esa-sceva/satcom-chunk-collection huggingface dataset:
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 4 Aeronautical Mobile Telemetry
### Channel Coding in the 21 st Century
Flight tests using the \((32,26)\times(32,26)\) turbo product code were reported in [185]. The measured coding gain was 3 dB for PCM/FM and 4-5 dB for SOQPSK-TG. Because the bit error rate vs. \(E_{b}/N_{0}\) curve is steep for coded links, it was observed that the coded link was either error-free or lost. It was noted the resynchronization time (in the wake of a link outage) was much greater for the coded system than for the uncoded system, but that the increased resynchronization time did not have much of an impact on the overall link availability. This is because bit errors and link outage/resynchronization reduce link availability in the uncoded link. In contrast, only link outage/resynchronization reduces link availability in the coded link. The recommendation was positive: the cost of bandwidth increase \((256/169\approx 1.5)\) was justified by increased reliability. The \((64,57)\times(64,57)\) was seriously considered by Boeing for use in testing the Boeing EA-18G Growler [186] at NAS Pax River [187; 188]. In the end turbo product codes were not used for the EA-18G tests due to concerns over long resynchronization times [189]. Turbo product code development was dropped and disappeared into obscurity in aeronautical mobile telemetry circles. The next set of efforts, described in Sections 4.2.1-4.2.4, have been field-tested, are currently in use, or are actively being developed for future use. Figure 22: A graphical representation of encoding used by a turbo product code based on \((n_{1},k_{1})\) and \((n_{2},k_{2})\) linear block codes.
## 2 Materials and Methods  
### Case Situation Analysis  
This study used a professionally manufactured ceramic substrate as an example. As shown in Figure 2, it was a 24 \(\times\) 10 array structure and had a circuit pattern imprinted on it. The metal circuits were linked with the ceramic substrate by electric plating, which could make the locations of the circuits be more precise and allow the space between circuits to be narrower. This technique is largely used in industries such as high-power LED and semi-conductor manufacturing. The ceramic substrate joint images are collected by the AOI terminal. The system consists of a charge-coupled device (CCD) digital camera (TAIYO TT-VISION A400 model) and a three-color (red, green, and blue) hemispherical light-emitting diode array light resource.  
We converted every gray scale image into a binary image by choosing a predefined threshold (T) in the original image, and then converting every pixel black or white according to whether its gray value is greater than or less than T. Grayscale images contain pixel values in the range from 0 to 1. One way to determine a \"good\" value for T is to look at the grayscale histogram of the image (Figure 3). Because ceramics have a continuous surface, most of the pixels in the image are bright. Since we wanted to examine the defects of ceramic substrates, we wanted to turn off the healthy pixels, while leaving the pixels for the defects turned on. Thus, we had to choose a value of T somewhere before the large peak and turn pixels above that value \"off\". We set the threshold to 0.8. Thus, we used the less operator \(\leq\) to compare the blurred image to the threshold T. Therefore,  
* A pixel turns on, if its gray level is \(>\)0.8.
* A pixel turns off, if its gray level is \(\leq\)0.8.  
Figure 1: Flow chart of this study.  
Manufacturers use AOI devices to examine the defects of ceramic substrates and then manually conduct a re-examination. Overkilled ceramic substrates will be returned to the production line for further processing, and ones with genuine defects will be labeled as such. The defect detection rate of AOI is about 20% (Equation (1)), and after the manual re-examination, 15% of the detected defects are found to be false alarms (images that are actually standard).  
\[\text{AOI Defect detection rate}=\frac{\text{Number of ceramic substrates detected as defected by AOI}}{\text{Total number of ceramic substrates}} \tag{1}\]
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 4 OA-UWSN MAC Protocol Simulation Environment Settings
### Acoustic Properties of Underwater Channels
#### 4.2.2 The Model of Energy Attenuation  
The energy consumption model [34] for acoustic communication in underwater nodes is shown in Equation (11):  
\[E(r)=P\bullet T_{p}\bullet A(r). \tag{11}\]  
In Equation (11), \(E(r)\) is the energy required for the node to send information, \(T_{p}\) is the data transmission time, \(P\) is the lowest power that each node can normally receive a packet, and \(A(r)\) is the energy attenuation of the data packet when the underwater transmission distance is \(r\). Equation (12) is  
\[A(r)=r^{k}a^{r}. \tag{12}\]  
In Equation (12), \(a\) is the attenuation coefficient, \(r\) is the communication distance between nodes, \(k\) is the energy diffusion factor and is generally 1.5, and \(a=10^{a(f)/10}\) is determined by the absorption coefficient \(a(f)\)[35]. Equation (13) is  
\[a(f)=0.11\frac{10^{-3}f^{2}}{1+f^{2}}\ +\ 44\frac{10^{-3}f^{2}}{4100+f^{2}}\ +\ 2.75\ \times\ 10^{-7}f^{2}+3\ \times\ 10^{-6}. \tag{13}\]  
In Equation (13), \(f\) is the carrier frequency, the unit is kHz, and the absorption coefficient unit is dB/m.  
