esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
Drones, up to now, are still missing a complete ER-ID system for identifying and locating themselves anytime and anywhere. The results of this study showed the ability of applying FSA for increasing the range of drone detection while reducing the consumed energy in their platforms. This increases flight security and decreases flight accidents. When aiming to maximize the ER-ID performance in terms of range, it was impossible to neglect the effect of consumed energy on the drone's platform. The detection scenario is  
Figure 4: Drone path scenario of case study.  
based on electronic identification against the potential combinations of all the available RF-CSs. These RF-CSs include Wi-Fi, Bluetooth, IoT, 4G/5G, ADS-B and satellite communications. The FSA manages the detection tasks such that it handles the connection within the appropriate RF-CS and establishes an RF communication between the drone and the local aviation authority to transmit the electronic remote identification. The FSA has the functionality to choose the best energy efficient RF communication system available, which helps in minimizing the consumed energy in drones. The problem of deterioration, due to fluctuation when more than one RF-CS signal is available in the same area, is solved by the developed FSA algorithm. In turn, the flight time of drones has been dramatically increased. As a future work, it is recommended to practically implement the developed FSA of this study. The results of this study also can be expanded to airplanes and civilian aircraft.  
Conceptualization, I.A.-D. and C.K.; methodology, G.T.; software, C.K.; validation, P.P., M.J.M. and P.D.; formal analysis, P.D., I.A.-D., P.P. and M.J.M.; investigation, C.K., I.A.-D. and G.T.; resources, P.D. and P.P.; data curation, M.J.M.; writing--original draft preparation, C.K., I.A.-D.; writing--review and editing, G.T. and I.A.-D.; visualization, C.K.; supervision, P.D. and P.P.; project administration, P.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding but the publication expenses were covered by MDPI.  
Not applicable.  
The authors declare no conflict of interest.
## Data and Methods
### Methods
For station-scale comparison, the GPPs were compared with in-situ precipitation data at the locations of each station, for this purpose the nearest pixel to the station was derived and compared. For descriptive statistics, commonly used error metrics were applied: correlation (R),bias, root mean-square difference (RMSD) and the ratio of standard deviation (STD). These statistical metrics were determined as follows:  
\[R=\frac{\sum_{i=1}^{n}(Station_{(i)}-\overline{Station_{(i)}})\ ( Gridded_{(i)}-\overline{Gridded(i)})}{\sqrt{\sum_{i=1}^{n}(Station_{(i)}- \overline{Station_{(i)}})^{2}\sum_{i=1}^{n}(Gridded_{(i)}-\overline{Gridded(i)} )^{2}}} \tag{1}\]  
\[Bias=\overline{(Station_{(l)}-\overline{Gridded_{(l)}})} \tag{2}\]  
\[RMSD=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}Gridded_{(i)}-Station_{(l)} \Bigr{)}^{2}} \tag{3}\]  
\[Ratio\ of\ Standard\ Deviation\ (STD)=\frac{\sigma_{gridded}}{\sigma_{Station}} \tag{4}\]  
In the above equations, 'gridded' and'station' represent the precipitation products from the different GPPs and in-situ data, respectively, while 'n' is the number of observations. The correlation (R) (Equation (1)) was used to quantify the scale of linear relationship between station and GPPs. Bias (Equation (2)) was used to show the average direction of the centered deviation of GPPs from in-situ observations; thus, a positive value implied that the GPPs underestimated the in-situ precipitation and a negative value implies an overestimation of precipitation. RMSD (Equation (3)) was used to estimate the mean magnitude of the differences between the GPPs and in-situ precipitation products, while STD (Equation (4)) was used to assess the relative variation and differences in the amplitude of GPPs and in-situ observed precipitation distribution.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 4 Tasking Offloading Algorithm Based on DRL
### DRL-Based Task Offloading
```
0: System model parameters, \(\lambda_{m}(t)\), \(\varphi_{m}^{loc}(t)\), \(\varphi_{m,n}^{trans}(t)\), \(d_{m,n}^{edge}(t)\), \(H(t)\) Initialization: trainable networks--\(\theta\); target networks--\(\tilde{\theta}\); replay memory capacity--\(R_{B}\); batch size--B. for each episode\(k=\{1,2,\dots,k_{max}\}\)do  Reset task-offloading environment  Generate an initial state \(s_{0}\) for each user\(m\in\{1,2,\dots,M\}\)do  Select an action \(a_{t}\) with the function: \(\arg\max\{v_{\tilde{\varphi}(s^{\prime},a^{\prime})}+m_{\tilde{w}}(s^{\prime},a^{\prime})Z+\beta_{1}v_{\overline{\varphi}(s^{\prime},a^{\prime})}+\beta_{2} m_{\overline{\varphi}}(s^{\prime},a^{\prime})Z\}\)  Execute action \(a_{t}\) and calculate the reward \(r_{t}\)  Save sample data \((s_{t},a_{t},r_{t},s_{t+1},done)\) in the experience replay buffer  Train the eval networks and periodically update the target networks with (13)-(14)  end for  end for Output: Optimal policy \(\pi^{*}\)
```  
**Algorithm 1** DRL-based solution for task-offloading decision in MEC system
## 4 Design and Optimization of Hardware Prototype System
### Hardware Prototype Optimization  
Different from the deeply-coupled system implemented in software receivers, the hardware prototype optimization copes with embedded software architecture, synchronization of IMU and GNSS data sampling and INS aiding information delay.
