esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Coral Shape on Nadir View Reflectance
This shading factor, which in our data ranges from 0.50 to 0.84, could be of immediate use in approaches that use diffuse reflectance as a bottom boundary condition, for example, model inversion techniques for image processing [27]. If basic inputs are surface reflectances, a variable shading factor (effectively a shade endmember) could be included as an additional parameter to be estimated in the inversion, or fixed at a reasonable value, e.g., \(\sim\)0.7, or certainly less than one. A variable factor would not only accommodate the difference between the coral shapes shown here but would also accommodate the residual error in each coral,  
Figure 4: Reflectance over coral shape area only (no substrate) at the bottom of the water column with no interstitial water scattering or attenuation included. Plots show surface reflectance (red), reflectance over coral area under 32 treatments (grey), surface reflectance scaled by shading factor (black), and % difference of each treatment from scaled surface reflectance (light blue, right hand y-axis). due to variation under illumination conditions (Figure 4). However, this also points to the importance of the design of in-situ methodologies used for collection of spectral reflectance data [10; 11; 28; 29]. Protocols which include multiple sampling over benthos will likely include a certain amount of the shading factor already, but this is scale-dependent, i.e., for small branching corals it may be completely included, whereas for larger massive morphologies the measurements may be closer to surface reflectances. Moreover, protocols that use shading or artificial light sources may or may not include the relevant amount of shading for a remote sensing context. Careful consideration of the relative scale of measurements in the context of the application is required. From our results, the maximum error in the brightness of the endmember reflectance, when using a surface scale reflectance instead of macro-morphology scale, could be a factor of as much as 0.5 (or 2). It may be worthwhile to consider if spectral libraries should be standardized or normalized in this respect, to either include or not include the shading factor. With this information, there would be the possibility to apply a post-hoc factor, at least as a rough estimate (between 0.5 and 0.84 in our data, e.g., \(\sim\)0.67), or as a species-specific value (Table 3). Another consideration is that for very high spatial resolution imagery (pixels \(\leq\) 0.5 m), where benthic structure is at a similar scale to the pixels, shading effects may vary from pixel to pixel, over for example large massive corals, where the sides and top may be imaged in different pixels.
## 3 Results and Discussion
### 2D Periodic Hills Flow
The SST-SAS model also predicted reasonable results, but the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model drastically delayed the flow reattachment. The same characteristics could be observed from the skin friction coefficient (\(C_{f}=2\tau_{w}/(\rho U_{b}^{2})\)) along the bottom wall (Figure 8). The \(C_{f}\) predicted by the proposed SAS model was better than those from the other two models. Table 1 lists the separation and reattachment points, as well as the length of the main recirculation region predicted by the three models. It was demonstrated that, although the proposed SAS model predicted both the separation and reattachment points slightly early, the length of the recirculation zone was in good agreement with the prediction of LES. The SST-SAS model predicted the separation point accurately but predicted the reattachment point much later. The SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model overpredicted the recirculation length significantly, with the furthest reattachment point. Figure 6: Schematic of the geometry and boundary conditions of the 2D periodic hills flow. Figure 7: Comparison of the normalized mean stream-wise velocity profiles on different transverse sections. The performance difference between the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS and SST-SAS models can also be illustrated by the \(Q\)-criterion. The turbulent structures predicted by the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS and SST-SAS models using an iso-surface of \(Q=30~{}1/s^{2}\) were shown in Figure 9. There was a clear difference between the two images, indicating that the performances of these two models were different, although the computational conditions were the same. It was clear that the proposed SAS model produced a periodically resolved turbulent spectrum very well. In contrast, the SST-SAS model could not produce a periodically resolved turbulent spectrum. Specifically, in segment S3, the turbulent structures predicted by the SST-SAS model were very weak.
