esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Asymptotic Analysis of Proposed Enhanced Distributed Fountain Codes
### Asymptotic Analysis of Decoding Performance
From Lemma 1, the partial derivative of \(\Omega_{i}\Big{(}\overset{\rightharpoonup}{x}\Big{)}\) can be calculated as follows \[\frac{\partial\Omega_{i}\Big{(}\overset{\rightharpoonup}{x}\Big{)}}{ \partial x_{i}}=\frac{\Phi^{\prime}{}_{i}(x_{i})}{2}\Bigg{(}\frac{1}{n}\Gamma^{ \prime}{}_{1}\Bigg{(}\frac{1}{n}\underset{j=1}{\sum}^{n}\Phi_{j}(x_{j})\Bigg{)} +\frac{1}{t}\Gamma^{\prime}{}_{2}\Bigg{(}\frac{1}{t}\underset{j=n-t+1}{\sum} ^{n}\Phi_{j}(x_{j})\Bigg{)}\Bigg{)}\] (11) The average output degree \(\beta_{i}\) is the partial derivative of \(\Omega_{i}\Big{(}\overset{\rightharpoonup}{x}\Big{)}\) at \(\overset{\rightharpoonup}{x}=1\) as \[\beta_{i}=\left.\frac{\partial\Omega_{i}\Big{(}\overset{\rightharpoonup}{x} \Big{)}}{\partial x_{i}}\right|_{\overset{\rightharpoonup}{x}=1}=\frac{\Phi^ {\prime}{}_{i}(1)}{2}\Bigg{(}\frac{1}{n}\Gamma^{\prime}{}_{1}(1)+\frac{1}{t} \Gamma^{\prime}{}_{2}(1)\Bigg{)}.\] (12) The average input degree \(\alpha_{i}\) can be calculated as \[\alpha_{i}=(1+\varepsilon_{i})\beta_{i}=\frac{\sum_{j=1}^{n}k_{j}}{2k_{i}}(1+ \varepsilon)\Phi^{\prime}{}_{i}(1)\bigg{(}\frac{1}{n}\Gamma^{\prime}{}_{1}(1)+ \frac{1}{t}\Gamma^{\prime}{}_{2}(1)\bigg{)}.\] (13) The edge-perspective degree distribution can be expressed as \[ \begin{split}\omega_{i}\Big{(}\overset{\rightharpoonup}{x} \Big{)}&=\frac{\partial\Omega_{i}\Big{(}\overset{\rightharpoonup}{x} \Big{)}}{\partial x_{i}}/\frac{\partial\Omega_{i}\Big{(}\overset{\rightharpoonup }{x}\Big{)}}{\partial x_{i}}\Big{|}\overset{\rightharpoonup}{x}=1\\ &=\phi_{i}(x_{i})\frac{\bigg{(}\frac{1}{n}\Gamma^{\prime}{}_{1} \Bigg{(}\frac{1}{n}\underset{j=1}{\sum}\Phi_{j}(x_{j})\Bigg{)}+\frac{1}{t} \Gamma^{\prime}{}_{2}\bigg{(}\frac{1}{j}\underset{j=n-t+1}{\sum}\Phi_{j}(x_{j })\bigg{)}\bigg{)}}{\big{(}\frac{1}{n}\Gamma^{\prime}{}_{1}(1)+\frac{1}{t} \Gamma^{\prime}{}_{2}(1)\big{)}}\end{split} \] (14) Finally, by substituting (11) into (6), we can obtain the expression of asymptotic performance in (5). \(\square\)
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.2 Multi-Tone Transmission Support  
To reach the massive device deployment objective, NB-IoT introduces the allocation of Resource Units (RU) to multiple User Equipment (UE) contrary to LTE where the whole resource block is  
Figure 2: Narrow band Interest of Things (NB-IoT) Flexible Allocation inside Long-Term Evolution (LTE) spectrum (in-band and guard-band) and when referring the Global System for Mobile Communications (GSM) spectrum (standalone).  
allocated to a single UE in the uplink. In this regard, tones (frequency domain) with different duration are allocated to UEs. For the uplink transmission, each tone may either occupy 3.75 kHz or 15 kHz of transmission bandwidth based on the SC-FDMA scheme; for downlink NB-IoT uses 15 kHz of transmission bandwidth with OFDM scheme as LTE. With 15 kHz spacing, NB-IoT can dedicate either single-tone (8 ms) or multi-tone (3 tones, 6 tones, and 12 tones) to different UEs with the duration of 4 ms, 2 ms, and 1 ms, respectively. On the other hand, the 3.75 kHz spacing supports only single-tone allocation to different users with 48 subcariers of 32 ms duration [11; 39; 40].
