esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### SCT Framework
#### 3.4.4 Loss Function and Assigning Labels  
Learning the label assignment techniques adjust the network parameters leading iteratively to an optimum position on the error surface. The loss function influences the detection results during the object detection phase. The Single Shot-multibox Detector (SSD) matching technique is used for the ground truth bounding box. The matching result is stored in matrix \"\(S\)\". If \(G_{t}\) represents the bounding box from ground-truth on \(F_{t}\) for \(i\)th chained anchor \(A_{i}\), checked by: (IoU ratio > threshold \(t_{p}\)), then we have \(S_{ij}=1\). If the smaller threshold is represented by \(t_{n}\), so that: (IoU ratio \(<t_{n}\)), then \(S_{ij}=0\). Depending on the value of \(S\), the ground-truth label (\(c_{cls}^{i}\)) is assigned to the SCT classification branch for \(A_{i}\) as given by:  
\[c_{cls}^{i}=\left\{\begin{array}{ll}1,&if\ \sum_{j=1}^{k_{t}}S_{ij}=1,\\ 0,&if\ \sum_{j=1}^{k_{t}}S_{ij}=0,\end{array}\right. \tag{1}\]  
where \(k_{t}\) represents the cardinality of the bounding boxes for the ground-truth frame. The ID verification branch is assigned the ground-truth label for \(A_{i}\), the predicted pair and the corresponding ground-truth (bounding boxes) are (\(B_{t}^{i},\ \beta_{t}^{i}\)) and (\(GB_{t}^{j},\ GB_{t+1}^{k}\)) respectively, as given by the kernel function:  
\[c_{ID}^{i}=\left\{\begin{array}{ll}1,&if\ c_{cls}^{i}=1\ \text{and}\ I \big{(}GB_{t}^{i}\big{)}=I\ (\ GB_{t+1}^{k}),\\ 0,&\text{otherwise},\end{array}\right. \tag{2}\]  
where \(I[\cdot]\) denotes the target identity in the bounding box. The loss function is given by:  
\[L_{all}=\sum_{t,i}[L_{reg}\Big{(}\Delta_{d}^{t,i},\ \ \Delta_{d}^{t+1,i},\ \ \Delta_{g}^{t,j},\ \ \Delta_{g}^{t+1,k}\Big{)}+\alpha\mathcal{F}\Big{(}\mathcal{P}_{cls}^{i},\ c_{ cls}^{i}\Big{)}+\beta\mathcal{F}(\mathcal{P}_{id}^{i},\ c_{id}^{i})], \tag{3}\]  
where the focal losses, given by \(\mathcal{F}\big{(}\mathcal{P}_{id}^{i},\ c_{id}^{i}\big{)}]\) and \(\mathcal{F}\big{(}\mathcal{P}_{cls}^{i},\ c_{cls}^{i}\big{)}\)[54], are used for both branches of classification and ID verification (particularly for alleviating class imbalance problems), respectively. Moreover, \(\mathcal{P}_{id}^{i}\) and \(\mathcal{P}_{cls}^{i}\) denote the prediction score or confidence level; where \(\alpha\) and \(\beta\) are the weighting factors, \(\Delta_{d}^{t,j},\ \ \Delta_{d}^{t+1,i}\) denote the bounding box offset generated by Faster R-CNN [6], and \(\ \Delta_{g}^{t,j},\ \ \Delta_{g}^{t+1,k}\) denote the offsets for the ground-truths.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Fuzzy Logic-Based Safe Energy Level Determination
#### 4.4.4 Defuzzification  
After the _Inference Engine_ determines the output's membership degree on fuzzy sets (by using Equation (9)), the fuzzy set with the highest membership degree is considered the output variable. Finally, we utilize the \(CoG\) function to calculate the crisp value of the output's fuzzy set.  
