esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Three steps of a switch opening and exchange method have been also implemented in [4] to cope with distributions system reconfiguration; this method is based on sequentially opening the switches and exchange from a looped system to obtain the optimal radial distribution system. Besides, distributed generation (DG) is one factor that overcomes the issue of under-voltage and power loss in the power distribution system. The sitting and sizing of DGs were described in detail in [25; 26; 27; 28] and presented a particular algorithm that results to get the best sitting and sizing of each studied system. The authors in [29] have proposed a method of DG placement and reconfiguration simultaneously to address the issue of not only power loss and voltage profile but also system in critical condition like tripping distributed transformer and control increasing short circuit level; they have also studied on real topology 33 bus of the city in Iran. In [30; 31], the authors have presented separately case studies focusing on DG injection into the grid system before, during, and after reconfiguration with the objective of loss reduction and voltage profile improvement. The planning of a distribution system with integration of renewable energy source (RES) and energy storage (ES) has been studied in [32; 33]; the optimal capital and operation costs of components (i.e., line, RES, and ES) as the development cost over a 1-year time horizon based on its sizing, allocation, and curtailment is provided using mixed-integer linear programming (MILP) model. However, a huge search space, time consumption, and obtaining optimal solution are still a big challenge to solve in large systems with these existing methods. In addition, this existing algorithm development has been attracted the current authors to propose a modified method based on search space reduction for each iteration process. This research paper proposes to modify the existing sequential opening switch and exchange in [4] for optimal reconfiguration challenges in comparison to an optimal solution and computation time. The rest section of the paper is organized as follows. Section 2 provides the proposed method and problem formulation to achieve the research objectives in reconfiguration. Section 3 describes the simulation results and discussion. Finally, the conclusions are addressed in Section 4.
## 4 Experiments
### Synthetic Sample Identification Performance Analysis
For synthetic samples above baseline, the validation set recognition accuracy of the Up-CNN model was 100%, and the recognition rate of the test set was 98.43%. The overall recognition rate of synthetic datasets above baseline was 99.69%. (3) There are 273 classes of synthetic samples below baseline, 300 of each class, a total of 81,900, and the recognition model of characters below baseline (Down-CNN model) is trained by the synthesized below-baseline samples. The samples were randomly divided into a training, validation and test sets with proportions of 70%, 10% and 20%, respectively. The experimental parameters are set to: stochastic gradient descent, the number of samples per batch is 800, the initial learning rate is 0.1, the maximum number of rounds is 10 rounds, and after each two rounds of training, the learning rate = the previous round of learning rate * 0.1. The training took 5 min and 31 s, and the experimental results are shown in Figure 11.  
It can be seen in Figure 1 that the stability is reached at about 300 iterations, the recognition accuracy of the training set is 99.875%, and the recognition accuracy of the validation set is 99.3773% at the 300 th iteration. The loss in the training set is 0.0073, and the loss in the validation set is 0.0217. The recognition accuracy of the Down-CNN model is 99.48% in the validation set and 99.45% in the test set. The overall recognition accuracy of the synthetic dataset below the baseline is 99.77%. Figure 10: Up-CNN model training process.
## 2 Islanding Detection Methods
### Hybrid Methods
#### 2.3.2 Voltage and Real Power Shift Method  
This method's passive component is the average rate of voltage change, and its active component is the real power shift. At the DGs side, the voltage signal is routinely observed, and islanding is recognized if the average rate of voltage change is larger than zero for more than five cycles. However, if the disturbance lasts for fewer than five cycles, it is unable to detect islanding. In this instance, the actual power shift approach can do so [30].
