esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Adversarial Attack for Different Application Scenarios +
## 2 Related Work
### White-Box Attack on Text  
In terms of white-box attacks, the related works are not as much as black-box attacks, which are mainly based on gradient methods. [PERSON] et al. [16] propose a gradient-based white-box attack method to generate adversarial examples. This attack aims at a character level RNN classifier and greatly increases the error rates on text classification and machine translation.Cheng et al. [17] propose a white-box method to generate adversarial examples against NMT and improve the robustness of NMT. Greedy attack proves to be a very effective method of attack [8; 9; 18], and [PERSON] et al. [8] even show that greedy attack achieves state-of-the-art attack rates across various kinds of models. However, it also has some problems. [PERSON] et al. [9] propose that it may not guarantee producing the optimal results and sometimes spends too much time because the algorithm needs to search the candidate words for every iteration, at the same time, due to the nature of being greedy, the algorithm can replace sub-optimal words that do not contribute much to the final goal in an earlier position. Another limitation is that replaced words tend to be in a close area of the sentence, especially in the front. This greatly reduces the readability of the sentence and even destroys the semantic meaning of the original text. They propose a \"Global Search\" algorithm, which obtains candidate words by calculating a perturbation, and then replace the words in the position where the perturbation is larger. The larger the perturbation, the more sensitive the classifier is to the change of the word. Results of global search prove to generate better examples than the greedy attack and higher attack success rate, which is the baseline used in our experiments. In addition, [PERSON] et al. [19] propose a greedy method can be very time consuming when the space of attacks is large and give the optimization scheme.  
The related work of white-box attacks have proved to be very effective in text-based attacks, but there are still some problems to be solved, such as a good algorithm in the replacement percentage, attack success rate, word similarity, grammatical correctness, and semantic similarity can still have better results. Based on the strategy of gradient generation perturbation, we propose a new white-box attack method for sentiment classification task and make some progress in the above aspects.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)  
### A Nutshell on Data, Information, and Knowledge  
A piece of data is defined as an atomic value with a unit, which is the most fundamental element that is unprocessed and raw and contributes to knowledge generation. Let us understand these concepts with reference to an example from a vehicular network. An example of data can be the Global Position System (GPS) position \((x1,y1,z1)\).  
Information is a processed and organized collection of data that is more meaningful than raw data and can be used to make a decision. An example of information can be the distance from the controller to a vehicle, which is calculated by processing data containing the status of the vehicle, such as the GPS position of a given vehicle at a given timestamp (_vehicle\({}_{1}\)_, 15:05, \((x1,y1,z1)\)). In conventional networks such as Vehicular Ad Hoc Networks (VANETs), information such as safety notifications, vehicle state information, sensor measurement information, navigation information, etc. is exchanged between the vehicles. Conventional vehicular networks use this exchanged information to make decisions on network functions such as routing without generating knowledge [55].  
On the other hand, in KDNs, the approach is shifted from the information-centric approach to the knowledge-centric approach. Knowledge is defined as the state of understanding obtained through experience, learning, and the analysis of collected data/information. In other words, knowledge is an abstract content obtained by learning and analyzing a large amount of data/information [56]. Thus, the decision-making power of knowledge is much higher than that of information. An example of the knowledge that can be obtained using a set of previously mentioned information could be whether _vehicle\({}_{1}\)_ is likely to have a collision or not, which can be inferred by understanding and learning a lot of information, such as safety notifications, sensor measurement information, navigation information, etc., about the given vehicle and other vehicles in the neighborhood.
## 4 Numerical Results
### Analysis
#### 4.1.3 Pivot Offset  
Another practical parameter available to the TPJB geometry is the pivot offset, which is known to affect the load capacity of the bearing. The centrally pivoted pad, \(\chi_{\rm p}/\chi=0.5\), has a pivot on the center back. The typical offset range in this study was set as 0.5 to 0.6 (50% to 60% offset). As seen in Figure 9, the larger pivot offset tends to display a lower response amplitude, and delays the occurrence of Hopf bifurcation near 14 krmp. In cases with lower unbalance, the offset significantly pushes back the Hopf bifurcation and stabilize the response states, whereas, the Neimark-Sacker bifurcation emerges during the high speed operation near 17-19 krmp. If the rotor system has a higher imbalance on the disc, the period doubling bifurcation and some peculiar occurrences, such as 1/2 sub-synchronous response, were observed before the Hopf bifurcation event; this trend is more prominent when the TPJB has more pivot offset.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### Measured S11 Parameters  
In this subsection, we evaluate S11 parameters by antenna prototype measurements. The antenna is measured on the abdomen and on the hand, as shown in Figure 7a,b, respectively. The measurements were taken with and without the thin cloth between the antenna and the skin. The thickness of the cloth was approximately 3 mm. The measured S11 results in these different cases are shown in Figure 8. Additionally, S11 results measured with the hand phantom are included in the figure.  