Let us consider an example where \(L_{\tau}=3,E_{min}=3\), and \(E_{th}=4\). First, the fuzzy inputs of the two variables \(L_{\tau}\) and \(E_{min}\) are calculated using Equations (7) and (8), as presented in Table 5. Then, we use Equation (9) to calculate the output membership degree of each fuzzy rule. For example, the fuzzy output for rule \(R_{1}\) is given by  
\[\mu_{R_{1}} = \min(\mu_{L}(L_{\tau}),\mu_{L}(E_{min}))\] \[= \min(0,0)=0.\]  
The values of all the fuzzy rules are shown in Table 6. From Table 6, it can be seen that rule 3 achieves the highest membership degree. Therefore, the output variable \(\theta\) obtains the linguistic value of _low(L)_. Finally, we use the \(CoG\) function to convert the output's linguistic variable into a crisp value. According to Equation (3), the \(CoG\) of the output linguistic variable _low(L)_ is defined by  
\[CoG_{L}=\frac{\int_{0}^{\delta}\theta\mu_{L}(\theta)\mathrm{d}\theta}{\int_{0 }^{\delta}\mu_{L}(\theta)\mathrm{d}\theta}, \tag{10}\]  
where \(\mu_{L}\) is the membership function of _low(L)_. Following the definition of the output membership function represented in Table 3, we have  
\[\mu_{L}(\theta)=\left\{\begin{matrix}\frac{3}{\delta}\theta&,&0\leq\theta\leq \frac{1}{3}\delta\\ 2-\frac{3}{\delta}\theta&,&\frac{1}{3}\delta\leq\theta\leq\frac{2}{3}\delta\\ 0&,&\frac{3}{3}\delta\leq\theta\leq\delta.\end{matrix}\right. \tag{11}\]  
where \(\delta=\left(0.1-2\frac{E_{th}}{E_{max}}\right)\). By substituting Equation (10) into Equation (11), we obtain the value of \(CoG_{M}\) as \(\frac{1}{3}\delta\). It means that with \(L_{\tau}=3,E_{min}=3\), and \(E_{th}=4\), the value of \(\theta\) is \(\frac{1}{3}\delta\), thus the _safe energy level_\(E_{sf}\) is given by \(E_{sf}=E_{th}+\frac{1}{3}\delta E_{max}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(R_{\#}\) & \(\mu_{R_{\#}}\) & \(R_{\#}\) & \(\mu_{R_{\#}}\) & \(R_{\#}\) & \(\mu_{R_{\#}}\) \\ \hline
1 & 0.00 & 4 & 0.00 & 7 & 0.00 \\ \hline
2 & 0.00 & 5 & 0.00 & 8 & 0.00 \\ \hline
3 & 0.75 & 6 & 0.25 & 9 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fuzzy rules evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variable** & **Membership Function** & **Value** \\ \hline \multirow{3}{*}{\(L_{r}\)} & \(\mu_{L}\) & 0.75 \\ \cline{2-3}  & \(\mu_{M}\) & 0.25 \\ \cline{2-3}  & \(\mu_{H}\) & 0.00 \\ \hline \multirow{3}{*}{\(E_{min}\)} & \(\mu_{L}\) & 0.00 \\ \cline{2-3}  & \(\mu_{M}\) & 0.00 \\ \cline{1-1} \cline{2-3}  & \(\mu_{H}\) & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inputs of linguistic variables.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 5 Conclusions  
In the paper, we come up with a new method to settle the problem of recognition of FEC coding types in non-cooperative systems without any prior knowledge. Based on improved RNN, the method proposed can realize end-to-end feature extraction and error-correcting code type recognition. Firstly, datasets of communication signals are generated by simulating channel environment of the wireless communication system. Then, in order to improve the prediction accuracy, the performance and robustness of the proposed network are evaluated under different signal-to-noise ratios and different types  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Run Time(s)** \\ \hline Improved RNN & 14.1 \\ Simple RNN & 11.4 \\ MLP & 1.8 \\ Typical CNN & 5.8 \\ CNN\_SVM & 15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average run time of different networks.  
Figure 10: Performance comparison of the improved RNN with others.  
of FEC codes. Finally, we compare the performance of the improved RNN in different code lengths and sizes, the results shows when the code lengths \(L\geq 1024\) or sizes \(S\geq 128k\), the identification accuracy will exceed 90%, which ensures saturation of accuracy and complexity in actual code type recognition. Compared with the identification accuracy and complexity, we finally conclude that improved RNN is in line with the requirements of engineering practice under the condition of non-cooperative communication and has a promising future in recognition of FEC codes.  
Conceptualization, F.M. and Y.L.; methodology, F.M.; software, F.M.; data curation, F.M. and H.C.; writing--original draft preparation, F.M.; visualization, F.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to express their thanks to [PERSON] from the National University of Defense Technology for her valuable comments on this paper.  
The authors declare no conflict of interest.
## 2 MEMS Sensor-Based Localization Using the Data and Model Dual-Driven Structure  
MEMS sensor-based localization can provide relatively precise short-term positioning performance, while the performance of the existing PDR or the INS mechanizations is constrained by the cumulative error, low sampling rate of the step-length calculation, and the time delay of the location update. This section presents a data and model dual-driven structure for MEMS sensor-based indoor localization, which combines the deep learning-based speed estimator (DLSE) with INS mechanization, and the fused results are further integrated by the AUKF with the Wi-Fi and acoustic-based location sources for the accurate positioning. The overall structure of H-IPS is described in Figure 1.
