esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 2 Related Work
In doing so, cross-layer optimization strategies are also designed to reduce the energy consumption of nodes while increasing the message delivery probability. Through experiments, the proposed scheme is shown to be superior to chosen benchmark schemes in terms of packet delivery probability. A limitation of the model is its dependence on accurate estimation of meeting probabilities and last meeting times, which may not always be reliable in highly dynamic and unpredictable environments. Additionally, while the model aims to reduce energy consumption, the computational complexity involved in maintaining acknowledgment tables and performing cross-layer optimization may lead to increased overhead, especially in large-scale networks with numerous nodes. This could negatively impact the overall network performance in terms of scalability and responsiveness. Unlike the discussed works, this paper proposes a routing model for OppIoT networks, which utilizes the beta distribution and node's context information to calculate the trust and reputation of every node. The values are then utilized to determine the suitable nodes to carry the data packets from source to destination nodes while isolating those nodes that have malicious intent.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 3 Preliminaries
The first step in answering the query is to determine the weight of \(e\) in each graph sketch. To be more specific, we locate the corresponding position \(M_{i}[H_{i}(s)][H_{l}(d)]\) on each graph sketch \(\mathcal{K}_{i}\), and return its value as an estimated weight. As a result, we can obtain a set of weights \(\{w_{1},w_{2},\ldots,w_{m}\}\). Based on the count-min sketch principle [5], we return \(min\{w_{1},w_{2},\ldots,w_{m}\}\) for the edge query. **Node query:** The purpose of node query is to return the aggregated edge weight for a given node \(n\). The answer is obtained by finding the row corresponding to node \(n\) (i.e., the \(H_{i}(n)\)th row) in the adjacency matrix \(M\), and then summing the values in that row. In the same manner, we obtain a set of sums \(\{sum_{1},sum_{2},\ldots,sum_{m}\}\), and return the minimum value for the node query. **Top-k node query:** The purpose of top-k node query is to return the list of top-k nodes whose aggregated weights are the highest in graph stream \(S\). In order to respond to this query, we maintain a min-heap with size \(k\) to hold the top-k nodes. Following the update of each edge \(e=(s,d)\), we conduct a node query for node \(s\) and obtain its aggregated weight \(w_{s}\). In the next step, we push the tuple \((s,w_{s})\) into the min-heap. The lowest-weight tuple will be popped out if the min-heap is full. We return the nodes in the min-heap as the answer to top-k node queries after all edge updates have been completed.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 6 Evaluation of 1-D CNN Models for Untrained Damage Cases
### Discussion on Damage Classification Results
For the untrained damage level D6 in Case 3 (see Figure 28d), all three shaded regions indicate that the damage level belonged to D7. This visualization highlights the CNN models' performance with partially untrained data and demonstrates their capability to classify untrained damage levels into the most similar trained levels. Additionally, combining results from two approaches using time-history and SVD data could strengthen classification results and provide additional insights for investigators. Figure 29 compares the accuracy of CNN models trained using time-history and SVD data under three untrained cases. All CNN models achieved high accuracy. For Case 1 and Case 2, the CNN models trained using time-history responses achieved accuracies of 99.6%  
Figure 28: Probability assessment of damage classification results: Case 3: (**a**) untrained D1; (**b**) untrained D2; (**c**) untrained D5; (**d**) untrained D6. The blue-shaded region shows results based on time-history data, the red-shaded region shows results based on SVD data, and the green-shaded region shows combined results. and 99.9%, respectively. Meanwhile, the CNN models trained using SVD responses had an accuracy of 100% for both cases. For Case 3, the CNN model trained using time-history data maintained high accuracy at 99.5%. However, the accuracy of the CNN model trained using SVD data fell to 86.9%. Note that the misclassification mainly came from the trained damage levels (i.e., D3 and D4). This comparison reveals that each approach adapted to particular situations. Therefore, using both approaches and integrating their results could provide better insights and enhance overall classification accuracy.