## 2 Experimental Section
### GlobAlbedo-Derived LAI and fAPAR
#### 2.1.4 Data Format  
GlobAlbedo-derived fAPAR and LAI products are provided in the NetCDF format [35] via the subsetting tool previously referred to. Data are stored using the MODIS SIN projection and tiling system [36]. The online data have 321 non-fill land tiles, each one of 1200 \(\times\) 1200 1 km pixels (see below), 240 \(\times\) 240 pixels at 5 km and 48 \(\times\) 48 at 25 km.
## 3 Results
### Experimental Comparison  
#### 3.3.1 The Effect of Crested Porcupine Optimizer (CPO)  
In this study, We utilized the Crested Porcupine Optimizer (CPO) algorithm on the dataset at the temperature measurement point 1 (because the data at point 1 are one of the few datasets that does not require preprocessing, as no dirty data were generated during sensor operation) to optimize the number of LSTM units, learning rate, and dropout rate. We set the number of iterations to 100 to more fully capture the optimization process, but the results showed that the model had already achieved good convergence around the 50 th generation. The optimization results are shown in Figures 7 and 8.  
Figure 8: Curve of optimization for number of learning rate.  
Figure 7: Curve of optimization for number of LSTM units.  
After optimization, the number of LSTM units was set to 27, the learning rate to 0.0091, and the dropout rate to 0.3. The commonly used number of LSTM units is 64, but the optimized value of 27 may be due to the periodicity of the weather data, the relatively low complexity of the data, and the modest data volume. Excessive LSTM units could lead to overfitting and increase the computational resource consumption.  
We conducted experiments on the models before and after optimization, selecting data from the remaining 30 temperature measurement points at the Xuefeng Lake Bridge for testing. For the ease of presentation, we chose to display the data from the first 15 temperature measurement points using the numbers 1-15 to represent different temperature measurement sites (The complete experimental results are in Table A1 of the Appendix A). The experiment results are shown in Table 1, and it can be observed that the three performance indicators of the optimized model all decreased.  
In summary, using optimization algorithms for parameters optimization in deep learning networks not only reduces the workload of manual parameters tuning but also enables more extensive testing of different parameters, resulting in more intuitive results.
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 5 Discussion
For example, when the Metop Advanced SCATerometer (ASCAT) data are used to measure the wind speed above 20 m/s, a reliable estimation of TC intensity cannot be provided [46]. However, these spaceborne microwave sensors can be employed to measure the TC intensity during its early stages. In addition, as pointed out by [PERSON] et al. [47], heavy rain can also affect the sounding performance of spaceborne microwave sensors except for these operating at a lower frequency.
# Icing Mitigation by MEMS-Fabricated Surface Dielectric Barrier Discharge
## 4 Icing Experiment
### Anti-Icing Results
On the area where the discharge is not formed due to the absence of the ground electrode (lighter area) the ice accumulates to the PCB electrodes, but in the case of MEMS samples the ice accretion is distributed homogeneously without specific anchor points. Thus, it can be concluded that MEMS electrodes exhibiting a thickness below 0.3 \(\upmu\)m do not provoke the ice attachment in contrast to the 35 \(\upmu\)m thick PCB electrodes. Therefore, the advantage of the MEMS fabricated SDBDs for anti-icing applications is well pronounced and can be attributed to the electrode thickness. The FR-4 SDBD with geometry G2 allows to draw conclusions about a potential influence of the ionic wind. The long electrodes intersect two separate areas of the sample. The upper area of the actuator design generates an ionic wind in downstream direction, while the lower area with symmetrical SDBD design suppresses the ion wind formation. Considering the expansion of the ice free area from the right electrode side one can see that it is independent of the geometry, see Figure 10. Our results improve the confidence of the suggestion made by [PERSON] et al. [23] that the ionic wind does not contribute to the anti-icing effect. Unlike their 70 \(\upmu\)m thick electrodes, our MEMS-electrodes provide a direct evidence, as they do not interfere with the flow. Figure 10 also exhibits that the icing is stronger near the leading edge where the ice accretion is more severe. Whereas on all electrodes of the FR-4 SDBD an ice accretion is visible, only a minor ice accretion is found in the first segment on the glass SDBD, see the slight gray haze in the center excerpt on the bottom of Figure 10. The SDBD area on the zirconia sample is completely free of ice. In aft of the electrodes an ice-free area is formed. We hypothesize that that the heat generated by the discharge is transported downstream and this vortex-induced heat exchange [52] causes the anti-icing effect in the inactive SDBD aft area. Additionally, pre-heated runback water might contribute to the observed anti-icing effect like it is known from conventional heating devices [53].