# High-Power, High-Beam-Quality, Long-Pulse-Width 532 nm Laser Based on a 4f Optical System
## 4 Results and Discussion
M\({}^{2}\) is used to evaluate beam quality, which is defined as the ratio of the product of beam parameters to the product of beam parameters of the fundamental mode Gaussian beam:  
\[\text{M}^{2}=\frac{\pi\text{D}_{0}\theta}{4\lambda} \tag{7}\]  
In the formula, D\({}_{0}\) is the waist width, \(\theta\) is the beam divergence angle, and \(\lambda\) is the wavelength of the laser. Using the beam quality analyzer (M2 DU-200 Stage) (Redding, CA, USA) from DataRay, when the short-cavity 532 nm laser outputs 110 W, its beam profile and quality are shown in Figure 6. The blue and red curves represent the beam quality in the X and Y directions, respectively. The beam quality factors are M\({}_{\text{x}}^{2}\) = 12.62 in the horizontal direction and M\({}_{\text{y}}^{2}\) = 14.67 in the vertical direction. For the 532 nm laser with the 4f optical system extended cavity, with an output power of 108 W, the beam profile and quality are shown in Figure 7. The laser beam is nearly circular with a typical Gaussian intensity distribution. At 108 W output power, the beam quality factors are M\({}_{\text{x}}^{2}\) = 15.76 in the horizontal direction and M\({}_{\text{y}}^{2}\) = 14.25 in the vertical direction. The decline in beam quality for the 532 nm laser is attributed to aberrations introduced by the two spherical mirrors and the misalignment of the image plane (M\({}_{1}\)\({}^{\prime}\)). Figure 5: The pulse width of the short-cavity 532 nm laser and the 4f extended-cavity laser under different pump currents. Figure 6: The beam profile and beam quality of the short-cavity 532 nm laser at 110 W power (The blue and red curves represent the beam quality in the X and Y directions, respectively).
## 4 Summary and Discussion  
Two main questions were posed in the introduction:  
1. How accurate can grain size be determined from LUS simulations on various simulated microstructures and temperatures?
2. How accurate can the phase composition be determined from LUS simulations on various multiphase media and temperatures?  
Both questions were dealt with, and the results are summarized here. Consequently, the question has been raised how well this can measured in practice, giving uncertainties in sample thickness and temperature fluctuations.  
All simulations in Table 2 were performed at four different temperatures to see the impact of temperature on the results. As has been noticed in the previous section, temperature influences the elasticity matrix and density of a sample, and hence has an impact on the wave velocity and the amount of scattering (via grain contrast).  
Two parameters were determined from the individual simulations: pressure wave velocity and frequency-dependent attenuation. Both are affected in a different way by the sample characteristics. An overview is given in Table 3, where the impact of grain size and phase fraction on the measurable velocity and on the attenuation is listed.  
As mentioned, the temperature \(T\) and the sample thickness \(H\) should be known sufficiently accurately as well, since they have an impact on the wave velocity and attenuation as well. This leads to the list of cross-dependencies, as shown in Table 4. The values listed in the table lead to velocity or attenuation changes that are in the same order as caused by grain size variations of 1 \(\upmu\)m or phase fraction variations of 10%, thus showing that no quantifiable dependency can be given for the sample thickness in case of the attenuation. Note that these dependencies scale linearly with the desired accuracy of the grain size and phase fractions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Characteristic** & \
\begin{tabular}{c} **Impact on the Pressure Wave** \\ **Velocity** \\ \end{tabular} &
\begin{tabular}{c} **Impact on the Frequency** \\ **Dependent Attenuation** \\ \end{tabular} \\ \hline
**Microstructure** & \begin{tabular}{c} Austenite: velocity decrease \\ of 1.6 m/s per \(\upmu\)m. \\ \end{tabular} & \begin{tabular}{c} Austenite: attenuation \\ increase \\ of 0.03 per \(\upmu\)m. \\ \end{tabular} \\ _Austenite: 15 \(\upmu\)m_ & \
\begin{tabular}{c} Ferrite: velocity decrease \\ of 1.1 m/s per \(\upmu\)m. \\ \end{tabular} &
\begin{tabular}{c} Ferrite: attenuation increase \\ of 0.011 per \(\upmu\)m. \\ \end{tabular} \\ \hline
**Phase volume fraction** & \
\begin{tabular}{c} Velocity increase of 37 m/s \\ per 10 percent increase of the \\ martensite fraction. \\ \end{tabular} &
\begin{tabular}{c} Attenuation increase of 0.02 per 10 percent increase of the \\ martensite fraction. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: An overview of the influence of sample characteristics on measurable properties.