###### Abstract  
Industrial process monitoring is an important field of research where different chemical processes are monitored and controlled. In this work, electrical impedance spectroscopy (EIS) was used to analyze antisolvent based crystallization of sucrose solutions. The impedance and phase spectra were recorded for four known sucrose concentrations in water, and for each case, four predetermined amounts of ethanol were added. As a result, sixteen different solutions involving sucrose solutions of different concentrations and ethanol to water ratios were analyzed. Significant differences were observed in the magnitude and phase spectra of the solutions in the frequency range of 50 kHz to 300 kHz. The experimentally obtained data from the EIS were converted into frequency response models. Three continuous-time transfer function models of the first-order, second-order, and a second-order with a zero were estimated and compared. In addition, a 2-D electrical resistance tomography (ERT) system with a low conductivity sensor unit was designed and tested with demineralized water, tap water and industrial food grade saturated sucrose solution. Non-conducting phantom and sugar crystals were observed within the saturated sucrose solution using the Bayesian reconstruction algorithm. These demonstrations have the potential to be developed into a multi-frequency ERT systems for monitoring the distribution of the crystals in the reactor. The EIS modality can be a complementary process analytical technology (PAT) tool indicating supersaturation status and provide quality assurance.  
impedance spectroscopy; antisolvent sucrose crystallization; electrical resistance tomography; industrial process monitoring; data-driven models +
Footnote †: journal: Applied Computer Sciences, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (R.W.); [EMAIL_ADDRESS] (L.J.-S.)2 Separation Science Department, Lappeenranta University of Technology, 90-924 Lappeenranta, Finland; [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (T.K.)
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### RF Compatibility  
When designing a new RNSS signal, RF compatibility analysis between existing signals sharing the desired frequency band and candidate signals is essential. For example, RF compatibility with GPS was a key driver for determining the Galileo signal plan [35].  
The interference analysis methodology described in ITU-R M.1831 is used for RF compatibility analysis between RNSS systems [36]. This methodology can also be applied to RF compatibility analysis with Globalstar [21]. This methodology uses the effective carrier-to-noise density ratio, i.e., effective \(C/N_{0}\), as a measure of the impact of the interference from various interferers on the desired system. The effective \(C/N_{0}\) is obtained by modeling the interference as a white noise that induces the same performance degradation on the correlator output as follows [36]:  
\[\left(C/N_{0}\right)_{eff}=\frac{C}{N_{0}+I_{intra}+I_{inter}+I_{ext}} \tag{3}\]  
with  
\[I_{x}=\sum_{i=1}^{N}C_{i}\frac{\int_{-B_{r}/2}^{B_{r}/2}G_{s}(f)G_{i}(f)df}{ \int_{-B_{r}/2}^{B_{r}/2}G_{s}(f)df} \tag{4}\]  
where \(C\) is the received power of the desired signal, \(N_{0}\) is the noise power density, and \(I_{x}\) (\(I_{intra}\), \(I_{inter}\), \(I_{ext}\)) are the equivalent noise power density of intrasystem, intersystem, and external system interference, respectively. Assuming that each system consists of \(N\) satellites, the equivalent noise power density \(I_{x}\) (\(I_{intra}\), \(I_{inter}\), or \(I_{ext}\)) is calculated using Equation (4), where \(C_{i}\) is the received power of the interfering signals from the \(i\)th satellite, \(B_{r}\) is the receiver front-end filter bandwidth, and \(G_{s}(f)\) and \(G_{i}(f)\) are the normalized PSD to unity over the transmission bandwidth of the desired and interfering signals, respectively.  
The received power of the interference signal depends on the distance between the receiver and visible satellite, the number of visible satellites, transmitted power, and antenna gain at the elevation angle and off-boresight angle. However, these factors are time-varying and not related to the signal waveform. Therefore, when we design a signal waveform, we analyze RF compatibility by considering only the PSD of the desired and interfering signals independently from the above factors using the spectral separation coefficient (SSC) [37]. SSC is defined as shown in Equation (5), where a higher SSC lets the receiver undergo severe interference at the same received interfering signal power.  