## 2 Electro-Optical System  
The system that was analysed and simulated is illustrated in Figure 1a. Data was generated in the form of delta trains that were shaped with low pass square root raised cosine (SRRC) filters. The roll-off factor \(\beta\) was equal to 1 as a full-bandwidth is more realistic to emulate an ASP-based system. The shaped digital streams were then used to modulate carriers from microwave local oscillators (LO) with ideal IQ mixers, performing a quadrature phase shift keying (QPSK) modulation on each subchannel. Once the subchannels were combined, the resultant signal and its Hilbert transform, required for the optical SSB generation [10], were fed to an optical IQ modulator. The laser at the input of the optical modulator was ideal (a noise free optical carrier). The parallel MZMs inside the optical modulator were biased at quadrature. The amplitude of the obtained optical SSB signal was modified with a variable optical attenuator (VOA) that simulated fibre losses. The Erbium doped fibre amplifier (EDFA) in the pre-amplified receiver presented a noise figure of 5 dB and produced a constant output average optical power that was fed to an ideal noise-free photo-receiver that performed the direct detection (DD) of the received signal. The resultant electrical signal was demodulated with ideal IQ mixers and the matched low pass SRRC filters. The obtained digital binary streams were analysed by measuring bit error rate (BER). Note that the use of SRRC filters and the particular phase alignment imposed in the local oscillators and the baseband signals are required to make the scheme compatible with orthogonal transmission in accordance with FBMC theory [5; 13; 14]. From the previous description of the simulated electro-optical scheme, it can be derived that the only sources of impairments were the IMD coming from the optical modulator and the amplified spontaneous emission (ASE) generated in the EDFA. These conditions were considered because the signal-ASE beat noise is the main source of optical noise in practical SCM systems implementing pre-amplified receivers [2], especially when working close to the sensitivity level.  
Two different microwave frequency plans were analysed. The first case corresponds to a SCM/SSB signal consisting of three 2.7 Gbaud QPSK subchannels with LOs located at 5.4, 10.8, and 16.2 GHz, as depicted in Figure 1b. The electrical signal presents a data rate of 16.2 Gbit/s with a spectral efficiency of 1 bit/s/Hz. The second case halves the LO separation and achieves the OSCM/SSB spectrum shown in Figure 1c, increasing the spectral efficiency to 1.5 bit/s/Hz. Note that both spectra are only illustrative and the real optical carrier to signal power ratio (CSPR) depends on OMI.
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 4 Methodology
### Byzantine Tolerance  
In ZenoPS, we use the following score-based approach for Byzantine tolerance.  
**Definition 6** (ZenoPS validation).: _Assume that, in the \(t\)th iteration, based on the model parameters \(x_{t^{\prime}}\) (with latency) on the server, where \(t^{\prime}\leq t-1\) (\(t^{\prime}=t-1\) in the synchronous mode), the validator locally updates the model parameters for \(H\) steps using the validation data and obtains the accumulated update for validation: \(v=-\eta\sum_{h\in H}\
abla f(x_{t^{\prime},h-1};z_{t^{\prime}h}{\sim}\mathcal{ D}_{r})\), where \(x_{t^{\prime},h}=x_{t^{\prime},h-1}-\eta\
abla f(x_{t^{\prime},h-1};z_{t^{ \prime},h}{\sim}\mathcal{D}_{r})\), and \(x_{t^{\prime},0}=x_{t^{\prime}}\). An update \(u\) from any worker will be approved and sent back to the server for updating the global model parameters if the following two conditions are satisfied:_  
\[\langle u,v\rangle\geq\rho\|v\|^{2}+\epsilon,\] \[\|u\|^{2}\leq(1+\gamma)\|v\|^{2}\quad\text{alternative: clip $u$ to the maximum norm $(1+\gamma)\|v\|^{2}$}\,,\]  
_where \(\rho,\gamma>0\) are some hyperparameters for the thresholds._  
The above validation mechanism allows both the candidate update and the validation vector to be the accumulation of multiple steps of SGD updates. Thus, such a validation mechanism can be used for distributed SGD with infrequent synchronization, such as federated optimization or local SGD.
## 5 Comparative Analysis Results and Discussion
### Comparison of ClaimsBERT against Other Network-Based Classifier Models
#### 5.2.2 BiLSTM  
Bidirectional LSTM (BiLSTM) models are especially convenient for sequential modeling [60]. BiLSTM models have therefore found widespread application in retrieving contextual knowledge. This knowledge is the results of convolution layer feature maps [60]. In bidirectional LSTMs, the embeddings are obtained by concatenating two one-way actions [61].  