# A Survey on Hardware Accelerators for Large Language Models
## 5 ASIC Accelerators
### Energon  
In 2023, [PERSON] et al. presented an algorithm-architecture co-design approach that accelerates various Transformers using dynamic sparse attention, called Energon [30]. Energon proposes a mix-precision multi-round filtering (MP-MRF) algorithm to dynamically identify query-key pairs at runtime.  
Energon adopts a low bit-width in each filtering round and only the finally selected pairs are used for high-precision tensors in the attention stage to reduce overall complexity. Thus, they manage to reduce computation costs by 4\(\times\) to 8\(\times\) with negligible accuracy loss.  
Energon is implemented as a co-processor using a 45 nm ASIC library. Based on the performance evaluation, Energon achieved speedups of 168\(\times\) and 8.7\(\times\), and energy reductions of up to 10,000\(\times\) and 1,000\(\times\) over the Intel Xeon 5220 CPU and NVIDIA V100 GPU, respectively.
# AI-Driven Waste Management in Innovating Space Exploration
## 5 Challenges and Considerations  
### Practical Constraints and Implementation Realities  
Practical limitations identified from ground-based and ISS experimental data include significant energy requirements for plasma arc gasification, necessitating enhanced solar arrays or nuclear energy, the latter carrying inherent safety risks demanding rigorous containment measures. Al-driven waste sorting, while promising, faces computational limitations in space environments due to restricted onboard computational resources, as evidenced by ISS-based testing and ground simulations. Although highly efficient in laboratory conditions, bioreactor systems encountered microbial containment challenges and fluctuating processing efficiencies under microgravity conditions aboard the ISS, highlighting the need for optimised sterilisation and containment solutions [77, 78]. Future technological designs must address these operational limitations through modular and energy-efficient designs to ensure practical implementation in deep-space missions. (Table 5) (Figure 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Efficiency Metrics of Current vs. Enhanced Recycling Systems**} \\ \hline
**System** & **Current Recycling Efficiency (\%)** & **Enhanced Recycling Efficiency (\%)** \\ \hline UN Office for Outer Space Affairs & Long-term sustainability of outer space activities & Sustainable Space Exploration \\ \hline \multicolumn{3}{c}{Inventory of Waste Management Equipment on the ISS} \\ \hline Equipment & Manufacturer & Capacity \\ \hline Closed-Loop System & Boeing (Washington, DC, USA) & Processes 6 kg/day \\ \hline O\({}_{2}\) Generation System & Airbus (Blagnac, France) & 2 kg O\({}_{2}\)/day \\ \hline Urine Processor Assembly & Lockheed Martin (Bethesda, MD, USA) & 1.5 L/day \\ \hline Solid Waste Compactor & Thales Alenia Space (Cannes, France) & 50 kg of waste \\ \hline Water Recovery System & Hamilton Sundstrand (Windsor Locks, CT, USA) & 6 L/day \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 5: The recycling efficiency of the current system is in line with the proposed systems.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 5 HD Maps
### HD Map Layers  
Based on the available information, the AECC has produced preliminary estimates of the operation of HD maps and the overall network occupancy that occurs when a vehicle contributes data to HD maps.  
The AECC divided the different layers of HD maps into dynamic and static (permanent). Static maps represent today's famous maps of the territory. We divide dynamic maps into four categories, based on updating information according to the time interval (Figure 7) [28].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Method of Collection**} & \multicolumn{1}{c}{**Character**} \\ \cline{2-7}  & & & & & \\
**Domain** & & & & & & \\
**Communication of V2I** & & & & & & \\
**Intelligent infrastructure** & & & & & & \\ Intelligent systems & & & & & & \\ Road infrastructure data & & & & & & \\ Communication of I2V & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._These are:  
* The Permanent Static Layer contains information that has the lowest data frequency of updates of the order of days and more. The types of data that this layer represents are, for example, information about intersections or traffic signs. We can also mark this layer as a static map.
* The Transient Static Layer updates data on an hourly basis. The layer includes, for example, the place of work on the road, accidents, road closures and others.
* In the Transient Dynamic Layer, the information is changed in units of minutes. The layer includes weather data, illegally parked cars, dirt on road infrastructure and so on.