## 4 Numerical Simulation of Soliton Molecules
### In-Phase Two-Soliton Bound States  
In-phase soliton molecules can be produced when the initial noise pulses are set in phase, with evolution process shown in Figure 18a. This state exhibits a symmetrical optical spectrum with respect to the central peak (Figure 18b), implying generation of two identical in-phase solitons. The temporal trace in Figure 18c further verifies the in-phase feature. Interestingly, the peaks of Kelly sidebands (marked by green arrows in Figure 18b) coincide perfectly with the peaks of the modulated fringes, suggesting a vital role of dispersive waves in the formation of bound solitons. The interaction length of dispersive waves can be longer than direct interaction by profile overlap in comparison to Figure 16, so that the RSW can be larger too. From the simulation results, we know that the pulse separation is 27.2 times pulse duration, indeed concluding one order of magnitude larger RSW than the case in Figure 16. In addition, the variation of initial phase difference mainly leads to the change of pulse separation, with pulse width nearly constant (0.15 ps here). Similarly, if we set the initial pulse separation to be 1 ps, another in-phase bound state can be achieved, in which the RSW is decreased to 22.7 and the pulse duration is again 0.15 ps (not shown here). These numerical results indicate that the initial conditions affect pulse-pulse interaction more intensively than individual pulses in such a dissipative system.  
Figure 16: Formation of an out-of-phase bound state and its spectral and temporal properties. (**a**) Evolution process of pulses driven by initial noise; (**b**) Optical spectrum and (**c**) temporal profile of the soliton molecules.  
Figure 17: Formation of another out-of-phase bound state initiated by different noise compared to Figure 16.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 2 Proposed RMLP-18T Cell and Its Operations
### Memory Operation of the Proposed RHLP-18T Cell
#### 2.1.2 Read Mode  
For a read operation, both bit lines (BL and BLB) are typically precharged to VDD. Subsequently, both WL and WWL are driven to VDD, which activates the NMOS access transistors N5 and N6 but keeps the PMOS write-assist transistors P11 and P12 deactivated. When the cell stores a logic '1' (Q = 1, QB = 0), bit line BL, connected to node Q via N6, remains high. This state is primarily maintained by the active pull-up transistor P4 (see Figure 1). Simultaneously, bit line BLB, connected to node QB via N5, begins to discharge toward ground. This discharge occurs through the conductive path formed by transistors N5, N1, and N3, which are active when Q = 1. The resulting voltage difference between BL and BLB is detected by a connected sense amplifier to read the stored data.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 2 Related Work
### Clustering-Based Routing Protocol  
In order to improve the performance of routing protocols, some scholars propose clustering-based routing protocols. Cluster is a kind of network technology that groups a large number of nodes into sub-networks. On the one hand, clustering can better adapt to the VANET environment, on the other hand, clustering is easy to manage the nodes in the cluster, and has certain advantages in routing selection, bandwidth allocation, and channel access. There are two hotspots in cluster-based routing protocol research: firstly, how to cluster the vehicle nodes more effectively; secondly, how to construct reliable and low delay routing links by clustering. [PERSON] et al. [20] design a clustering algorithm according to the distance and density of nodes. Due to the inherent characteristics of continuous change of moving object position, clustering results will change dynamically. By analyzing the characteristics of urban street network, the concept of cluster block (CB) is introduced as the bottom cluster unit. In order to achieve high-quality multimedia transmission, [PERSON] et al. [21] propose a user-oriented VANET multimedia transmission solution based on clustering, which can transmit multimedia content according to passengers' preferences. Merely discussing how to form more effective clusters does not ultimately solve the data transmission problem in VANET. In addition, some researchers have studied how to build routes. [PERSON] et al. [22] propose a stable routing method based on passive clustering. Considering the mobility of nodes, this clustering method only assigns tasks to vehicles that need to communicate. Due to the characteristics of high-speed motion of nodes in VANET, [PERSON] et al. [23] propose a clustering method based on moving motion regions. When forwarding data, a route is constructed through cluster edge nodes to surrounding cluster head nodes.