It is observed that in the measurements, the antenna matching is relatively good within the whole bandwidth. One can note the variation between the measured S11 results. The best antenna matching is obtained as the antenna is located directly on the skin of the abdomen. The S11 parameter remains below \(-10\) dB up to 9 GHz, after which it remains below \(-9\) dB. As the antenna is located on the hand, directly on the skin, the S11 parameter remains below \(-10\) dB within the whole measured range except at 2-3.7 GHz and 6.5-7 GHz, in which the S11 remains below 8 dB. Including the cloth between the antenna and the skin decreases antenna matching slightly at the frequency range 6-7 GHz, but interestingly improves antenna matching at lower frequencies.  
Figure 5: Dependence of the antenna-skin (a-s) distance from the cable length: (**a**) a-s = 3.2 mm as the cable thickness is 6.5 mm, (**b**) a-s = 0 mm as the antenna is on the skin layer which has been cut partially below the thin cable.  
Figure 6: S11 results simulated with the layer model with different antenna-skin distances (coaxial cable thicknesses).  
Next, the impact of bending the antenna on S11 results is studied on the hand and on the abdomen by locating the antenna on the areas where occurs natural bending, e.g., on side of the hand. The measurements were taken with and without the cloth between the antenna and the skin. The results are shown in Figure 9. The S11 values measured on the unbended, i.e., straight part, are included for comparison. It is observed that the bending causes some changes in S11s at certain frequencies: on the hand, the major changes can be seen below 4.5 GHz, and on the abdomen, major changes are seen above 9.5 GHz. However, the antenna matching remains good, despite bending. In fact, the bending even improved the antenna matching on the abdomen measurements.  
Figure 8: Measured S11 in different cases: antenna set on the hand phantom (green line), on the volunteer’s hand with and without the cloth between the antenna and the skin (blue lines), volunteers abdomen with and without the cloth between the antenna and the skin (red lines).  
Figure 7: (**a**) Antenna located on the hand, (**b**) antenna located on the abdomen, both locations, measured with and without the thin cloth between the antenna and the skin.
## 3 Proposed Sparse SLMS Algorithms
### First Proposed Algorithm: SLMS-ZA  
The cost function of the LMS-ZA algorithm [7] was developed as  
\[G_{ZA}\left(\mathbf{w}(n)\right)=\left(1/2\right)e^{2}(n)+\lambda_{ZA}\,\left|\, \mathbf{w}(n)\,\left|\,\right|^{1}\!\right\}_{1} \tag{17}\]  
where \(\lambda_{ZA}\) stands for a positive parameter to trade off instantaneous estimation square error and sparse penalty of \(\mathbf{w}(n)\). It is worth noting that the optimal selection of \(\lambda_{ZA}\) is very difficult due to the fact that \(\lambda_{ZA}\) depends on many variables such as channel sparsity, instantaneous updating error \(e(n)\)  
Figure 2: Monte Carlo based MSE curves averaging over 1000 runs with respect to different characteristic exponents \(\alpha\in\{1.0,\,1.2,\,1.4,\,1.6,\,1.8\}\), in scenarios of dispersion parameter \(\gamma=1.0\), channel sparsity \(K=8\), channel length \(N=128\), and \(SNR=10\) dB.  