\[k_{is}=\int_{-B_{r}/2}^{B_{r}/2}G_{s}(f)G_{i}(f)df \tag{5}\]
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 3 Violation of the Helmholtz Equation  
This section proves that SSHs are not the angular part solution of the Helmholtz equation. Consider an arbitrary spherical sector region \(\theta\in[\theta_{1},\theta_{2}]\), \(\phi\in[\phi_{1},\phi_{2}]\). The related SSH function is shown in (4), defined in the spherical coordinates. Therefore, if SSHsare solutions of the Helmholtz equation, they should satisfy the Helmholtz equation in spherical coordinates, shown in (7) below:  
\[\begin{split}&\frac{1}{r^{2}}\frac{\partial}{\partial r}(r^{2}\frac{ \partial p}{\partial r})+\frac{1}{r^{2}\sin\theta}\frac{\partial}{\partial \theta}(\sin\theta\frac{\partial p}{\partial\theta})\\ +&\frac{1}{r^{2}\sin^{2}\theta}\frac{\partial^{2}p} {\partial\phi^{2}}-\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}=0, \end{split} \tag{7}\]  
where \(p\) is the sound pressure at an observation point \((r,\theta,\phi)\) at time \(t\). By separating the variables, we have  
\[p(r,\theta,\phi,t)=\mathbf{R}(r)\mathbf{\Theta}(\theta)\mathbf{\Phi}(\phi) \mathbf{T}(t), \tag{8}\]  
and (7) can be rewritten to four ordinary differential equations:  
\[\frac{d^{2}\mathbf{\Phi}}{d\phi^{2}}+m^{2}\mathbf{\Phi} =0, \tag{9a}\] \[\frac{1}{\sin\theta}\frac{d}{d\theta}(\sin\theta\frac{d\mathbf{ \Theta}}{d\theta})+[n(n+1)-\frac{m^{2}}{\sin^{2}\theta}]\mathbf{\Theta} =0,\] (9b) \[\frac{1}{r^{2}}\frac{d}{dr}(r^{2}\frac{d\mathbf{R}}{dr})+k^{2} \mathbf{R}-\frac{n(n+1)}{r^{2}}\mathbf{R} =0,\] (9c) \[\frac{1}{c^{2}}\frac{d^{2}\mathbf{T}}{dt^{2}}+k^{2}\mathbf{T} =0. \tag{9d}\]  
Considering a single frequency case, with (3) and (4), we have  
\[\begin{split} p(r,\theta,\phi,t)=& e^{j\omega t} \sum_{n=0}^{n}\sum_{m=-n}^{n}\Gamma_{n}^{m}(r,k)K_{n}^{m}\\ \times&\mathcal{P}_{n}^{m}(q_{1}\text{cos}\theta+q_ {2})e^{jmu\phi},\end{split} \tag{10}\]  
where \(\omega=kc\) is the radial frequency. Then, based on (8)-(10), for SSHs to be considered solutions of the Helmholtz equation, \(\mathcal{P}_{n}^{m}(q_{1}\text{cos}\theta+q_{2})\) should satisfy (9b), and \(e^{jmu\phi}\) should satisfy (9a).
# The Polar Code Construction Method in Free Space Optical Communication
## 1 Introduction
Finally, the error performance of STPO was simulated under different code lengths, code rates, and turbulence intensities, and was compared with the MC method.
# All About Audio Equalization: Solutions and Frontiers
## 6 Sound Reproduction Applications
### Headphone Equalization
This idea was first proposed by [PERSON] and [PERSON], who simulated various headphones by convolving the input signal with the headphone impulse response [194]. [PERSON] _et al._[195] have compared headphone responses in a similar fashion while [PERSON] and [PERSON] [192] also included an equalizer modeling the acoustic isolation response of the headphones, so that the ambient noise could be included in the simulation. Another headphone application based on equalization filters is the modeling of head-related transfer functions (HRTF), which leads way to three-dimensional sound reproduction [196; 197]. Various digital filter design methods, such as balanced model truncation [198] and cascaded peak/notch filters [51; 199], have been used for modeling HRTF responses [200]. Simplification of HRTF filters has been used for demonstrating which spectral details are required for hearing directions correctly [201]. [PERSON] _et al._ have developed a method to compare loudspeakers using headphones with equalization filters, which are based on HRTF responses [202]. In _augmented reality audio_, a headset having microphones near the user's ears is used for combining reproduced and ambient sounds [203; 204; 205; 206]. The ambient sounds captured by the microphones must be equalized to enhance their naturalness [203; 205; 207]. Also the isolation of the headphones must be accounted for, since the leaked and reproduced ambient sound both reach the user's ear [208; 209]. The equalization of the microphone signal requires shelving and peak/notch filters [188; 208]. A related application is a real-time audio equalizer, which processes the external microphone sounds to be played instantly through the headphones, for example to enhance the perceived sound in a concert [210].
