datasets have been chosen for the experiments. The first one, used for two-class audio classification (music and speech), contains 64 samples per class of 30 s each. The second one, with an original frame rate of 22,050 Hz, contains 100 samples per class of 30 s each for ten different musical genres in the dataset, making it a total of 1000 items and 30,000 s of audio. The third is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. The dataset consists of 5-s recordings organized into 50 semantic classes.  
We have chosen 1-s item patches without overlapping and divided the dataset into training (60%), evaluation (20%), and test (20%). The input signal from these datasets is always monoaural. EnCodec at 48 kHz is stereo, while EnCodec at 24 kHz is monoaural. In order to transform the samples to stereo, the signal is duplicated in both channels. This conversion does not add any information to the signal. However, it is necessary for EnCodec to work with the audio information. Although EnCodec is used as-is, only the encoder part is needed, leaving out the quantizer and decoder parts. The framework's source code was implemented with the PyTorch 2.1.0 library on Python 3.11.4.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Effectiveness Evaluation and Analysis  
#### 3.4.1 Standard Deviation of Distance  
After determining the Bezier curve, the optimization effect is evaluated by calculating the standard deviation of the distances between the curve and the data points during the spanning motion phase, both before and after optimization, using the following formula:  
\[\sigma=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left({d_{i}}-\frac{1}{n}{\sum_{i=1}^{ n}}{d_{i}}\right)}^{2}}, \tag{18}\]  
where \(d_{i}\) represents the distance between the Bezier curve and the known data points, and \(\sigma\) denotes the standard deviation of the distances, with a smaller value of \(\sigma\) indicating a better fitting effect.  
The standard deviation of the distances for the Bezier curves, both before and after optimization, is calculated using Equation (18), as illustrated in Table 3. The results indicate that the optimized Bezier curves 1 and 2 are closer to the data points of Trajectory 1 and Trajectory 2 in Stage B, with improvements of 35.63% and 40.57%, respectively. This indicates that the optimized Bezier curves more accurately represent the actual trajectory data.
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 6 Results and Discussion
This is obviously because it is a general practice for rice growers to oversupply with N. Thus, the extra N nutrition (higher than 240 kg N per ha) usually does not provide any yield benefit. Therefore, the scope of the developed algorithm, which was presented in the current work, was to detect cases of field zones where the extra N nutrition will not provide any yield benefit. The advantage of this methodology is the reduction of inputs for the rice growers and obviously environmental benefits can be realized by the adoption of this algorithm. In Figure 8 though, there are observations that they are markedly different in terms of yield, including zones characterized by low yields such as 6 tn per ha. These outliers frequently occur in commercial practice and have to do with completely different aspects than the fertilization parameter and have more to do with failure of herbicide applications or disease outbreaks (such as _Py  
Figure 10: Indicative N curves created by feeding the model with various N levels in four different management zones (**a–d**). Apart from defining optimal N-doses, management zone delineation has given insight about the P and K deficiencies, and all the other physical and chemical properties explaining yield variance. P deficiency of rice in Greece is quite common, as Greek soils are highly calcite, while K have been supplied for several years in Thessaloniki Plain in low amounts, causing serious K deficiency in some of the zones. The growers tend to use low amounts of K fertilizer to reduce the fertilizer cost, and there is a general perception that K deficiency is uncommon for rice growing in Thessaloniki. Covering P and K demand allowed the model to better explain the effect of LNC (as an index correlated with N uptake), on yield variance. The rice growers in the Plain of Thessaloniki tend to use an NPK 32-5-5 fertilizer (containing mostly nitrogen and providing low concentrations of phosphorus and potassium), due to high potassium supplying capacity of many soils and the increased phosphorus availability under flooded conditions. However, phosphorus and potassium availability depend on soil texture [3] and the continuous use of the same fertilizer management approach has resulted in unbalanced fertility within the field parcels. If an efficient nitrogen topdressing management advice is provided to the growers timely, the spatial availability of P and K can also be addressed; P and K explain a large proportion of the yield variance [3]. Fertility issues of rice cultivation in the soils of the area have been discussed by [18].
# Key Issues in Using the Freezing Method in Power Tunnel Rehabilitation Projects
## 5 Analysis of Pathway Observation Points  
### Temperature Comparison of Upstream and Downstream Symmetry Points Under Seepage Conditions  
To accurately investigate the impact of groundwater flow velocity on the freezing process of permafrost curtains, it is evident from the previous subsection that the seepage conditions upstream and downstream of the curtain exhibit non-uniformity. Consequently, symmetry points were established at distances of 2 m from the outermost edge of the freezing pipes, both upstream and downstream. These points are denoted as A\({}_{1}\) and A\({}_{2}\), as illustrated in Figure 13(a).  