## 3 Proposed Framework
### mBERT  
mBERT, a multilingual version of BERT, is trained on Wikipedia texts from 104 languages, allowing it to handle multilingual text within the same representation space, and a lot of studies have highlighted mBERT's excellent CLT performance [51]. Hence, this paper adopts mBERT as the base model.  
A linear classification layer <\(W\), \(b\)> can be directly added to the mBERT model \(f_{\theta}\) to build the model \(\psi\), \(\psi=\{f_{\theta},W,b\}\). Given a labeled dataset \(X_{s}^{l}=\left\{x_{s}^{i},y_{s}^{i}\right\}_{i=1}^{N_{s}^{l}}\) in the source language, the model is trained using the dataset \(X_{s}^{l}\), resulting in the base model.  
\[\psi_{0}=\operatorname*{argmin}_{\psi}\sum_{x_{s}^{i}\in X_{s}^{l}}CE(p(x_{s} ^{i},\psi),y_{s}^{i}) \tag{3}\]  
where \(CE\) is the cross-entropy function. \(p(x_{s}^{i},\psi)\) represents the label distribution of model output samples. With mBERT, being a multilingual pre-trained model, \(\psi_{0}\) can be directly applied to OLD task in the target language, and it generally offers some cross-language generalization. However, the direct transfer faces challenges and drawbacks:  
1. Language differences: Significant variations in syntax, vocabulary, and expressions exist across languages. Without labeled data in the target language, the model may struggle to adapt to specific language structures and cultural nuances, leading to performance degradation.
2. Domain disparities: Even within the same language family, domain differences can persist. Applying a model trained in one domain directly to another may fail to capture specific features of the target domain, impacting performance.
3. Task specifics: OLD is a task with unique challenges. Expression of offensive language can vary widely across languages and cultures. Models trained directly in the source language might not effectively capture offensive language features in the target language.
4. Label bias: Offensive language in the source and target languages may have different label distributions. Without target language labels, the model may encounter label bias issues, resulting in unstable performance in the target language.
5. Limitations of transfer learning: Despite mBERT's inherent transfer learning capabilities, without labeled data in the target language, the model's transfer ability may be restricted, making it challenging to fully adapt to the specific requirements of the target language.
## 2 Materials and Methods
### Muscle Activation Calculation  
The muscle activation level (_a_) is obtained from raw sEMG signals and can be calculated by the method recommended by [PERSON] et al. [39]. The filters used in this paper are all 4 th-order zero-lag shift Butterworth filters having different types and cutoff frequencies. The raw sEMG signals are first passed through a high-pass filter with a cutoff frequency of 5 Hz to remove the DC offsets. Then, they are rectified and passed through a low-pass filter with a cutoff frequency of 3 Hz. This process is implemented to mimic the low-pass effect of muscle force generation characteristic and obtain a smooth trend. For a further analysis of the low-pass cutoff frequency, the reader is referred to the discussion section of this paper. The filtered signal denoted by _e_(t) is normalized using the maximum voluntary contraction (MVC) test activation value. _a_(t) is then calculated as follows [39]:  
\[u(t)=\alpha e(t-d)-\beta_{1}u(t-1)-\beta_{2}u(t-2) \tag{5}\]  
\[a(t)=\left(e^{Au(t)}-1\right)/\left(e^{A}-1\right) \tag{6}\]  
Equation (5) represents the muscle activation dynamics. Equation (6) is used to de-linearize the results. In this paper, parameters \(\alpha\), \(\beta_{1}\), \(\beta_{2}\) and \(A\) are set as 2.25, 1, 0.25 and \(-0.5\), respectively. The effects of different low cutoff frequencies are also tested in discussion section. The lower the low-pass cutoff frequency is, the smoother the activation level will be. A smooth result can be more suitable for engineering applications, although it may suffer from the deficiency that some valid signal may be eliminated in clinic analysis.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 3 Derivation of the BECPB for Rayleigh Channel
### Battery Discharge Power for Prescribed SER  
Assume the channel gain is given by \(\frac{1}{L(d)}\), where \(L(d)\) represents the path loss expressed as  
\[L(d)=M_{\rm I}G_{0}d^{k}, \tag{12}\]  
where \(d\) is the transmitter-receiver distance, \(k\) is the power-decay exponent, \(G_{0}\) and \(M_{\rm I}\) denote the path loss at 1 m and the link margin, respectively.  
