esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stability Concepts
### Three-Stability  
[PERSON] defined the following stability concept, for a two-sided market, where the choice functions of each side over the contracts are \(F\) and \(G\):  
**Definition 10**.: [8] Subset \(S\) of \(E\) is _three-stable_, if there exists subsets \(A\) and \(B\) of \(E\), such that \(F(A)=S=G(B)\) and \(A\cup B=E\), \(A\cap B=S\). Pair \((A,B)\) with this property is called a _three-stable pair_, and \(S\) is a _three-stable set_.  
The explanation of the name, three-stable, is that we partition the set \(E\) of contracts into three parts, as showed in the Figure 1, \(S\), \(A\setminus S\) and \(B\setminus S\), where \(S\) is stable, \(A\setminus S\) is \(F\)-dominated by \(S\) and \(B\setminus S\) is \(G\)-dominated by \(S\).  
In the original marriage model, \(F\) and \(G\) select the one best partner for the men and women. It is easy to see that every three-stable set, \(S\), is a matching, and it is stable, since men prefer contracts in \(S\) to \(A\setminus S\) and women prefer \(S\) to \(B\setminus S\).  
On the other hand, if \(S\) is a stable matching, then it is also a three-stable set with the pair \((A,B)\), where we define \(A\setminus S\) as the set of contracts that the men prefer less than the contracts of \(S\) and \(B:=S\cup(E\setminus A)\).  
**Example 6**.: _Figure 2 illustrates a small example for a possible market. There are two possible contracts, \(a\) and \(b\), and \(F=Q_{1}\), \(G=Q_{1}\); then, only \(S=\emptyset\) is three-stable, and it can be achieved with two \((A,B)\) three-stable pairs, where \(A=\{a,b\}\), \(B=\emptyset\) or \(A=\emptyset\), \(B=\{a,b\}\)_  
Figure 1: Three-partition of the edge-set.  
Figure 2: Example for three-stability.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 4 Proposed Approach
These states are as follows: transmission (\(T_{x}\)), reception (\(R_{x}\)), CPU (processing), and LPM (low power mode). The LPM state is typically known as sleep mode. The Contiki operating system [38] uses an energy estimation unit, known as _Energest_ to obtain information regarding how long a system has been in various states. In addition, the power that is consumed by these states can be obtained through the datasheet of the CC2420 chipset. Finally, the \(E_{cur}(n)\) of a node \(n\) based on each state over time is obtained, as shown in Equation (7). \[E_{cur}(n)=T_{Tx}\times P_{Tx}+T_{Rx}\times P_{Rx}+T_{CPU}\times P_{CPU}+T_{ LPM}\times P_{LPM} \tag{7}\]  
where, \(T_{state}\) is the time duration the node spent in each state. \(P_{\text{state}}\) represents the corresponding power that is consumed in that state in a certain time unit. The REI (\(n\)) is for current node where \(n=\text{DODAG}\) root. The following context correlation is used in order to consider the residual energy of a node in a recursive way, reducing the influence of the parent's residual energy. The \(REI\) (\(n\)), for the current node, where n \(\
eq\text{DODAG}\) root, is obtained, as in Equation (8). \[REI(n)=Max\Bigg{(}\frac{E_{max}(n)-E_{cur}(n)}{E_{max}(n)},\quad REI(p_{n}) \times\beta\Bigg{)} \tag{8}\]  
where, \(\beta\) is the smoothing factor used to reduce the influence of the parent's residual energy on the routing. In our experiment, the value of \(\beta\) is set to 0.35, so that the power depletion does not affect greatly in the network. As the value of the smoothing factor \(\beta\) close to zero, the smoothing effect increases during the experiments.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Advanced Encryption Standard (AES)
#### 3.1.2 Integrity and Authentication in AES  
To achieve integrity and authentication for the exchanged packets we use a tag. To this purpose, we use the cipher-based message authentication code (CMAC) algorithm. Following NIST specifications, CMAC is equivalent to one-key MAC 1 (OMAC1): it is an authentication code for messages and it is based on block cipher algorithms. The initial step involved in CMAC is, first, to choose a block cipher \(E\) (and we used AES) and then thelength of the CMAC code \(t\). So, before sending a packet, it is generated, using AES and the shared symmetric key, a tag. Then, when the server receives a packet, it will accept it only if the tag-verify step returns \"true\", meaning that the packet has not been modified while transmitting it. Instead, if the tag-verify step returns \"false\" it means that the received packet has been illegally modified. Thus, while ensuring data integrity, using this method we ensure that data are generated only by entities which are part of the networks, i.e., they share a valid symmetric key. All the operations made in order to achieve confidentiality, integrity and authentication are shown in the Figure 3.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects
### Starlink Enhancement with 5G  
As of the third of February in 2022, SpaceX has launched 2091 of their expected 42,000 Starlink satellites into orbit. Starlink is a satellite internet network using a large LEO constellation of small satellites operated by SpaceX. Starlink provides advanced satellite internet services that are not currently available with traditional satellite internet services, including video calls, online gaming, streaming, and other global broadband services. Since the Starlink constellation is not fully deployed, there are gaps between satellites causing frequent and short-duration disconnections for global wideband applications that required  
Figure 10: SaT5G Architecture for Use Case 3: 5G Hybrid backhauling using multilinking testbed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SaT5G Use Case** & **eMBB SaT5G Testbed Use** & **Results** & **Conclusion** \\ \hline \multirow{4}{*}{Use Case 1} & & (1) Session start-up time–zapping & (1) Improved video distribution \\  & 5G MEC-enabled platform for & time: 1.5 to 0.9 s; (2) Start-up layer: & efficiency using mABR over \\  & 5G MEC-enabled platform for & Always starts on high-quality layer; & satellite, and video delivery \\  & 5G MEC-enabled platform for & (3) Bitrate and layers switches: 99\% & synchronization between screens; \\  & 5G MEC-enabled platform for & of the session at highest layer; & (2) Reduced E2E latency using \\  & 5G MEC-enabled platform for & (4) Bandwidth saving: Terrestrial & CMAF-CTE Dash over mABR link; \\  & 5G MEC-enabled platform for & time: 1.5 to 0.9 s; & (3) Multiscreen capability \\  & 5G MEC-enabled platform for & time: 1.5 to 0.9 s; & over satellite; \\  & 5G MEC-enabled platform for & time: 1.5 to 0.9 s; & \\  & 5G MEC-enabled platform for & time: 1.  
continuous connections, e.g., remote telework, video calls, and gaming. In addition to this challenge, Starlink does not currently provide mobile broadband services. To address the Starlink challenges, Simplewan has considered integrating 5G (and existing 4G) into the Starlink network [15]. Simplewan has developed a secondary and complimentary internet connection device to pair the 4G/5G with Starlink service providers. The Simplewan device employs Software-defined wide area networks (SD-WAN) technology enabler allowing the internet service provider to aggregate broadband connections between Starlink and 4G/5G networks automatically.
## 2 Structural Design
### Determination of Parameters of Four-Bar Mechanism  
According to the theoretical analysis in Section 2.3, we programmed the VB calculation program to adjust the parameters of the four-bar mechanism to obtain the ideal movement trajectory of point E. Finally, when the selected ratio of each rod is _OA:_OC:_BC:_AB:_AD:_DE = 19:99:93:68:85:60, and \(\angle\)EDA = 120\({}^{\circ}\), \(\angle\)DAB = 165\({}^{\circ}\), \(\angle\)MOC = 0\({}^{\circ}\), the movement trajectory of the point E can meet the transplanting requirements of the boat-bottom-shaped insertion method. The proportional parameters of the four-bar mechanism and the motion trajectory of point E are shown in Figure 5.  
Figure 5: Optimizing system interface for transplanting manipulator. (**a**) Interface for adjusting parameters; (**b**) interface showing movement trajectory.  
As shown in Figure 6, according to the obtained trajectory shape, combined with the agronomic requirements of the boat-bottom-shaped insertion method, we can select the appropriate ridge top position; the ratio of the planting depth c to the actual planting depth a in the trajectory is used as a reference to restore the four bars' actual value.  
The value of c measured from UI is 4.65 mm. For the actual planting depth of sweet potato seedlings a, the value range is 40-60 mm, and 50 mm is taken for theoretical calculation, so Equation (10) can be obtained.  
\[\frac{c}{a}=\frac{4.65}{50} \tag{10}\]  
According to the proportional value of Equation (10), the length of each rod can be obtained: \(OA\) = 204 mm, \(OC\) = 1065 mm, \(BC\) = 1000 mm, \(AB\) = 731 mm, \(AD\) = 914 mm, \(DE\) = 645 mm.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 4 Simulation Results
### The Spectral Function of NC-MUSIC Algorithm in the Simulation  
In order to intuitively show the practicability of the NC-MUSIC algorithm in this paper, we put the spectral function of NC-MUSIC algorithm. The spectral function of NC-MUSIC is compared with conventional MUSIC spectral function. The spectral function of NC-MUSIC in TR38.901 channel model is shown in Figure 10, and the simulation parameters in Table 9.  