## 1 Introduction
### Contributions
Moreover, the KK algorithm is comprehensively presented, and due attention is paid to the related effects of different factors such as chromatic dispersion, the optoelectronic frontend, and IQ imbalance on the KK scheme;
7. A comprehensive tutorial on various approaches by which the conventional KK system challenges regarding the spectral broadening, high CSPR value, and associated additional sensitivity penalty can be attended to considering factors such as the system complexity and latency is presented;
8. Apart from different potential systems for 100 G and beyond applications that have been considered in the other review papers, we also present different SCOH-based experimental demonstrations that are capable of supporting high-speed short- and medium-reach use cases; Furthermore, an overview of recent research and development efforts on the enabling technologies to support 100 Gb/s and beyond (Tb/s) per wavelength is presented;
9. Additionally, we address further research directions and future optical network considerations for performance enhancement. In this context, advancement towards high-baud-rate transmission is presented, and the photonic integrated circuit (PIC) exploitation for low-cost, low-power consumption, and low-footprint schemes is comprehensively discussed. We highlight the need for tremendous synergy between photonic technologies and the software-defined networking (SDN) programmability to facilitate the functionalities of flexible transceiver systems that will be able to support the content-aware future transmission systems. An in-depth discussion on how the synergy can facilitate advanced functionalities and ensure full exploitation of photonic technology's potentialities and available resources is presented.
## 3 Results and Discussion
### In-Vitro Study  
#### 3.3.1 Corrosion Rate Results Obtained by Weight Loss Measurements  
The corrosion rates of the Zn stents were studied by determining the weight loss of the samples after immersion in a standard culture medium containing serum, under cell culture conditions for 72 h, and the results are illustrated in Figure 14. The corrosion rate (rate of degradation) of unexpanded, uncoated Zn stents (assigned here as control samples) was found to be around 0.0951 mm/year, which was lower than that previously observed for Mg and its alloys [13]. Unexpanded Parylene C-coated stents showed a 50% reduction in corrosion rates compared to unexpanded uncoated samples. Expanded stents, both uncoated and coated Zn stents, had a higher corrosion rate than their respective unexpanded stents, presumably because of the elastoplastic deformation caused in the stents by expansion. Expanded Parylene C-coated stents showed a 25% reduction in corrosion rate compared to expanded, uncoated samples. There were significant differences between the conditions, determined by a two-way ANOVA (coating and expansion group p values were each \(<\)0.001) and Holm-Sidak post-hoc tests (all conditions differed at the \({}^{*}\)_p_\(<\) 0.001 level, n = 3). Parylene C-coated stents degraded at significantly higher rates than uncoated ones, both before and after expansion. Both uncoated and Parylene C-coated samples increased their degradation significantly after expansion. Thus, using this measure of corrosion rate, the rate of corrosion was significantly altered by both coating and expansion. As mentioned earlier, crevices appeared in the polymeric coating during the expansion act and these presumably served as potential corrosion sites by exposing uncoated Zn metal to the culture medium. Thus, the rate of corrosion was significantly altered by both applying coating and conducting expansion.  
Figure 13: Photographs of water droplets and the measured contact angles \(\Theta\) on the surface of: (**a**) Parylene C-coated Zn; (**b**) Uncoated Zn.