## 3 Results and Discussion
### Biocompatibility of the Nanocarrier
#### 3.4.2 Cytotoxicity Assay in Monolayer Culture of Cell Lines with Free and Loaded MTX
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cell Lines**} & \multicolumn{3}{c}{**IC\({}_{50}\) (nM)**} \\ \cline{2-4}  & **MTX** & **UiO-66-MTX** & **UiO-66-NH\({}_{2}\)-MTX** \\ \hline MDA-MB-231 & \(29\pm 0.13\) & \(103\pm 0.25\) & \(1069\pm 0.35\) \\ MDA-MB-468 & \(28\pm 0.15\) & \(113\pm 0.09\) & \(425\pm 0.21\) \\ MCF7 & \(75\pm 0.21\) & \(203\pm 0.14\) & \(1632\pm 0.24\) \\ MCF10A & \(67\pm 0.10\) & \(73\pm 0.36\) & \(1129\pm 0.12\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IC\({}_{50}\) values of cell viability (96 h) for different cell lines against MTX, UiO-66-MTX, and UiO-66-NH\({}_{2}\)-MTX. Figure 9: Chemosensitivity assay in MDA-MB-231, MDA-MB-468, MCF7, and MCF10A. This figure illustrates the cytotoxicity profile of drug treatments (MTX, UiO-66-MTX, and UiO-66-NH\({}_{2}\)-MTX) on (**A**) MDA-MB-231, (**B**) MDA-MB-468, (**C**) MCF7, and (**D**) MCF10A cell lines after 96 h of exposure. Various concentrations ranging from 0.1 nM to 5 uM were applied to each cell line. The data points represent the average \(\pm\) SEM of three independent experiments, with values normalised to the control group, which was set at 100% treated with 0.1% DMSO. lines except for MDA-MB-468. The findings suggest a potential differential response to the amino-functionalised UiO-66-NH\({}_{2}\)-MTX, warranting further exploration into its specific mechanisms and implications across diverse cancer cell types. It is important to note that the slow-release kinetics observed with UiO-66-NH\({}_{2}\)-MTX could be a critical factor in tailoring drug delivery systems for sustained and targeted therapeutic outcomes. These results hold significant importance in the development of an anticancer drug delivery system based on MOFs. Table 3 represents the IC\({}_{50}\) values, representing the drug concentration required to inhibit 50% of cell growth, for the MDA-MB-231, MDA-MB-468, MCF7, and MCF10A cell lines after treatment with MTX, UiO-66-MTX, and UiO-66-NH\({}_{2}\)-MTX for 96 h. The values are recorded as means with standard errors (\(n=3\)) and are normalised to the control group, which was set at 100% in DMSO.
# Analysis of Vehicle-Bridge Coupling Vibration Characteristics of Curved Girder Bridges
## 4 Spatial Vibration Analysis of the Coupled Time-Varying System of Vehicle-Curved Grider Bridge
### Analysis of the Impact of Vehicle Types  
Four common vehicle models were selected: heavy trucks, Santana cars, FAW Jiabao, and box trucks, named Car-1, Car-2, Car-3, and Car-4 [23], respectively. Considering the good level of deck irregularity, the Timoshenko curved beam element was used to discretize the structure. A self-written calculation program was made to get the vehicle-bridge coupled dynamic response of a car traveling at a constant speed of 40 km/h through a curved girder bridge and to analyze the impact of vehicle type on the vehicle-bridge coupled vibration of curved bridges.  
The comparison diagrams of both displacement and acceleration response time-history curves at the mid-span of bridges under different vehicle types are shown in Figures 13-17, and the comparisons of peak responses at the mid-span of bridges are shown in Table 4 and Figure 18.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Type**} & **Vertical** & **Vertical** & **Lateral** & **Lateral** & **Torsional** \\  & **Displacement (mm)** & **Acceleration (m/s\({}^{2}\))** & **Displacement (mm)** & **Acceleration (m/s\({}^{2}\))** & **Displacement (rad)** \\ \hline Car-1 & 1.890 & 0.124 & – & – & 0.753 \(\times\) 10\({}^{-4}\) \\ Car-2 & 0.318 & 0.079 & 3.449 & 0.747 & 0.128 \(\times\) 10\({}^{-4}\) \\ Car-3 & 0.196 & 0.035  
Figure 14: The mid-span vertical acceleration curves under different vehicles.  