As can be seen from the figures, when time delay estimation is carried out under NLOS channel, the conventional MUSIC algorithm has taken the non-direct path with the largest strength as time delay, while the direct path with lower strength is greatly submerged in the conventional MUSIC calculation method, which was difficult to be identified in the spectral function peak search process, so the conventional MUSIC algorithm has a large delay estimation error under NLOS channel. The NC-MUSIC algorithm proposed in this paper canceled the three non-direct paths of path2, path3, path4, so as to accurately captured the direct path with lower strength, and thus it has a better performance for time delay estimation in the NLOS channel.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 2 Language Models and Attention Mechanism
### Multihead Attention  
Instead of recurrent layers, transformers differ from sequence to sequence by using multihead attention layers; hence, they do not suffer from vanishing gradients problems that are related to the length of the sequences. Figure 2 highlights the multihead attention mechanism in transformers. In the multihead attention mechanism, a set of parallel self-attention layers are added, which are called heads.  
\[Z =MultiHead(Q,K,V)\] \[=Concat(z_{1},z_{2},\dots,z_{n})\ W^{0} \tag{5}\] \[z_{i} =attention(Q(W_{i})^{Q},K(W_{i})^{K},V(W_{i})^{V})\]  
As shown in Figure 2, the output of these heads is further concatenated to produce a single output. This multihead attention mechanism emulates the recurrence sequence effect but with attention. Each head uses different linear transformations to represent words, and therefore different heads can learn different relationships between words. The multihead attention mechanism executes the attention of the scaled dot product in parallel. The multihead model is therefore able to jointly attend to information from different representations at different positions over the projected versions of queries, keys, and values. As shown in Equation (5), these output values are then concatenated and weighted, where each head \(z_{i}\) is the attention function of query, key, and value with trainable parameters \((W_{i})^{Q},(W_{i})^{K},(W_{i})^{V}\).
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 2 Related Work
### Double-Delay Depth Deterministic Policy Gradient Algorithm  
Twin-delayed DDPG (TD3) improves the high variance problem of DDPG through the double Q network, target strategy smoothing, and delay strategy updating and improves the robustness of continuous control tasks. Its specific network structure, state space, and reward function will be described in detail in the fourth section. The key formulas and mechanisms are as follows.  
(1) Double Q network and minimum value selection.  
\[y_{i}=r_{i}+\gamma\cdot\min_{i=1,2}\!\!Q_{i}(s_{i+1},\mu_{\theta^{-}}(s_{i+1})+\varepsilon) \tag{12}\]  
where \(\varepsilon\sim clip(N(0,\sigma),-c,c)\) for the noise disturbance.  
(2) Target strategy smoothing.  
Clipping noise is added to the target action to suppress the Q value spike:  
\[a^{\prime}=\mu_{\theta^{-}}(s^{\prime})+clip(\varepsilon,-c,c),\varepsilon \sim N(0,\sigma) \tag{13}\]  
(3) Delay policy update.  
The actor network is updated after each update of the critic network.  
Under the background of non-vertical well trajectory design, this paper chooses the double-delay depth deterministic strategy gradient (TD3) algorithm.  
Firstly, TD3 is an offline strategy algorithm. For the actual oil and gas well drilling situation, in order to collect real-time new data for training, it may need to bear a high cost and waiting time. The offline policy attribute of TD3 gives it the ability to learn from the past experience stored in the replay buffer. The historical data obtained from the design or simulation of the past wellbore trajectory can be reused, which greatly reduces the need for continuous data collection during the design process.  
Secondly, TD3 is a deterministic algorithm. For wellbore trajectory design, a deterministic strategy is beneficial. In the case of a known wellbore state (such as wellbore position, dip angle, and geological conditions), if the strategy remains unchanged, the trajectory design curves given by deterministic strategies, such as TD3, are consistent. The consistency of wellbore trajectory design means that the strategy (and an engineer's assessment of the wellbore design process) can be better grasped when dealing with complex and high-risk drilling tasks.  