## 3 Results  
### Characteristics of Study Population  
The study population comprised healthy controls (n = 15) and MR patients with preserved EF (n = 26). The 'Controls' group comprised eight men (53%) and seven women (47%), with a median age of 33 years (interquartile range, IQR = 20). In contrast, the 'Patients' group included 14 men (54%) and 12 women (46%), with a median age of 61.5 years (IQR = 15). Baseline characteristics encompass age (year), heart rate (HR, bpm), systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), and body surface area (BSA, m\({}^{2}\)). The cardiac function includes left ventricular end-diastolic volume indexed (LVEDVI, mL/m\({}^{2}\)), left ventricular end-systolic volume indexed (LVESVI, mL/m\({}^{2}\)), left ventricular mass indexed (LVMI, g/m\({}^{2}\)), LVEDV (mL), LVESV (mL), LVM (g), and left ventricular ejection fraction (LVEF, %). Only HR, SBP, DBP, and BSA exhibited a normal distribution among the baseline characteristics. The normally distributed continuous data are presented as mean \(\pm\) standard deviation (SD), while non-normally distributed data are expressed as the median (interquartile range, IQR). Among the baseline characteristics, notable differences in age and DSP were observed between controls and MR patients with preserved EF (\(p\)-value < 0.05). MR patients demonstrated significant elevation for LVEDV, LVEDVI, LVESV, and LVESVI compared to controls. The data were missing for LV mass in controls due to the retrospective nature of the collection. Table 2 provides a comprehensive overview of the baseline characteristics and cardiac function of the study participants. Navigating Hemodynamic Forces: Unveiling Trends in Healthy Controls vs. MR Patients with Preserved EF  
Figure 2 demonstrates the visual presentation of the statistical differences between controls and patients with MR for systolic-diastolic average (AVG) HDF in the S-L, I-A, and B-A directions (Figure 2(A1,A2)); systolic-diastolic RMS HDF in the S-L, I-A, and B-A directions (Figure 2(B1,B2)); overall AVG HDF (Figure 2(C1)); and RMS ratios (Figure 2(C2)). This outcome highlights the importance of considering hemodynamic forces when evaluating patients with MR. Table 3 presents the distinctive numerical values indicating only significant differences in HDF between controls and patients with MR. The full analysis will be available in Supplementary Table S1.
## 2 Methods
### Path Optimization  
To obtain the optimal path from the end effector trajectories generated by the improved FMT* algorithm, the following steps are taken. Firstly, the geometric envelope method is employed to check for interference between the robotic arm links and obstacles. Trajectories that result in collisions are removed. Then, the cost of each remaining path is calculated, and the path with the lowest cost is selected. Finally, the chosen path is smoothed using the \(B\)-spline algorithm.
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 2 Related Work
Furthermore, it does not simply reroute traffic along the available alternate routes in the event of a primary link failure; instead, it prioritizes real-time flows and reroutes them along a low-latency path calculated by the proposed method itself when a certain queue delay threshold is met.
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Naive Bayes Classifier  
Bayesian theory (formulated by [PERSON] in 1763) describes the a posteriori probability of an event as a function of conditional probabilities and the a priori probabilities of other events. From the perspective of an image classification, the a posteriori probability is defined as the likelihood of occurrence of a certain class given a certain feature vector (e.g., spectral bands). The conditional probabilities are the likelihoods of occurrence of a particular feature vector for a particular class. The a priori probabilities are the overall likelihood of occurrence of a particular class and the overall likelihood of occurrence of a particular feature vector. Conditional probabilities induce many correlations between features and as such are numerically expensive to derive for a large feature vector with many degrees of freedom. The solution to this problem assumes an independence of features and is the foundation of the Naive Bayes method. Such a simplification allows for the reduction of a multidimensional probability density matrix to one-dimensionaldensity estimation. Although independence of features is a weak assumption, in practice, the Naive Bayes method was found to be robust, especially for large training datasets with many features. The performance of the Naive Bayes classifier under the violated independence assumption was studied by [88], where it was proven that the classification results are unaffected if the dependencies between features are distributed evenly between the classes or if the dependencies cancel each other out. The main advantages of the Naive Bayes method are related to the quantification of classification uncertainty and numerical efficiency for large datasets. The main disadvantages are related to the assumption of independent features and to the \"zero frequency problem\", where one of the conditional probabilities is zero and consequently the entire a posteriori probability equals zero. In such a case, a Laplace smoothing technique can be used that involves adding a single row (one count) while computing conditional probabilities. This solution eliminates the \"zero frequency problem\" without any significant influence on the classification results.