The parameter settings are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & \(P\) & \(F\) & \(R\) & \(k\) \\ \hline Value & 3 mW & 25 kHz & 20 m–36 m & 1.5 \\ \hline \end{tabular}
\end{table}
Table 3: Acoustic energy consumption parameters.
## 3 Theoretical Model
\begin{array}{l}m(\dot{u}-vr)=X_{\dot{u}}\dot{u}+X_{uu}u^{2}+X_{vv}v^{2 }+X_{rr}r^{2}+X_{vr}vr+X_{\delta\delta}\delta^{2}+X_{F_{1}}\\ m(\dot{v}+ur)=Y_{\dot{v}}\dot{v}+Y_{\dot{r}}\dot{r}+Y_{\dot{v}}v+Y_{r}r+Y_{|v|}v |v|+Y_{\dot{v}|r}|v|+Y_{\delta|r}\delta|r|+Y_{\delta}\delta+Y_{F_{1}}\\ I_{z}\dot{r}=N_{\dot{v}}\dot{v}+N_{\dot{r}}\dot{r}+N_{v}v+N_{r}r+N_{v|v|}v|+N_{v| r|}v|+N_{r|r}|r|+N_{\delta}\delta+N_{\delta|r|}\delta|r|+N_{F_{1}}\\ \end{array} \right. \tag{6}\]  
where \(Y_{v}\) and \(N_{v}\) are velocity coefficients, indicating the slopes of the lateral force and yawing moment curves at the origin for the pump-jet vector thrust submarine in sideslip motion. Similarly, \(Y_{r}\) and \(N_{r}\) represent the angular velocity coefficients, indicating the slopes of the lateral force and yawing moment curves at the origin when the pump-jet vector thrust submarine has both linear velocity and angular velocity. Additionally, \(Y_{\delta}\) and \(N_{\delta}\) are the vector jet angle coefficients and \(X_{\dot{u}r},Y_{\dot{v}},Y_{\dot{r}},N_{\dot{v}},N_{\dot{r}}\) are also acceleration coefficients. \(X_{uu}\), \(X_{vv},X_{rr}\), \(X_{\delta\delta}\), \(Y_{v\,|v|},N_{v\,|v|}\), \(Y_{r\,|r|}\), etc., are second-order or higher hydrodynamic coefficients as nonlinear coefficients. The parameters \(X_{vr},Y_{v\,|r|},Y_{\delta\,|r|},N_{v\,|r|}\) and \(N_{\delta\,|r|}\), among others, are partial derivatives with respect to two or more motion parameters and are coupling coefficients. \(X_{F1}\) and \(Y_{F1}\) represent the components of the pump-jet vector thrust force and \(N_{F1}\) represents the torque due to the pump-jet vector thrust force in the horizontal plane. The expressions for the lateral force and yawing moment induced by sideslip motion are shown in Equations (7) and (8) [47]. \[Y_{(v)}=Y_{0}+Y_{\dot{v}}v+Y_{v|v|}v^{2} \tag{7}\]  
\[N_{(v)}=N_{0}+N_{\dot{v}}v+N_{v|v|}v^{2} \tag{8}\]  
The symbols \(Y_{0}\) and \(N_{0}\) represent undetermined coefficients that can be obtained through data fitting. The expressions for the lateral force \(Y_{r}\) and yawing moment \(N_{r}\) induced by the yaw motion are shown in Equations (9) and (10) [47]. \[Y_{(r)}=Y_{r}r+Y_{r|r|}r^{3} \tag{9}\]  
\[N_{(r)}=N_{r}r+N_{r|r|}r^{3} \tag{10}\]
# Game Theory-Inspired Evolutionary Algorithm for Global Optimization
## 3 Proposed Algorithm: GameEA
### Framework of Proposed Algorithm  
Some symbols are defined in Table 1. Algorithm 1 presents the general framework of GameEA. The initialization procedure initially generates \(N\) initial players and initializes the active payoff of the game and the passive payoff of the match for each player. Within the main loop, in case the termination condition is not met, each player \(l_{i}\) selects an opponent \(l_{j}\) as his/her challenger, and the challenger makes a decision by carefully checking the selected player. At the beginning of the game evolution, considering that the players are very weak, namely \(I_{i}{}^{v}=0\), we let the player perform the imitation operation with \(P_{1}\) to produce a new individual (shown in step 7). Evolving ahead, the challenger \(l_{i}\) makes a decision by carefully checking the selected player \(l_{j}\), then the imitation or the belief learning procedure is employed for offspring generation. If player \(l_{j}\) is more competitiveness then player \(l_{i}\), player \(l_{i}\) decides to imitate some genes from player \(l_{j}\) by applying the imitation operator. Otherwise, player \(l_{i}\) insists that it will become more competitiveness by applying the belief learning operator. GameEA has only one population (set of players) and generates new offspring through the imitation operator between the challenger and the opponent and the belief learning operator via self-training strategies. In GameEA, the objective values are not used to calculate the dominance among the players, and the total expectation payoffs based on the history information are employed to facilitate the player becoming a rational decision-maker. The implementation details of each component in GameEA are described in the succeeding paragraphs.  