For this research we also explored the relative impact provided by varying the number of BiLSTM layers and their hidden units, the dense layers and their neurons, and the dropout rates for each layer stacked on top of BERT. The LrFinder function [53] was used to determine the optimum LR for each architecture. Table 4 reports the highest accuracy and F1 for each architecture. The highest accuracy for the combined BERT+BiLSTM-based model was achieved by stacking a BiLSTM with 150 hidden units and a 0.3 dropout rate on top of BERT, linked to one dense layer with 64 neurons and a 0.5 dropout rate.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Ortho-Rectification and Mosaicing  
The process of ortho-rectification is well described in literature. The main two approaches are direct and indirect rectification. The first projects every input pixel into the resulting map product by an image-to-map transformation. The second starts from the map and projects every point back into the input image by a map-to-image transformation. The process of mosaicing, that is, stitching multiple individual ortho images to one product is understood as well. Design issues are the selection of pixels for one map cell and the feathering option. For both processes we refer to the standard literature [105; 106; 107; 108; 109]. A remaining challenge can be seen in an efficient integration into the complete workflow, which will be discussed in his work.
## 2 Theory and Principle  
Let us now focus on the transmission characteristics of resonator loaded lines and the role of symmetry. The two types of structures considered are: (i) lines loaded with a single resonator, where a single notch (resonance) may appear or not depending on particle orientation and position; and (ii) lines loaded with a pair of resonant elements, where disruption of symmetry leads to frequency splitting.
## 3 Results and Discussion
### Surface Hydrophobicity of Actomyosin  
Surface hydrophobicity can also be an indicator of fish protein denaturation and aggregation in extracted actomyosin samples during freeze-thaw treatment. As shown in Figure 5, increased surface hydrophobicity of actomyosin was observed in all samples. However, in the PH, AH and SuSo groups, increase in surface hydrophobicity was retarded after six freeze-thaw cycles in comparison with a sharp increase in the control group (\(P<0.05\)). Overall, the control group showed 245.7% increase in surface hydrophobicity of actomyosin by the second freeze-thaw cycle, and 320.2% increase after six freeze-thaw cycles. While the total increases in surface hydrophobicity of SuSo, PH-2, PH-4, PH-6, AH-2, AH-4, and AH-6 groups were 124.8, 110.4, 120.8, 284.5, 191.0, 274.8 and 323.5%, respectively. Low surface hydrophobicity referred to less hydrophobic binding of protein to fluorescent probes and possibly less protein denaturation [18]. Surface hydrophobicity changes in samples with hydrolyses were generally in agreement with the decreases in Ca\({}^{2+}\)-ATPase activity (Figure 3), with the PH-2 group displayed the lowest surface hydrophobicity (\(P<0.05\)). In addition, the number of hydrolyses added to surimi also influenced the increases in surface hydrophobicity.  
SuSo group exhibited continual increase in surface hydrophobicity throughout six freeze-thaw treatments. In the hydrolysate groups, after the initial increase, surface hydrophobicity sharply decreased after four freeze-thaw cycles and then increased further. [PERSON] and [PERSON] had revealed that exposure of aliphatic and aromatic amino acid residues to protein molecular surface could lead to increases in surface hydrophobicity [31]. It is possible that during the freeze-thaw treatments, peptides and/or FAAs in the hydrolyses interact with actomyosin via hydrophobic interactions, inducing a decrease in surface hydrophobicity. Our findings also supported that the hydrolyses and SuSo might adopt different mechanisms of cryoprotection.  
Figure 4: Total sulfhydryl content of actomyosin from different surimi samples during freeze-thaw cycles. SuSo (surimi added with sucrose-sorbitol blend); PH-2, PH-4 and PH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Protamex hydrolysis, respectively); AH-2, AH-4 and AH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Alcalase hydrolysis, respectively); and control (surimi without cryoprotectant). The reported data represent mean values from three replications. Bars represent standard deviations.
## 4 Numerical Verification
### Damage Feature Extraction  
For preprocessing, the WPT is employed to process the acceleration responses and generate WPNEs. Let \(f_{p}(t)\) be the acceleration response generated from the \(p\)th damaged case. The db4 wavelet optimally selected is used to decompose \(f_{p}(t)\) up to the sixth layer, resulting in 64 WPNEs. Figure 6a,b show the WPNEs and their normalized forms of the intact case, where the normalization is conducted as:  
\[e_{i,p}=\frac{\left(e_{i,p}-\overline{e}\right)}{\sigma} \tag{15}\]  
where \(\overline{e}\) and \(\sigma\) are the mean value and the standard deviation of the WPNE vector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Number** & **Damaged Element** & _SRR (\%)_ \\ \hline
1–30 & 1 & 1, 2,..., 30 \\
31–60 & 2 & 1, 2,..., 30 \\
61–90 & 3 & 1, 2,..., 30 \\
91–120 & 4 & 1, 2,..., 30 \\
121–150 & 5 & 1, 2,..., 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layout of damage cases.  