In addition, the TD3 algorithm itself has some very good features, such as delayed strategy update and truncated double Q learning, which are very suitable for the complexity of wellbore trajectory design. Delay policy update in TD3 helps to reduce the variance in policy updates, so as to achieve more stable and reliable learning [28]. Because of the existence of a large number of states and actions in wellbore trajectory design, the reward of single Q learning is easily overestimated in this process, and the truncated double Q learning mechanism effectively solves this problem.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 3 Research on RGB LEDs Dimming
### Simulation Analysis of Dimming
In summary, the light intensity on the receiving surface decreases as the distance increases, and distance has a significant impact on the light intensity on the receiving surface.
## 4 Results
This may save the expensive converter capacity and must be subjected to detailed economical investigation. To investigate further the frequency components causing the fluctuations, a frequency analysis of the output power was performed. Figure12(e) shows the result of a fast Fourier transform (FFT) analysis during the different power stages. Power is normalized to rated output for each stage. In general, the figure shows the expected characteristics with a decreasing level of distortion as power is aggregated. The peak of the spectrum coincides well with the wave period, and it can be seen how the farm has high attenuation at 0.1 Hz and significantly lower attenuation at 0.01 Hz in comparison with the WEC. The local minimum observed at 0.05 Hz on both the farm output and the array output is expected to be caused by low excitation in combination with good attenuation at this frequency. The frequency plot can serve as a powerful tool when optimizing the farm layout. As an illustration of the utilization of installed power capacity, Figure 12(f) shows the power/time distributions at the three farm levels. The plot is created by sorting all the points in Figure 12(d) in descending order. This plot is an effective tool for optimizing the the power components in the power transfer chain as it visualizes the energy that is produced on various power levels. For instance, it shows that both the array and the farm output have steep inclines close to the y-axis. The allowed peak power of the power transfer equipment can be drawn as horizontal lines on the plot. The area between output power line and the horizontal line will then represent lost power due to the transfer capacity deficit, while the area below the horizontal line represents the power that can be transferred. For instance, it can be seen directly from the figure that the array peak-to-average transfer capacity can be reduced from 2.5 to 2.0 with only a small energy sacrifice. As these curves currently represent only one wave state, they should be used with care. When a complete analysis is performed, these data serve as valuable inputs to the economical cost model. Thus far, the analysis has only been performed for the design wave state, and for the optimal wave direction. A complete analysis should be performed, taking into account all sea states and directions encountered throughout a year to find the annual produced energy with the given system configuration. The smoothing effects will be less efficient from unfavorable wave directions and will cause higher peak production than the installed capacity. This is not a problem in and of itself, but will force the WECs to hold back production and exporting less power due to the downstream restrictions. A thorough analysis with annual data will show the exact amount of power shedding and give a good basis for detailed scaling of the power components. However, based on experience from our previous work [13], and given the strong directionality at the Wavehub site, which is the basis of this work, it is believed that the current findings are realistic and only require minor adjustments after a comprehensive analysis.
## 2 Mid-Infrared Optical Parametric Oscillation Laser (OPO)  
The optical parametric oscillation laser (OPO) is one of the main ways to realize a mid-infrared laser output of 3-5 \(\upmu\)m and is composed of nonlinear crystal, a pump source and a resonant cavity, as shown in Figure 1. It can reach an output band that cannot be realized by traditional lasers and has many advantages, such as a wide tuning range, simple structure, high output power, narrow linewidth, etc. [3]. With the emergence of various nonlinear crystals, the optical parametric oscillator has achieved important breakthroughs and opened new application prospects, which has once again become a research hot spot of scholars in the world. According to the different nonlinear crystal materials, the mid-infrared laser based on optical parametric oscillation is classified as follows.
## 3 Results and Discussion
### Detection and Quantitation Limits (Sensitivity)  
LOD and LOQ were calculated based on the standard deviation from the AUP for the lowest concentration used in linearity studies as well as the slope of the calibration curve. The LOD and LOQ for Hum R for method A was 0.79 and 2.41 IU/mL, respectively, and for method B was 0.98 and 2.97 IU/mL, respectively. The results thus demonstrate the sensitivity of the two methods.