Figure 13: The mid-span vertical displacement curves under different vehicles.  
Figure 15: The mid-span lateral displacement curves under different vehicles.  
From Table 4 and Figures 13-18, under the action of different vehicle types, the order of axial weight was Car-1, Car-4, Car-2, and Car-3. As the vehicle axle weight decreased, the vertical and torsional dynamic responses in the bridge span decreased while the lateral dynamic response gradually increased. The vertical displacement decreased from 1.89 mm to 0.20 mm; the vertical acceleration decreased from 0.12 m/s\({}^{2}\) to 0.03 m/s\({}^{2}\), the lateral displacement increased from 1.91 mm to 5.90 mm, and the lateral acceleration increased from 0.03 m/s\({}^{2}\) to 1.02 m/s\({}^{2}\).  
Figure 16: The mid-span lateral acceleration curves under different vehicles.  
Figure 17: The mid-span torsional displacement curves under different vehicles.  
Figure 18: _Cont._
## 4 Finite Element Analysis
### Comparison and Analysis  
Based on the aforementioned computational model, numerical simulations were conducted for both bolted and welded joint specimens. Load-displacement curves were obtained and compared with the curves obtained from experiments, as shown in Figure 12. Overall, the simulated curves for each joint specimen closely match the experimental curves, exhibiting consistent trends and deformation stages. When comparing the three stages of the loading process, it is found that:  
1. During the initial stage of elastic deformation, both the simulated and experimental curves exhibit linear elastic behavior. However, the stiffness of the simulated curve is higher than that of the experimental curve. This difference arises because the numerical simulation assumes ideal material behavior.
2. As the load increases, the stiffness of the curves begins to decrease with displacement. A comparison between the experimental and simulated curves of welded joints reveals that the stiffness change in the simulation process is slower, indicating better ductility.
3. When the prestressing steel bars start to yield, the stiffness of the specimens further decreases, and displacement increases rapidly with the increasing load. During this stage, the simulated curves closely match the experimental curves, exhibiting almost identical stiffness development. These results indicate that the numerical simulation method employed can accurately predict the tensile behavior of both types of joint configurations.
# Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
## 2 Materials and Methods
### Machine Learning Algorithm  
As has already been mentioned in Section 2.2, using the proposed formulation, the problem of seismic vulnerability ranking was converted to a binary classification problem. There are many available algorithms in the literature for this task [25; 26]. Working on the same dataset as OASP (Greece), the authors in [18; 20] have compared a variety of different ML models and they have concluded that the Gradient Boosting (GB) classifier offers the highest performance. Extending the previously established results, we will also employ GB in the sequel and to this end we are presenting a short overview of the algorithm.  
Gradient Boosting [27] belongs to the group of the ensemble classifiers [28]. It combines a large number of individual \"weak\" learners, most often Decision Trees [29]. Specifically, the algorithm incrementally learns a function with the form  
\[f(\mathbf{x})=\sum_{i=1}^{N}\alpha_{i}h_{i}(\mathbf{x};\theta_{i}). \tag{1}\]  
The \(h_{i}(\mathbf{x};\theta_{i})\) stands for the \"weak\" learners whose parameters \(\theta_{i}\) are trained during the iterations, \(N\) is a hyperparameter denoting the maximum number of such models and \(a_{i}\) is the learned weights of the ensemble. These are learned via a process known as \"boosting\" [30], wherein the model iteratively computes the gradient of an arbitrary differentiable loss function \(\mathcal{L}\) which is used as an input to train the next iteration. An overview of the process is shown in the following Algorithm 2[27]. The algorithm was implemented using the Python programming language (v. 3.11.5) and the dedicated Machine Learning library scikit-learn (v. 1.3.0) [31].