Figure 5: Three-span continuous bridge.  
of neurons in the mapping layer as well as the remapping layer is given as 20. When the WPNEs are individually placed in the input layer and the output layer, the AANNs can be driven to train under the control of the cost function, i.e., _MSE_. Once the AANNs are well trained, as indicated by the _MSE_ arriving at the stop criterion, the outputs of the bottleneck layer generate the NPCs, leading to five NPCs. These NPCs are the damage features extracted from the WPNEs, as illustrated in Figure 6c for the intact case.  
The damaged cases listed in Table 2 can be divided into five groups in terms of the damaged elements (locations), from Ele 1 to Ele 5, with Ele being an abbreviation of _element_. Damaged cases in same group differ from each other according to their different damage severities. Visualization of the NPCs is utilized to indicate their feasibility to characterize damage. The first three NPCs of all damage cases are plotted in Figure 7a, with Figure 7b-d providing progressively zoomed-in displays of the portion covered by the zoomed-in window. In these figures, there are five dotted curves extending to different directions, each comprising a sequence of separated dots. Each curve denotes a group of damage cases with the damage located at the same element; each dot of a curve labels a damage case of specific damage severity. The dispersion of the dotted curves indicates the effectiveness of the NPCs to reflect different damage locations; the separation of the dots in one curve designates the efficiency of the NPCs to characterize different damage severities. Clearly, the NPCs can be used to characterize not only the damage location but also the damage severity, even in damaged cases with the _SRR_ less than 5%.  
Figure 7: Visualization of the capability of NPCs to characterize damage by a progressively zoomed-in cube.
## 4 Development Phase According to Design Science Research
### BIM-Based Process Definitions
#### 4.1.2 Container Allocation  
The container allocation in the new BIM-based process differs fundamentally from the current process in that no physical inspection of the warehouse will be necessary. The required container typologies and panel configurations according to customer requirements are exported from the BIM modeling software to the _Container Manager_. The _Container Manager_ provides direct access to the _Virtual Container Warehouse_ in the form of an MS Access database. This merging of the two information sources and data streams in the _Container Manager_ enables a target-oriented allocation of the most suitable containers according to the drawing requirements. For this purpose, a selection support has been developed in the _Container Manager_, which allows for filtering according to the currently available inventory containers with the lowest effort in terms of panel modification.  
If the physical containers are assigned from the warehouse, their status in the virtual container warehouse automatically changes to _\"lent\"_. In addition, the current panel configuration (as metadata) is updated to that of the bid drawing, so that the correct status is already stored in the virtual container warehouse on return.  
As a further functionality within the new BIM-based process, it is also possible to import the assigned rental IDs back into the BIM model, so that manual input is no longer necessary. In addition, at the push of a button, all 2D drawings of individual containers comprising the entire facility are updated according to the bid drawing, and plan files are generated. These are then to be saved as PDF files so that the 2D drawing repository can be updated within a very short time without any additional effort.
# Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
## 2 Background
### Attack on Speaker Recognition System
In addition to the methods mentioned above, recently, the authors of [50] have borrowed from the technical methods of speech steganography, using a gated convolutional autoencoder [51] to generate imperceptible audio adversarial examples, and using a multi-objective loss function for training. This method bypasses the gradient requirements as the general generation of adversarial examples. This network is used to generate adversarial examples. From the results, the PESQ score of 4.30 for targeted and non-targeted adversarial examples can achieve a high success rate. In a real physical environment, and for the success of the attack, it is necessary to consider factors such as reverberation, noise, etc. Therefore, the authors of [52; 53] base their work on the white box, which solves the impact of the room impulse response on the adversarial example in the actual attack. However, in comparison, the study [52] also considers that non-universal adversarial examples require a lot of time to train and the real-time nature of the attack--they both utilized a gradient optimization method to generate robust universal adversarial examples. However, both were still in the experimental stage and did not attack in the real world. In the black-box setting, to take advantage of practical gradient information for gradient descent, to solve the optimization problem of adversarial example generation, [PERSON] et al. [54] used the gradient estimation algorithm (NES) based on the evolution strategy. They found an impressive attack success rate on [PERSON] with their proposed method, but the problem of this article is that all the test data sets are small. As their study only included five speakers, an extensive study with a much higher number of test speakers is still needed. The above content briefly describes the existing adversarial attack methods that some authors believe mainly occur speech recognition and speaker recognition. Researchers firstly start from the generation of audio adversarial examples to the generation of aggressive adversarial examples which to the human auditory system are imperceptible, and consequently many adversarial attack methods have been trialled successfully. Although many methods have achieved high attack success rates, each method still has comparatively more or less shortcomings.