## 2 Materials and Methods  
### Sediments Collection  
The dredged sediments utilised in the experiment were collected manually (by shovel) in Hajek, Czech Republic (GPS 50\({}^{\circ}\)17\({}^{\prime}\)31.5\({}^{\prime\prime}\) N 12\({}^{\circ}\)53\({}^{\prime}\)35.2\({}^{\prime\prime}\) E). The sediments were complexly contaminated with trace elements (TEs) and hexachlorocyclohexane (HCH) isomers (\(\alpha\)-, \(\beta\)-, \(\gamma\)-, \(\delta\)-, and \(\varepsilon\)-). The contamination of the site originated from HCH production since the 1960s, primarily consisting of ballast isomers and chlorobenzenes [63]. Concentrations of HCH isomers and TEs in the DS are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Contaminant** & **MPC** & **Sediments** & **MAT** & **Biochar** \\ \hline \(\alpha\)- & & & \(18.1\pm 1.53\) & \(-\) & \(-\) \\ \(\beta\)- & & & \(102\pm 9.02\) & \(-\) & \(-\) \\ \(\gamma\)- & \(10.3\)\({}^{\text{a}}\) & \(59.6\pm 5.88\) & \(-\) & \(-\) \\ \(\delta\)- & & & \(283\pm 21.5\) & \(-\) & \(-\) \\ \(\varepsilon\)- & & & \(560\pm 41.0\) & \(-\) & \(-\) \\ \hline  & \(\Sigma\) & & \(1023\pm 56.8\) & \(-\) & \(-\) \\ \hline Mg & \(-\) & & \(40\),321 \(\pm\) 1546 & \(-\) & \(22\),147 \(\pm\) 1783 \\ Al & \(-\) & & \(90\),957 \(\pm\) 816 & \(-\) & \(42\),842 \(\pm\) 1057 \\ Si & \(-\) & & \(197\),636 \(\pm\) 966 & \(-\) & \(83\),524 \(\pm\) 979 \\ P & \(-\) & & \(11\),935 \(\pm\) 345 & \(-\) & \(176\),149 \(\pm\) 6165 \\ S & \(-\) & & \(382\pm 30.0\) & \(-\) & \(10\),257 \(\pm\) 12.5 \\ K & \(-\) & & \(12\),154 \(\pm\) 830 & \(-\) & \(13\),425 \(\pm\) 613 \\ Ca & \(-\) & & \(45\),115 \(\pm\) 944 & \(-\) & \(132\),669 \(\pm\) 4541 \\ Ti & \(-\) & & \(25\),713 \(\pm\) 510 & \(-\) & \(5253\) \(\pm\) 250 \\ Cr & \(200\) & \(459\pm 20.5\) & \(-\) & \(242\) \(\pm\) 9.00 \\ Mn & \(-\) & \(2888\pm 107\) & \(-\) & \(4039\) \(\pm\) 220 \\ Fe & \(-\) & & \(124\),914 \(\pm\) 468 & \(-\) & \(46\),049 \(\pm\) 2481 \\ Cu & \(100\) & \(152\pm 9.50\) & \(143\)–6000 & \(763\) \(\pm\) 37.5 \\ Zn & \(300\) & \(131\pm 7.00\) & \(416\)–7400 & \(3616\) \(\pm\) 287 \\ Rb & \(-\) & \(181\pm 6.00\) & \(-\) & \(43.6\)\(\pm\) 1.12 \\ Sr & \(-\) & \(400\pm 8.00\) & \(400\) & \(830\) \(\pm\) 60.0 \\ Zr & \(-\) & \(378\pm 6.00\) & \(-\) & \(143\) \(\pm\) 2.52 \\ Nb & \(-\) & \(137\pm 4.51\) & \(-\) & \(-\) \\ Pb & \(100\) & \(202\pm 15.0\) & \(121\)–\(300\) & \(103\) \(\pm\) 5.65 \\ \hline \hline \end{tabular} Note: MPC—Maximum Permissible Concentrations [13]; MAT—maximum allowable thresholds [64]; \({}^{\star}\)—MPC for Benthic community (reshwater sediment) [65].  
\end{table}
Table 1: Initial concentrations of HCH isomers (\(\mu\)g kg\({}^{-1}\)) and TEs (mg kg\({}^{-1}\)) in DS and biochar.
# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 3 Results and Discussion  
### Evaluation of the CNN Architecture  
Because of the complexity and parametric variation existing in the CNN models, it was not feasible to perform all possible models with their parameters, e.g., kernel size, the number of convolutional layers. Therefore, six representative CNN models with an increasing number of convolutional layers were compared to find the optimal network. The experimental results of the six CNN frameworks are tabulated in Table 3. It was shown that by increasing the depth and the number of kernels of the network, the validation accuracy changed from 74.6% to 83.8% (from Model 1 to Model 6). The accuracy and loss plots for Model 6 in Figure 4 suggested the overfitting, in which the model fit better on the training dataset than on the validation dataset. Considering the validation accuracy, the following data augmentation operations were conducted with the network used in Models 3-6.