For Rayleigh fading channel, it can be shown that the average SER is expressed as  
\[SER=\frac{1}{\rho+2} \tag{13}\]  
where \(\rho\) represents the average received SNR written as (refer to Equation (30) in [7]).  
\[\rho=\frac{P_{1}/L(d)}{2N_{0}B}. \tag{14}\]  
Assume the SER must be smaller than a prescribed value \(\Gamma\). It can be shown that  
\[P_{1}=2N_{0}M_{\rm I}G_{0}(\frac{1}{\Gamma}-2)Bd^{k}, \tag{15}\]  
and after plugging it into Equation (11),the discharge power from the battery is  
\[P_{\rm out}(t)=\left\{\begin{array}{ll}\frac{f_{\rm ray}(B,d)+P_{\rm a}+P_{ \rm d}}{\eta}&\mbox{when emitting symbol `1'}\\ \frac{P_{\rm d}}{\eta}&\mbox{when emitting symbol `0',}\end{array}\right. \tag{16}\]where  
\[f_{\text{ray}}(B,d)=\zeta Bd^{k} \tag{17}\]  
with \(\zeta=2(1+\alpha)N_{0}M_{l}G_{0}(\frac{1}{\Gamma}-2)\).
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 8 Prototype Implementation and Results
### Disturbance Disambiguation  
In addition to phase disambiguation and reconciliation of meter location (geospatial, electrical), the approach proposed here provides a mechanism for pinpointing objectionable disturbances which may produce IEEE-519 violations [51]. An example of this process is described graphically by the spectrogram shown in Figure 10.  
The current disturbance (DCP) presented in Figure 10 is a \"sweep\" or \"chirp\" of current which repeatedly transitions from around 900 Hz to around 1500 Hz in 1-second intervals. The \"sweep\" DCP is clearly evident in the UCP of Figure 10, but the cross-talk between phases is very intense.  
Figure 10: Spectrogram of downstream current disturbances (DCP) captured at the substation (UCP). The “cross-talk” between phases is clearly indicated.  
Figure 9: Spectrogram (filtered) of downstream current disturbance (DCP) captured at the substation (UCP). The DCP originated on a lateral serviced by Phase A, but the figure clearly shows “cross-talk” from Phase A to Phases B and C which occurred as the DCP moved through the distribution grid.  
As a result, the original or \"primary sweep\" is almost undetectable, and the phase which produced the \"primary sweep\" is difficult to determine. As in the discussion of the issues associated with phase disambiguation in Section 8.1, Figure 11 presents a bandpass-filtered perspective of the UCP, which may assist in a manual determination of the \"primary sweep\" which has a relatively higher magnitude than the synchronous harmonic distortions or \"sidelobes\" described in Section 5.2. However, automated mechanisms which use ML techniques to assist in phase disambiguation as well as determination of original artifacts could be extremely valuable. For example, the presence of the \"primary sweep\" can be easily detected by the smart meter which services the originator of the artifact. Such artifacts can be decomposed in a variety of ways to create the DCP description which assists in localizing and identifying the presence of the DCP in the UCP sequences captured at the substation.