## 4 Results
### Lithofacies Analysis
#### 4.3.1 Lithofacies
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Facies Code** & **Lithofacies** & **Grain Size** & **Description** & **Interpretation** \\ \hline Spb & Parallel bedded sandstone & Coarse to fine-grained sand & Light grey to brownish-red well-sorted sandstone with well-developed parallel bedding (Figures 5 and 6B). Spb typically occurs in the middle portion of a lithofacies succession, where it commonly overlies Sm. & This lithofacies is interpreted as traction flows, indicating depositional processes within lower to upper flow regimes [29; 51; 53]. \\ \hline Spc & Planar cross-bedded sandstone & Coarse to fine-grained sand & Light grey to brownish-red well-sorted sandstone with planar cross-bedding. The lithofacies Spc is characterized by oblique intersections between the laminate and a layer interface, with intersection angles ranging from 10\({}^{\circ}\) to 30\({}^{\circ}\) (Figures 5 and 6C). This facies mainly appears in the middle part of a sequence, usually above Cs or Ss, and is characterized by an abrupt contact at the base. & This lithofacies is interpreted as the result of the migration of 2D dunes under unidirectional flow conditions or the margin of dunes under trative flow conditions [29; 54; 55]. \\ \hline Smg & Normally graded sandstone & Pebbly sand to fine-grained sand & Light grey to brownish-red well-sorted sandstone with normally graded bedding. Pebble and mudstone rip-up clasts, along with mud clasts, are observed in the lower part of the bed, grading upward into medium- to fine-grained sandstone (Figures 5 and 6D). Characterized by abrupt contact at the base. & This lithofacies likely represents deposition from hyperconcentrated flow at the boundary between debris flow and traction flow regimes [58; 59]. \\ \hline Stc & Trough cross-bedded sandstone & Coarse to fine-grained sand & Light grey to brownish-red well-sorted sandstone with roughly cross-bedding. The lithofacies Stc is characterized by laminate intersecting curved bounding surfaces at angles of 10\({}^{\circ}\) to 30\({}^{\circ}\) (Figures 5 and 6E). & This lithofacies is interpreted as the result of the migration of 3D dunes under unidirectional flow conditions or channel scour-and-fill deposits driven by low-energy tractive flows [60; 61]. \\ \hline Scr & Sandstone with climbing ripples & Coarse to fine-grained sand & This lithofacies may be associated with settling processes from suspended load under waning turbulent conditions [55; 62]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussion
### Summary of System Specifications  
Technical specifications of the designed system are consolidated in Table 2. The energy consumption of the individual components based on the previously mentioned experiments and additional measurements performed on individual components are presented in Table 3. This table also consolidates the maximum output achieved from these components.
## 5 Conclusions  
In our cryptographic experiments, we rigorously evaluated the influence of various activation functions on the encryption strength and training efficiency. Our analysis revealed a clear hierarchy in performance. While the models employing \"LeakyReLU and tanh\" and the innovative \"Mish and Sigmoid\" delivered mixed results in terms of security and had a more extended training duration, the model utilizing the \"ReLU and Sigmoid\" combination emerged pre-eminent. With an impressively efficient mean training time of 16.51 s for every 2000 steps as compared to \"LeakyReLU and tanh\" (19.42 s) and \"Mish and Sigmoid (20.35 s)\", this outcome underscores the efficacy of the traditional ReLU and Sigmoid activation functions, reiterating their superiority in this cryptographic context. Drawing from our exhaustive experimentation, and the vivid distinction in the loss metrics, we discern a compelling narrative: while innovation and advancements propel us forward, it is the synergistic blend of established methodologies like the \"ReLU and Sigmoid\" pair that offers an unparalleled defense against adversarial breaches. This study not only highlights the potency of the \"ReLU and Sigmoid\" pair but also serves as a testament to the enduring relevance of established methodologies amidst rapid advancements.  
However, our study is not without limitations. The scope of the activation functions tested was selective and may not encompass all possible combinations that could yield different results. These limitations underscore the need for further research, especially in exploring the integration of adaptive learning with dynamic activation selections, evaluating model robustness under environmental perturbations, and benchmarking against established cryptographic standards. In light of our findings, future endeavors should delve into the nuanced interplay of various activation functions, both within and across layers.  
Conceptualization, P.S., S.D. and P.P.; methodology, P.S.; software, P.S.; validation, P.S., S.D. and P.P.; formal analysis, P.S.; investigation, P.P.; resources, P.S.; data curation, P.S.; writing--original draft preparation, P.S.; writing--review and editing, P.S., S.D. and P.P.; visualization, P.S.; supervision, S.D. and P.P; All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
There is no conflict of interest between the authors.
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
## 4 Results of the Breakdown Test Procedure
### PE Sample  
Figure 4 shows the recorded PRPD patterns of a representative sample (one of the eight tested samples) during the PE sample breakdown test. The PDIV for this sample was 4 kV, as shown in Figure 4a. The results indicated a notable reduction in discharge activity during the positive half-cycle, whereas the negative half-cycle displayed a higher and more-concentrated discharge, resembling a linear growth pattern at a 90\({}^{\circ}\) angle. This behavior suggests the simultaneous occurrence of surface and corona discharges.  