* The Highly Dynamic Layer is the most frequent layer of all dynamic layers. The data are collected after a few seconds and contain information about vehicles, pedestrians, or cyclists. The AECC does not deal with information that is collected in less time than the seconds required for autonomous vehicle driving. [28].
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 5 Numerical Experiment and Discussions
### Time Complexity of Our Proposed Algorithm  
In our proposed approach, we have \(O_{c}\) clusters and \(N_{c}\) nodes in each cluster; the algorithm necessitates around \(\Theta(O_{c})\) operations to access \(O_{c}\) clusters. Determining the reliability of each cluster takes approximately \(\Theta(N_{c}{}^{2})\) time. Additionally, the algorithm involves constant time operations for calculating residual energy, distance from the sink, number of neighbors, average distance of cluster nodes, and the distance ratio denoted as \(\hat{\sigma}\). Thus, the overall time complexity of the algorithm can be approximated as \(\Theta(O_{c}N_{c}{}^{2}+\hat{\sigma})\) or simplified as \(\Theta(O_{c}N_{c}{}^{2})\).
## 3 Proposed Framework
### Data Pre-Processing
#### 3.2.2 TF-IDF (Term Frequency--Inverse Document Frequency)  
In this paper, TF-IDF was applied to transform the text into numerical feature vectors which are then considered as input to supervised machine learning algorithms. We used TF-IDF at the word level. The reason for choosing the TF-IDF method is the nature of our data and framework as we needed word/feature relevancy and importance to apply our regression algorithms later on to check the propagation scale, so TF-IDF was better suited to reflect the importance of words in the entire corpus and in our context.  
Equation of TF:  
\(tf(t,d)=\) no. of times term t appears in a document d/total no. of terms t in a document d,  
The inverse document frequency measures how important a word is in a whole document. The equation of IDF:  
\[idf\ (t)=log\ (N\ /\ (df\ +\ 1)) \tag{2}\]  
The Tf-idf combined equation would be:  
\[tf-idf\ (t,\ d)=tf\ (t,\ d)\ *\ log\ (N\ /\ (\ df\ +\ 1)) \tag{3}\]  
The parameters and symbols would be as follows:  
df = document frequency;  
tf = term frequency; idf = inverse document frequency;  
N = total number of documents;  
df + 1 = docs containing terms;
## 6 Conclusions  
This work presents a comparison between two widely studied methods for alleviating dynamic loads caused by gusts. The first method is entirely passive, using a flared folding wing tip (FWT), while the second involves an active controller based on a static output feedback (SOF) scheme. Both approaches effectively reduce the wing root bending moment (WRBM) of about 15%, with the SOF also reducing the wing root torsional moment (WRTM) of 13%. However, a drawback of FWT is its reduced damping within the flight envelope, which, while maintaining aircraft stability, could pose greater challenges for fatigue. An advanced hybrid finite element (FE) aeroelastic model, in which the wing was replaced by a detailed 3D wingbox, was used to analyze the structural response in terms of failure indices (FIs). The FIs were found to be lower for the FWT configuration (\(-17\%\)), but the reduced aeroelastic damping led to a low-damped oscillatory response. This issue must be addressed from the early design phases, considering both fatigue life and load reduction.  
Although the two load alleviation systems provide nearly the same load reduction for the wing root bending moment (WRBM), other factors must be considered. The impact on fatigue has already been highlighted, and it could be mitigated by incorporating a damper in the folding wing tip (FWT) hinge. While it is true that FWT allows for an increased wingspan without requiring changes to the aircraft's aerodrome reference code (ARC), it also necessitates a dedicated system for sensing, actuation, and folding the wing, as well as releasing it during gusts and restoring its flight shape afterward. On the other hand, active gust load alleviation (GLA) can use conventional wing architecture and sensors to achieve a similar load reduction. However, the wingspan increase is constrained by the aircraft's ARC, and modifying this for ground operations is not as straightforward. All these aspects must be carefully considered during the conceptual design phase to determine the most cost-effective and practical solution.  
This research was funded by Grant Agreement number: 886552-H2020-CS2-CFP10-2019-01.  
**Institutional Review Board Statement:** Not applicable.  
Figure 19: Bottom skins fibre direction stress in ply 1 comparison between CNT (**left**) and FWT (**right**). t = 0.45 s, H = 58 m, z = 8000 m, \(V_{TAS}=229\) m/s, stress in [Pa].  
Figure 18: Top skins fibre direction stress in ply 1 comparison between CNT (**left**) and FWT (**right**). t = 0.45 s, H = 58 m, z = 8000 m, \(V_{TAS}=229\) m/s, stress in [Pa].