SNR, and so on. Throughout this paper, the regularization parameter will be selected empirically via the Monte Carlo method. According to Equation (17), the LMS-ZA algorithm was developed as  
\[\mathbf{w}(n+1) =\mathbf{w}(n)-\upmu\frac{\partial G_{ZA}(\mathbf{w}(n))}{\partial\mathbf{w}(n)} \tag{18}\] \[=\mathbf{w}(n)+\upmu e(n)\mathbf{x}(n)-\rho_{ZA}\text{sgn}\left(\mathbf{w}(n)\right)\]  
where \(\rho_{ZA}=\upmu\cdot\lambda_{ZA}\) depends the \(\upmu\) and \(\lambda_{ZA}\). To mitigate the \(\text{S}\alpha\)s noises, by constraining sign function on \(e(n)\), the SLMS-ZA algorithm is developed as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\upmu\text{sgn}\left(e(n)\right)\mathbf{x}(n)-\rho_{ZA} \text{sgn}\left(\mathbf{w}(n)\right) \tag{19}\]  
where the first \(\text{sgn}(\cdot)\) function is utilized to remove impulsive noise in \(e(n)\) while the second one acts as a sparsity-inducing function to exploit channel sparsity in \(\mathbf{w}(n)\). Please note that the steady-state mean square error (MSE) performance of the proposed SLMS-ZA depends highly on \(\rho_{ZA}\).
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Exploitable Protocols
#### 4.1.2 Packet Data Convergence Protocol  
Although the PDCP [10] adds only few header elements to the payload, these provide a good basis for a covert channel, especially with five bits of reserved header space. However, it must be taken into account that exploiting the PDCP sequence number as a carrier of hidden information entails a high risk of detection. So, in order to keep the probability of  
Figure 2: Header structure of SDAP [11], PDCP [10], and RLC [9].  
detection as low as possible, not every PDU can be modified. Ultimately, covert capacity and detection risk must be weighed. Alternatively, hiding information in the MAC-I field was discussed in the analysis of [PERSON] et al. [2]. This field is 32 bits in size and contains the Message Authentication Code (MAC) of the user data if integrity protection has been configured by the RRC layer. In 5G, this is enabled as a mandatory security feature for Control Plane messages over the Signaling Radio Bearer. Yet, for the user plane packets over the data radio bearer, it is only optional and is not used in real mobile networks, as shown, e.g., by [PERSON] et al. [25]. This header field could also be used in 5G for a covert transmission, but only in the PDUs of the control plane.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.16. NAVigational TEleX (NAVTEX)
Resolution MSC.96(72) defines the performance standards for the equipment installed on or after 1 July 2002. If the equipment was installed before 1 July 2002 and on or after 1 January 1997, it must comply with Resolution A.824(19). If the equipment was installed before 1 January 1997, it must comply with Resolution A.478(XII) [77]. The SDMD is compulsory equipment for all vessels of 50,000 gross tonnage and upwards [71]. 3.1.21. Sound Reception System  
The sound reception system allows the OOW to hear and determine the direction of sounds outside the bridge. It is a mandatory navigational aid if the bridge is totally enclosed and administration does not decide otherwise [71]. It receives outside signals (70 Hz-820 Hz) and reproduces them inside the bridge for the OOW so as they can perform their look-out duty effectively. Moreover, it determines the approximate direction of the sound [78].
## 3 Theory
### Support Vector Machine with Support Vector Regression (SVM-SVR)  
Support Vector Machine (SVM) with Support Vector Regression (SVR) is a powerful machine learning technique renowned for its effectiveness in capturing complex relationships and handling non-linear regression tasks. SVM-SVR builds upon the principles of SVM for classification tasks, extending them to the realm of regression by formulating the problem as a function approximation task. Mathematically, the prediction of an SVM-SVR model can be represented as follows [36; 37; 38]:  
\[y_{i,\ predicted}=\sum_{j=1}^{N}\alpha_{j}\,K(x_{i},x_{j})+b \tag{20}\]  
where \(y_{i,\ predicted}\) represents the predicted value for the \(i\)-th data point, \(N\) is the total number of support vectors, \(\alpha_{j}\) denotes the Lagrange multipliers associated with the \(j\)-th support vector, \(K(x_{i},x_{j})\) represents the kernel function, which computes the similarity between the \(i\)-th and \(j\)-th data points, and \(b\) is the bias term. The objective of SVM-SVR is to find the optimal hyperplane (or decision boundary) that maximizes the margin between data points while minimizing the error between the predicted and actual values. This is achieved by solving the following optimization problem [39]:  
\[min_{w,b,\xi,\xi^{*}}\frac{1}{2}\|\ w\ \|^{2}+C\sum_{i=1}^{n}(\xi_{i}+\xi_{i} ^{*}) \tag{21}\]  
Subject to:  
\[y_{i}-w^{T}\phi(x_{i})-b\leq e+\xi_{i} \tag{22a}\]\[w^{T}\phi(x_{i})+b-y_{i}\leq\epsilon+\xi_{i}{}^{*} \tag{22b}\] \[\xi_{i},\,\xi_{i}{}^{*}\geq 0 \tag{22c}\]  
where \(w\) represents the weight vector, \(b\) is the bias term, \(\xi_{i}\) and \(\xi_{i}{}^{*}\) are slack variables that allow for deviations from the margin, \(\epsilon\) is the margin of tolerance, and \(C\) is the regularization parameter that controls the trade-off between maximizing the margin and minimizing the error. SVM-SVR can capture non-linear relationships and high-dimensional interactions through kernel functions, allowing for greater flexibility in modelling complex datasets. Additionally, SVM-SVR inherently performs feature selection by focusing only on the support vectors, thereby reducing the computational complexity and memory requirements, especially for large-scale datasets.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 3 Results
### Nasal Obstruction Assessment
#### 3.1.3 Anterior Rhinomanometry (ARM)  
Rhinomanometry is a diagnostic procedure that measures nasal airflow resistance [36]. Active anterior rhinomanometry involves attaching a pressure-sensing instrument to one nasal passage at a time and measuring resistance individually on each side. The sensor tube quantifies the total airflow through the nasal passage and therefore the total nasal resistance [37].  