```
Begin
1.\(t\): \(=1\); // iteration number
2. Initialize players set \(\mathbf{I}\), \(I_{i}{}^{A}\):= 0, and \(I_{i}{}^{P}\):= 0 ( \(|\mathbf{I}^{t}|=N\), 0 \(<I<N\)); // Initialize players population for iteration
3. Evaluate \(f(\mathbf{I})\); // for each \(l_{i}\) of \(\mathbf{I}\), evaluate \(I_{i}{}^{\text{obj}}\);
4.while\(t>T_{\text{max}}\)do
5. Select 2 different competitors \(I_{i}\) and \(I_{j}\) from \(\mathbf{I}\);
6. Refresh the payoff of \(l_{i}\) and \(I_{j}\): \(I_{i}{}^{v}\):= \(I_{i}{}^{a}+I_{j}{}^{P}\), \(I_{j}{}^{v}\):= \(I_{j}{}^{a}+I_{j}{}^{P}\); // the following steps are responsible to reproduce a new player \(l_{i}\)
7. if \(I_{i}{}^{v}==0\)&& random() \(<P_{1}\)then Perform imitation operator: \(l_{i}=imitation(I_{i}\), \(l_{j})\);
8.else
9. Calculate the expectation payoffs E(\(l_{i}\)) of \(l_{i}\) using Equation (3);
10.if E(\(l_{i}\)) \(>0\)then Perform imitation operator: \(l_{i}=imitation(l_{i}\), \(l_{j})\);
11.elseif random() \(<P_{2}\)then Perform belief learning operator: \(l_{i}=\)beliefLearning(\(l_{i}\));
12.\(t\): \(=t+1\);
14.endwhile
```  
**Algorithm 1**GameEA.
# Large-Scale Modeling of Absorbing Aerosols and Their Semi-Direct Effects
## 2 Methods
### Model Description
Source areas are prescribed as regions with less than 10% vegetation cover, which is derived from the fraction of satellite retrievals of absorbed photosynthetic active radiation. Surface roughness is set to 0.001 cm in dust source areas. Simulated vertical dust fluxes are attributed to the insoluble coarse and accumulation modes in the model. For Saharan dust sources an additional source mask based on the infrared dust index from the SEVIRI instrument on the geostationary Meteosat Second Generation satellite is implemented [37; 38]. Dust emission fluxes were computed utilizing the scheme of [39], which computes the dust emission fluxes depending on surface conditions, like vegetation cover and soil moisture, and surface wind friction velocities for each potential dust source location. The dust flux emissions are grouped into the insoluble accumulation and coarse modes of the HAM aerosol scheme. The accumulation mode contains particles in the size rage 0.1 to 1 um, and the coarse mode particles larger than 1 um, with standard deviations of 1.6 and 2.0, respectively. Super-coarse dust particles are neglected [40]. Finally, sea salt emissions are parameterized according to [PERSON] et al. [41] with a dependence on sea surface temperatures according to [PERSON] et al. [42]. The aerosol dynamic processes in HAM include nucleation of sulfuric acid-water droplets, coagulation, condensation of sulfuric acid and aerosol water uptake [43; 44]. Condensation of sulfuric acid can occur on all particles. Coagulation is considered for the nucleation, Aitken and soluble accumulation modes. A mono-layer coating of sulfate causes moving an insoluble particle to a mixed (soluble) mode. Climatological aerosol optical properties are used in radiative transfer computations in ECHAM6 as standard [45], while those properties are dynamically computed when using the prognostic aerosol module in ECHAM6-HAM2. The effective refractive index of each aerosol mode is computed from volume-weighted averages of the refractive indices and Mie-scattering size parameters of the individual components assuming internal mixing [46; 47]. At 550 nm wavelength the complex refractive indices for BC is \(1.8+0.71i\)[48], and \(1.52+1.1~{}10^{-3}i\) for dust aerosol [45]. Extinction cross sections, single scattering albedos (SSA) and asymmetry parameters for each aerosol mode are provided via a look-up table and then re-mapped onto the bands of the ECHAM radiative transfer model. Absorption optical depths (AAOD) are then computed from aerosol optical depth (AOD) as AAOD = AOD \(\times\) (\(1-\)SSA).
# Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites
## 3 Multi-Sensor Photoelectric Fire Detection System
### Multi Sensor Photoelectric Fire Detection System Design  
#### 3.2.1 Photoelectric Fire Detector  
A photoelectric smoke detector is a type of fire alarm that uses light sensors to detect smoke. Figure 10 [31] illustrates the structure of a typical photoelectric fire detector. The  
Figure 8: Residual plot.  
Figure 9: Q–Q plot.  
fundamental operating principle involves light beams, either infrared or visible, scattered by smoke particles within a dark chamber. The sensors detect this light scattering, which plays a critical role in fire detection.  
As shown in Figure 10, a standard photoelectric detector comprises a dark chamber containing a light-detecting sensor and an LED, along with a circuit connecting them. It is crucial for the chamber to allow smoke entry while simultaneously obstructing any external light sources, except for the light emitted by the LED. The circuit powers the LED to emit light at regular intervals, and the sensor measures the amount of light, enabling the detection of smoke and the assessment of potential fire.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Alternative Graph  
An alternative graph is a directed graph and denoted by \(G(N,F\cup A)\) where \(N\) is the set of nodes and \(F\cup A\) contains all edges. The edges in set F represents precedence relations and are usually visualized by the solid directed weighted edges. On the other hand, the edges in set \(A\) are alternative paths and visualized by doted/dashed edges [47; 137]. Alternative edges can be weighted or not dictating whether swapping is allowed or not. In the context of shop scheduling problem, swapping refers to the ability for jobs to swap machines to avoid deadlock [48].  
An alternative graph is a generalization of a disjunctive graph (described in Section 5.14.1) in which undirected edges represent the alternative path. Based on this, the disjunctive graph is categorized as a mixed graph, while the alternative graph is classified as a directed graph.  
The alternative graph can be useful to model the scheduling problem with blocking situation (described in Table 3) [47; 48; 138; 139]. In the corresponding alternative graph of a manufacturing system considering the blocking situation, nodes represent the op  
Figure 16: A petri net representing a manufacturing system with a job and 2 machine (M1 and M2) such that the job can be processed by either M1 or M2.  
erations. Edges in \(F\) showcase the precedence relation between consecutive operations, while edges in \(A\) showcase an alternative processing order for operations that cannot occur concurrently [48].  
Figure 17 is an example for an alternative graph representing a partial manufacturing system with blocking situation. In this example, there are two operations \(i\) and \(j\) that cannot be done on the same time, i.e., these operations are processed by the same machine [47]. Also, \(\sigma_{i}\) and \(\sigma_{j}\) denote the operations associated with the blocking operations. Solid edges with weights show that \(i\) and \(j\) should be processed before \(\sigma_{i}\) and \(\sigma_{j}\), respectively. Since \(i\) and \(j\) that cannot be done concurrently, dashed edges show alternative paths for them. Eventually, one of these alternatives will be selected.