As illustrated in Figure 13(b), comparison of temperatures at the observed points relative to the symmetric point A under various seepage velocities indicated that the temperature variations upstream and downstream of point A were minimal and nearly symmetrical in the absence of seepage. As the seepage velocity increased, the inclination angle of the line connecting these points gradually increased. At a seepage velocity of 2 m, the temperature at symmetric point A\({}_{1}\) was approximately \(-\)4.96 \({}^{\circ}\)C, which is a 10 \({}^{\circ}\)C difference from the no-seepage condition due to the groundwater flow continuously transferring coldness from upstream to downstream. When the seepage rate reached 3 m, the temperature at the symmetry point A\({}_{1}\) remained above freezing, at about 2.96 \({}^{\circ}\)C. In summary, it can be concluded that the groundwater flow rate exerts a discernible impact on the temperatures of the upstream and downstream symmetry points, with the temperature difference between these points increasing as the seepage rate increases.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 6 Conclusions  
In this paper we proposed for the first time the combination of SDN-SDR via NFV virtual utility functions, as a cognitive and flexible framework to enable autonomic network management in CRN environments. We provided the design of a holistic framework for D2D type communications, which however, can be easily modified to address access point based communications as well. We detailed the architecture of the proposed framework into a tangible implementation, in a realization of a cross-layered resource allocation approach for SDR based cognitive radio networks. We tested our implementation in two actual testbeds (IRIS and ORBIT) of the ORCA federation. The implementation allowed us to successfully show the reconfiguration flexibility, the strict adaptation capability of the framework to the PU behavior, the fair allocation of resources (available channels) and spectrum reuse, and the collision avoidance capability among SUs, thus demonstrating the majority of the features of the proposed autonomic network management framework.  
Based on the obtained results from the actual evaluations, we plan to further extend the implementation and framework design. More research will be conducted in order to improve the cooperative spectrum sensing operation of the devices with higher accuracy and less inter-node signaling. Furthermore, extending the cross-layer implementation to include the network flow term  
Figure 8: Average number of assigned channels per secondary user.  
Figure 7: Channels utilized for transmissions in a 8-node full-mesh topology in ORBIT.  
and possibly transport layer parameters will enable better adaptation of the resource allocation to longer-term variations of the SDR environment. The latter will be investigated more extensively in the near future.  
**Author Contributions:** All authors contributed extensively to the work presented in this paper. [PERSON], [PERSON] and [PERSON] had the original idea on which we have based our current work and had the overall coordination in the writing of the article. [PERSON] and [PERSON] were responsible for the overall orchestration of the performance evaluation work. All authors contributed to the quantitative and qualitative evaluation of the proposed approach. [PERSON] and [PERSON] developed the code of the overall framework and executed the actual evaluation experiment. [PERSON], [PERSON] and [PERSON] contributed to the adaptation and implementation of the MRF framework and to the discussions and analysis of the evaluation results. All authors contributed to the writing of the article.  
**Funding:** The research leading to this work received partial funding from the European Horizon 2020 Program under the grant agreement No. 732174 (ORCA project).  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Conclusions  
In summary, to achieve the polarization-insensitive modulation and enhance the modulation capability of the graphene layer, a broadband polarization-insensitive graphene modulator based on plasmonic effect with DBOSW is proposed and comprehensively investigated. By introducing the two slot-like structure in the waveguide, the DBOSW can support two modes simultaneously, which enables the modulation of both the longitudinal and the transverse polarization electric fields in a broadband bandwidth. When the external bias voltage shifts from 0.608 to 5.442 V, the MD of 0.474 and 0.462 dB/\(\upmu\)m for the TM mode and complex mode can be obtained, respectively. A low effective index difference with an average of 0.001 is achieved within a wide optical band range from 1.1 to 1.9 \(\upmu\)m. The 3 dB-bandwidth of 101 GHz and a power consumption of 0.271 pJ/bit at a modulation length of 20 \(\upmu\)m can be obtained. The proposed graphene modulator has potential in microwave photonic systems.  
[PERSON] proposed the idea, carried out the simulations and wrote the manuscript. [PERSON] and [PERSON] plotted the figures and provided suggestions. [PERSON] supervised the simulations. [PERSON] and [PERSON] supervised the whole work, interpreted the results and improved the manuscript. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key Research and Development Program of China (2019 YFB2203001), National Natural Science Foundation of China (61675087, 61875069), and The Education Department of Jilin Province (JJKH20190118 KJ).  
Figure 14: The inequality of the top Ag strip \(\delta\) on (**a**) Re(\(\Delta N_{\text{eff}}\)); (**b**) \(\Delta\)MD; and (**c**) MD, PL of the TM mode and complex mode. The illustration in (**b**) shows the unequal condition of the DBOSW modulator, when \(\delta<\) 0 the width of the right Ag stripe is smaller than that of the left one.
## 4 Results
### Error Analysis  
To further evaluate the ET estimates, the frequency distribution of the percent errors was analyzed. According to numerical analysis and error analysis theory, the percent error is more scientifically rigorous than the absolute error when making assessments on the accuracy of estimates [38]. The percent error is the absolute error divided by the magnitude of the exact value, and then expressed in terms of per 100. The expression can be written as:  
\[\delta=\frac{\Delta}{\mathrm{L}}\times 100\% \tag{15}\]  
where \(\delta\) is the percent error, \(\Delta\) is the absolute error and \(\mathrm{L}\) is the exact value.  
Figure 5 displays the frequency distribution of the percent errors on ET estimates, which varies from 0% to 35%. An accuracy of the percent errors within 20% was achieved at more than 80% of study locations, and over 40% of the locations had percent errors lower than 5%.  
Figure 4: Comparison between ET observations from EC stations and ET estimates by the SEB-A method.