Figure 4b shows the PRPD pattern recorded at an RPDIV of 5.5 kV. Although the discharge signal threshold was adjusted from 3 to 5 pC during this measurement, it did not affect the observed pattern. Compared with Figure 4a, there was a significant increase in the discharge activity across all quadrants, with a notable increase in the first and second quadrants compared with the third and fourth quadrants.  
Finally, Figure 4c depicts the last PRPD pattern recorded before breakdown, which occurred at 12.7 kV. The maximum discharge was approximately 25 nC. The corresponding discharge activity and voltage timing diagram is shown in Figure 4d for a pressurization duration of approximately 47 min.  
Figure 5 shows the images of the PE sample before and after final breakdown. The red circle highlights the area exhibiting significant carbonization, specifically located between the two electrodes and substrate. Notably, the epoxy resin covering the surface remained intact despite breakdown.  
Figure 4: Breakdown test results of the PE sample.  
Figure 5: Images of the PE sample before breakdown.  
Figure 6 shows the statistical results of the breakdown tests conducted on eight PE samples. The average PDIV was 5.13 kV, whereas the average RPDIV was 6.38 kV. The overall average breakdown voltage of the samples was 12.8 kV.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 2 System Model and Traffic Impact Analysis
### Impact of the Normal Traffic on Amplitude and Phase Mismatches  
In order not to disturb the normal communication traffic flow and to allocate the limited transmitting power to the communication signal as much as possible, the power of the injected calibration signal must be lower than noise in the real-time online calibration system. The power level relationship of communication signal, calibration signal and noise is shown in the Figure 2. Amplitude and phase mismatches to be calibrated are illustrated in Figure 3, where red curves are true values. We set the communication traffic signal and calibration signal are of equal power. It can be seen that the amplitude and phase errors become more dispersed in the presence of traffic. Therefore, for weak calibration signals, how to achieve high-accuracy calibration in the presence of strong interference from communication traffic is a difficult problem.  
Figure 2: Power level relationship of communication signal, calibration signal and noise.
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 3 Performance Analysis  
The evaluation of the navigation signal security authentication capability encompasses three main aspects: security, authentication efficiency, and cost [24]. These aspects will be analyzed sequentially as follows.
## 2 Materials and Methods
### Measurements
#### 2.4.2 Electromyography  
We equipped the subjects with sEMG sensors, connected to a mobile sEMG system (Ultium, Noraxon). Beforehand, skin was prepared to achieve stable electrode contact and high skin conductance by lowering the impedance. For this purpose, hair was removed from the skin positions to be covered with a disposable razor. The skin was cleaned with alcohol and treated with an abrasive gel. This method is suggested for clinical use [28]. Adhesive gel dual disposable electrodes served as sEMG electrodes; we placed these on dominant body side on the M. erector spinae thoracic (EST) and lumbar (ESL), M. obliquus externus abdominis (OE), M. vastus medialis (VM), M. biceps femoris (BF), M. tibialis anterior (TA), and M. gastrocnemius medialis (GM) according to SENIAM guidelines (Figure 4). We took care to place the electrodes on the midline of the abdominal area, perpendicular to the length of the muscle fibers between the muscle tendon junction and the nearest innervation zone. The muscles were chosen and the electrodes were attached, standardized in such a way that they did not come into contact with the exoskeleton.  
An MVIC was performed for all muscles prior to the test. Based on the results of a previous study [17], the performance of the MVICs was also trained on the two previous days, in order to achieve the highest possible level of standardization.  
The rectified sEMG signal was set to a sampling rate of 2.000 Hz per channel and filtered using a fourth-order Butterworth filter with a bandpass of 20-500 Hz for GM and TA. To remove heart artefacts in OE and RA signals, a hampel filter was applied, which is a useful method to remove sEMG signal outliers [29]. SEMG signal was root mean squared (RMS) with a window size of 250 ms and normalized using MVICs.
## 4 Methodology
### Solution Representation and Evaluation
#### 4.1.1 Solution Representation  
The ASS-MCDS method uses an on/off variable representation of solutions. Specifically, a trial solution X will be represented as a bit vector \((x_{1},x_{2},\ldots,x_{V_{n}})\), where \(V_{n}\) is the number of nodes in the graph. The subscript/index values \(1,2,\ldots,V_{n}\), refer to the corresponding nodes in the graph vertices set \(V\). If a component \(x_{i}\) of \(X\) has the value \(0\), then the node represented by the \(i\)-th column in \(V_{n}\) is not contained in the node subset represented by the solution \(X\). Otherwise, the solution \(X\) contains the \(i\)-th node.