## 2 Edge Computing
### PUF-Based Authentication Mechanism  
IoT systems utilize diverse sensing devices and physical nodes, making them vulnerable to counterfeiting and tampering. Therefore, robust security measures, like assigning unique identifiers to devices, implementing reliable mutual authentication, and ensuring encrypted data transmission between devices and the cloud, are essential [70]. Authentication is crucial for securing IoT networks by preventing unauthorized access to nodes and data. However, due to the limited storage, computational resources, and bandwidth of IoT nodes, it is challenging for the network to support extensive encryption technologies or implement sophisticated security mechanisms [71]. PUFs exploit inherent physical differences in devices to generate unique cryptographic keys for applications such as identification and authentication [72; 73]. When an input called a \"challenge\" is applied to a PUF, it produces a corresponding output, or \"response,\" determined by each device's unique and complex physical properties. This response cannot be reproduced, as it relies on uncontrollable physical variations introduced during the manufacturing process of edge devices [74].  
As illustrated in Figure 2, the same challenge input presented to different PUFs produces unique responses. A collection of challenge inputs and their corresponding response outputs extracted from a PUF is called a challenge-response pair (CRP) set. The size of the CRP set categorizes PUFs as either strong or weak. A PUF with an exponentially large number of CRPs relative to the number of challenge bits is classified as a strong PUF, whereas weak PUFs have a more limited CRP set. Consequently, strong PUFs are suitable for complex cryptographic protocols, whereas weak PUFs are primarily used for key storage [75]. Weak PUFs, such as Ring Oscillators (ROs) and SRAM PUFs, are commonly utilized for key generation purposes. In contrast, strong PUFs include the Arbiter PUF (APUF) and its various enhancements, such as the XOR APUF, Feedback-Forward PUF (FFPUF), and Lightweight PUF (LPUF), which are used in device authentication [76].
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Semi-Supervised Strategies  
Generally, semi-supervised methods for anomaly detection in the literature fall in the category of One-Class Classification (OCC). In practice, it is quite frequent that normal events have a good representation, as they represent the majority of the captured sequences, whilst abnormal cases are rare, and the abnormal class is ill-defined. In these cases, the abnormality is detected based on the information learnt from the normal class only. One-Class Classification is present in reconstruction-based and prediction-based semi-supervised methods that employ the same basic principle; both try to generate the entirety or patches of a frame, evaluating their similarity to the ground truth. These models are trained on normality and assume that it is not possible to properly reconstruct an abnormal event that has never been learnt. Hence, a frame that greatly differs from the captured one is likely to represent abnormal or unexpected events. The main difference between both types of semi-supervised methods regards temporal information, as illustrated in Figure 1. On the one hand, reconstruction-based methods try to reconstruct the current frame, using previous and present information. On the other hand, prediction-based methods use the previous frames to compute a prediction of the following one. Recently, semi-supervised strategies have been increasingly focusing on video frame prediction due to its potential applications in unsupervised video representation learning. However, existing methods of this type deliver suboptimal results, especially when compared to newer methods that use weakly supervised techniques, due to their insufficient modelling of temporal information. Moreover, they suffer from inefficient training for implementing adversarial techniques or additional losses [13]. The lack of prior knowledge of abnormality is usually a cause of overfitting of the training data, not enabling a proper way to distinguish abnormal from normal events [6].
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results  
The accuracy of the reference cloud registration was characterized by a mean absolute error of 1.3 mm (based on internal scanner validation). Subsequently, the accuracy of the reference cloud was independently verified using 58 control points that were placed on the walls and floor and measured with a total station, yielding a RMSE of 1.4 mm. Given the expected accuracy of the SLAM scanners of approximately 1 cm, the reference point cloud is an order of magnitude more accurate and, therefore, suitable for use as a reference.