Total nasal resistance (TNR): TNR is the resistance to airflow during breathing, and is determined by measuring pressure and flow variations [37]. Three studies in this review reported TNR [21; 22; 27]. In the first [21], patients with septal deviation had a significant decline in mean partial nasal resistance for both nostrils one month following surgery (\(p<0.05\)), while no significant changes were observed in controls. Similarly, the second study [22] showed significant decreases in TNR at one and three months postoperatively compared to baseline (\(p<0.001\)). However, no significant difference was identified between the postoperative time points (\(p>0.05\)). In the third study [27], patients with septal deviation had significantly higher preoperative TNR than controls (\(p=0.001\)), although postoperative measures showed a significant decrease (\(p=0.001\)).
## 4 Empirical Study
### Feature Selection  
Diverse feature ranking and feature selection techniques have been proposed in the machine learning literature [25]. The purpose of these techniques is to discard irrelevant or redundant features from a given feature vector. Research also shows that there is no best ranking index for different datasets and different classifiers accuracy curves, as the function of the number of features used may significantly differ [24]. In this case, we use different methods to rank the features. Then feature subsets are generated based on the results of the feature ranking method. These feature subsets are further evaluated by using various classification algorithms. Finally, we obtain the selected feature subset which has the optimal performance for classification.
###### Abstract
DARTS applies a weighting scheme to a discrete operator pool to compose a continuous search space. However, it has to make a \"discrete\" choice of the operator from the encoded architecture where each operator has its weight, which shows a limitation of DARTS. It is suggested that this limitation could be relieved by temperature annealing of Softmax [13]. However, the actual design, implementation, and analysis of temperature annealing for this limitation have yet to be thoroughly explored. In this paper, we performed a systematic analysis of the temperature annealing of Softmax to illustrate the feasibility of relieving this limitation. DARTS (differentiable architecture search) has gained popularity [14; 15; 16] as a gradient-based approach that achieved state-of-the-art performance in NAS on datasets such as Penn Treebank, CIFAR-10, and ImageNet. DARTS is an algorithm that constructs models through a supernetwork, which searches for the optimal architecture. Unlike other NAS algorithms that have relied on RL-based or evolutionary-based approaches, DARTS parameterizes the model's layers to make them differentiable, allowing the supernetwork to construct the optimal model in a gradient-based manner [12; 17]. As explained earlier, to evaluate the possible operators for each layer, a weighted sum is performed on each operator, and they are combined into a single Softmax function. This is referred to as a mixed operator, which is used instead of selecting a single operator for each layer. Despite the success of DARTS in achieving high performance through a gradient-based approach, there are still several remaining challenges that need to be addressed. The \(\alpha\) parameter represents the weights of each operator at a specific edge. When the weights of operators went through the Softmax function at each edge (i.e., layer) as \"beta\", these weights can be seen as the contributions of each operator for that particular edge. All operators are treated as one-hot encoded vector like a labels. Therefore in DARTS, when selecting the final architecture, only the operator with the highest beta value is chosen, and the rest are discarded. During the search process, beta values are real numbers between 0 and 1 and are weighted sums of each operator's results. However, only one operator is used during testing, leading to architectural discrepancies between the search time and final models. This is known as the discrepancy problem. To mitigate this issue, we employ Softmax temperature annealing as a solution.