# Evaluating the Quality of Light Emitted by Smartphone Displays
## 3 Results
### Spectral Power Distribution and Blue Light Reduction
After applying the built-in blue light filter, the CCT significantly decreased, which occurred differently for each phone. The correlated color temperatures vary more among the devices, ranging from 4000 K to 6200 K. The largest difference between the variant with and without the filter, almost 3000 K, can be observed for sample 6. A vast difference (around 2600 K) is also noted for sample 7, where the manufacturer applied a filter to reduce blue light and increased radiation emission in the wavelength range responsible for the red color. More minor differences (around 1000 K) occurred for samples 2 and 5. Sample 5 is a smartphone where the manufacturer's filter performed the worst in reducing blue light. Nearly all smartphones achieved the lowest CCT for measurements taken with the blue light filter app. The exception is sample 6, which had a lower CCT for the variant with the manufacturer's filter. After using this app, the phones' screens exhibited CCTs in the 3300 K to 5200 K range. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**CCT (K)**} \\ \cline{2-4}  & **Without Internal Filter** & **With Internal Filter** & **With Dedicated App** \\ \hline
1 & 7108 & 5616 & 4141 \\ \hline
2 & 7203 & 6194 & 4493 \\ \hline
3 & 6846 & 4604 & 4084 \\ \hline
4 & 6762 & 5390 & 3937 \\ \hline
5 & 6802 & 5673 & 3808 \\ \hline
6 & 7762 & 4953 & 5151 \\ \hline
7 & 6811 & 4202 & 3381 \\ \hline
8 & 6850 & - & 3811 \\
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 2 Explicit Formulas to Describe Nonlinear-Hysteretic Response
### Modulus Degradation  
The instantaneous shear modulus (Gtan = d\(\tau_{\rm H}\)/d\(\gamma\)), defined as the slope on the stress-strain hysteresis loop along the loading path, can be obtained by taking the derivative with respect to shear strain in Equation (1):  
\[G_{\rm tan}^{H}=\mathrm{d}\tau_{H}/\mathrm{d}\gamma=G_{\rm max}\Big{[}1- \alpha\Big{(}\mathrm{sgn}(\dot{\gamma})\cdot\gamma+\Delta\gamma\Big{)}\Big{]} \tag{3}\]  
Given the \(\dot{\gamma}>0\) and \(\Delta\gamma=\gamma\), the tangent shear modulus can be expressed:  
\[\frac{G_{\rm tan}}{G_{\rm max}}=1-2\alpha\gamma \tag{4}\]  
This explicit form of the tangent modulus can be useful in the numerical simulations to reflect strain-softening response of granular materials under cyclic loading. The tangent stiffness \(G_{\rm tan}\) instantaneously captures the soil fabric change during a large strain test. It is not related with small strain stiffness \(G_{\rm max}\) measured under constant fabric [27; 28].  
The secant shear modulus (\(G_{\rm sec}=\tau_{\rm b}/\gamma\)), where the secant slope drawn from the origin to any specified point on the stress-strain curve can be derived using Equation (2):  
\[\frac{G_{\rm sec}}{G_{\rm max}}=1-\alpha\gamma \tag{5}\]  
The ratio between the secant and backbone tangent moduli is given:  
\[\frac{G_{\rm tan}}{G_{\rm sec}}=\frac{1-2\alpha\gamma}{1-\alpha\gamma}<1 \tag{6}\]  
The small positive quantity in \(\alpha\gamma\) indicates that the secant modulus is always larger than the backbone tangential modulus.