# CVE2 ATT&CK: BERT-Based Mapping of CVEs to MITRE ATT&CK Techniques
## 4 Discussion
### Error Analysis  
This subsection revolves around understanding the roots of the multi-label SciBERT model limitation. After a methodological investigation that aims to identify the cause of the model's errors, the observed performance deficiencies are further discussed. Table 4 presents different CVEs whose predicted techniques differ partially or completely from the labeled ones. For most errors in the dataset with multiple techniques tagged, the model succeeds in labeling a subset of correct techniques. This observation stands true for errors 1, 2, and 3 from Table 4. While analyzing error #1, the model extracts the most obvious technique, pointed out by language markers such as _password unencrypted_, _global file_, but fails to make the deduction that, in order for a user to access the file system, a valid account must be used. In contrast, the model successfully identifies the _Valid Accounts_ technique for error #2. In general, techniques that are not clearly textually encapsulated and whose understanding requires prerequisite knowledge are overlooked by the model. Figure 10 studies the model's choice of labels for CVE #2 from Table 4 using Lime [18], the model successfully recognizes the predominant label (i.e., _Valid Accounts_). Moreover, the model correctly identifies the most important concept, the word _authenticated_, which points in the direction of _Valid Accounts_. We can observe that there are techniques that are not ambiguous for the model and for which the labeling process is straightforward; such an example is _Valid Accounts_. The model extracts only the relevant features for the label and the technique is correctly identified. For the _Exploitation for Client Execution_, the model identifies patterns that suggest that the CVE should be mapped to the given technique, as well as patterns that suggest the contrary. Being capable to identify features that are correlated to both situations confuses the model. This problem results from the fact that the meaning behind multiple techniques is overlapping and, as a result, relevant features for a given technique cannot be differentiated. An interesting aspect is revealed in error #3, namely that the model correctly tags _File and Directory Discovery_, but also associates the CVE with _Exploit Public Facing Application_, instead of _Command and Scripting Interpreter_. Both techniques in the MITRE ATT&CK Enterprise Matrix could be equally correctly mapped on the given text description. This is an important observation and points out the established CVE labeling methodology; this highlights a fault in the data collection procedure, rather than the model's capacity to learn the multi-labeling problem. Example #4 presents a similar case, since the predicted technique _Endpoint Denial of Service_ is a correct label for the CVE, although it does not appear among the true labels.
# Dynamic Identification of Tensile Force in Tie-Rods by Interferometric Radar Measurements
## 5 Estimation of Tensile Force Starting from Vibration Data
### Experimental Identification of Natural Frequencies
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode Number**} & \multicolumn{3}{c}{**100 kN**} \\ \cline{2-4}  & \(f_{r}\) **(Hz)** & \(f_{a}\) **(Hz)** & \(f_{r}\)-\(f_{a}\)/\(f_{r}\) **(\%)** \\ \hline
1 & 37.160 & 37.134 & 0.070 \\
2 & 98.214 & 99.390 & 1.187 \\
3 & 126.488 & 126.866 & 0.299 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Natural frequencies experimentally determined starting from interferometric radar data (\(f_{r}\)) and accelerometric data (\(f_{a}\)). Applied tensile force: 100 kN.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## Appendix C Useful Results  
Calculation of \(\mathbb{E}\left[d_{k}^{2}\right]\)  
\[\mathbb{E}\left[d_{k}^{2}\right]=\int_{R_{\min}}^{R}r^{2}f_{d}(r)dr=\int_{R_{ \min}}^{R}\frac{r^{2}}{R-R_{\min}}dr=\frac{R^{3}-R_{min}^{3}}{3(R-R_{min})}. \tag{13}\]  
Calculation of \(\mathbb{E}\left[\frac{1}{d_{k}^{2}}\right]\)  
\[\mathbb{E}\left[\frac{1}{d_{k}^{2}}\right]=\int_{R_{\min}}^{R}\frac{1}{r^{2}} f_{d}(r)dr=\int_{R_{\min}}^{R}\frac{1}{r^{2}(R-R_{\min})}dr=\frac{1}{RR_{min}}. \tag{14}\]  
Calculation of \(\mathbb{E}\left[\log_{2}\left(\frac{c}{d_{k}^{2}}\right)\right]\)  
\[\mathbb{E}\left[\log_{2}\left(\frac{c}{d_{k}^{2}}\right)\right] =\int_{R_{\min}}^{R}\log_{2}\left(\frac{c}{r^{2}}\right)\frac{1}{ R-R_{min}}dr \tag{15}\] \[=\frac{\log(c)+\frac{2(R-R_{min}-R\log(R)+R_{min}\log(R_{min}))}{ R-R_{\min}}}{\log(2)}\]
## 4 Software Architecture
### Core Functional Module Design
#### 4.2.1 Scenario Management Module  
The primary functions of the scenario management module include adding new scenarios, associating nodes, and modifying scenarios. Here, the scenario refers to a physical testing site in the actual world.  
Nodes are crucial components when constructing the test scenarios. When a node accesses the TS, the user must associate it with the scenario. The association data include the scene to which it belongs and its coordinates.  
Figure 3: Software architecture of the C-V2X large-scale testing system. The software architecture is the organizational structure of a software system, which defines the relationships and interactions between its various components, as well as the overall design scheme [22].