## 2 Modeling for OMEF
### Power System  
#### 2.1.1 Modeling for Power System  
(1) Power balance  
\[P_{G,i,t}-P_{D,i,t}-P_{\text{P2G},i,t}=g_{ii}V_{i,t}^{2}+\sum_{j\in i,j,i\
eq i }P_{ij,t} \tag{1}\]  
\[Q_{G,i,t}-Q_{D,i,t}=-b_{ii}V_{i,t}^{2}+\sum_{j\in i,j\
eq i}Q_{ij,t} \tag{2}\]  
\[P_{ij,t}=g_{ij}V_{i,t}^{2}-g_{ij}V_{i,t}V_{j,t}\cos\theta_{ij,t}-b_{ij}V_{i,t}V _{j,t}\sin\theta_{ij,t} \tag{3}\]  
\[Q_{ij,t}=-b_{ij}V_{i,t}^{2}-g_{ij}V_{i,t}V_{j,t}\sin\theta_{ij,t}+b_{ij}V_{i,t} V_{j,t}\cos\theta_{ij,t} \tag{4}\]  
where \(P_{G,i,t}\) is the active output of generator at bus \(i\); \(P_{\text{P2G},i,t}\) is the power input of P2G at bus \(i\); \(Q_{G,i,t}\) is the reactive output of generator at bus \(i\); \(P_{\text{D},i,t}\) and \(Q_{\text{D},i,t}\) are the active power demand and reactive power demand at bus \(i\); \(V_{i,t}\) is the voltage magnitude of bus \(i\); \(\theta_{ij,t}\) is the phase angle difference of branch \(ij\); \(P_{ij,t}\) and \(Q_{ij,t}\) are the active power flow and reactive power flow from bus \(i\) to bus \(j\); \(g_{ii}\) and \(b_{ii}\) are the shunt conductance and shunt susceptance at bus \(i\); \(g_{ij}\) and \(b_{ij}\) are the conductance and susceptance of branch \(ij\).  
(2) Power output limits  
\[P_{G,i,t}^{\min}\leq P_{G,i,t}\leq P_{G,i,t}^{\max} \tag{5}\]  
\[Q_{G,i,t}^{\min}\leq Q_{G,i,t}\leq Q_{G,i,t}^{\max} \tag{6}\]  
(3) Bus voltage limits  
\[V_{i,t}^{\min}\leq V_{i,t}\leq V_{i,t}^{\max} \tag{7}\]  
(4) Ramp rate limits  
\[-\text{DR}_{i}\leq P_{G,i,t}-P_{G,i,t-1}\leq\text{UR}_{i} \tag{8}\]  
where DR\({}_{i}\) and UR\({}_{i}\) are the down and up ramp rate limits.  
Figure 1: Framework of IEGS.
## 3 Results
### RQ4: How Effective Are the Selected Recognition-Based Methods in Terms of Security and Usability?
Research has shown that estimating the password space is a major issue in recognition-based systems. Most of the RBGP methods have small password spaces. Hence, they are vulnerable to brute force and guessing attacks [27]. Increasing the password entropy is a countermeasure against an attacker who intends to guess the registered location. Research has shown that in recognition-based systems, the only method that is used to expand the password space involves increasing the number of objects (such as icons and images) that are included in a challenge set. However, having too many objects in a challenge set may burden the users with identifying their pass-objects. Finally, the login time for graphical password methods is a crucial metric for assessing the ease of use of any authentication system. The time it takes to log in can significantly impact user satisfaction and may influence their willingness to use the method repeatedly. Recording the login time provides essential insights into a method's usability. A faster login time indicates better usability, making it a critical metric for assessing the method's effectiveness. This measurement is indicative of the ease and speed with which users can navigate the authentication process. By tracking login time, we can identify any potential bottlenecks in the process and make appropriate adjustments to improve usability. Table 3 presents a comparative analysis of the selected RBMs' login times. Among these, [37] had the fastest login time, followed by [27], whereas the method in [49] had the slowest login time. The method in [39] had the longest maximum login time. Regarding the mean login time, [33] outperformed most of the other methods. This is because other methods use either a password, a passphrase, or both, which makes the authentication process more time consuming. In [33], users only need to remember their registered locations, which are unique to each person, and how they log in. This method makes the user's job a lot easier and does not add any more things to remember.