_Appl. Sci._**2012**, \(2\), 220-232; doi:10.3390/app2010220  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**2D Spin-Dependent Diffraction of Electrons From Periodical Chains of Nanomagnets**  
**[PERSON] and [PERSON] *  
Department of Physics, Addis Ababa University, P. O. Box 1176, Addis Ababa, Ethiopia;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +251-11-1223931; Fax: +251-11-1223931.  
_Received: 15 January 2012; in revised form: 27 February 2012 / Accepted: 27 February 2012 / Published: 6 March 2012_  
**Abstract:** The scattering of the unpolarized beams of electrons by nanomagnets in the vicinity of some scattering angles leads to complete spin polarized electrons. This result is obtained with the help of the perturbation theory. The dipole-dipole interaction between the magnetic moment of the nanomagnet and the magnetic moment of electron is treated as perturbation. This interaction is not spherically symmetric. Rather it depends on the electron spin variables. It in turn results in spinor character of the scattering amplitudes. Due to the smallness of the magnetic interactions, the scattering length of this process is very small to be proved experimentally. To enhance the relevant scattering lengths, we considered the diffraction of unpolarized beams of electrons by linear chains of nanomagnets. By tuning the distance between the scatterers it is possible to obtain the diffraction maximum of the scattered electrons at scattering angles which corresponds to complete spin polarization of electrons. It is shown that the total differential scattering length is proportional to \(N^{2}\) (\(N\) is a number of scatterers). Even small number of nanomagnets in the chain helps to obtain experimentally visible enhancement of spin polarization of the scattered electrons.  
**Keywords:** scattering length; un-polarized beams; probability; nanomagnets; scattering amplitude  
**Classification: PACS** 75.50.-Xx, 75.75.-c
## 3 Experimental Data and Processing Strategies  
The interpolation accuracy of the new model was examined for four different application scenarios: a medium-scale reference network with an average distance of 41 km, a large-scale reference network with an average distance of 98 km, out-of-network users, and a network with a typical minimum of three reference stations. In addition, two typical scenarios of medium-scale and large-scale reference networks were selected to test and analyze the PPP-RTK positioning performance. Details of the data processing strategies are shown in Table 1. Both USM and DSM select the satellite with the highest elevation angle as the reference satellite to calculate the interpolation coefficient. The code and carrier observations were still modeled randomly based on the elevation angle at the service and user end. The empirical value was used to determine the weight of tropospheric zenith delay and ionospheric delay in the test [24; 25].  
the user station and the reference stations in the reference network are in the range of 90-105 km, with a maximum distance of 103.2 km and an average distance of 98 km. This was considered scenario B. The ncta/ncec/ncego/ncwl stations in Figure 1 were selected as reference stations, and the ncsf station was selected as the user station to analyze the interpolation accuracy when the user was outside the network. The average distance from the user station to a reference station was 58.5 km. This was considered scenario C. With the ncta/ncec/ncef stations in Figure 1a selected as reference stations, the ncwl station was selected as the user station to analyze the interpolation accuracy when there were three reference stations. The average distance from the user station to a reference station was 43.5 km. This was considered scenario D. The data collection time interval was UTC 1:00-23:30 on 21 December 2021, with an epoch interval of 5 s.
# Human Annotated Dialogues Dataset for Natural Conversational Agents
## 6 Empirical Results  
The preliminary results of HUMOD dataset with existing supervised and word-overlap metrics is shown in Table 3.  
We provided the benchmark results for the overall correlation of human judgment with different models. Although supervised models are correlated up to some degree, it is still far from applicable to use in dialogue reply scoring as widely as translation scores are used to evaluate machine translation models both in terms of human correlation and ease of use.  
Figure 12: BERT used as a dialogue reply scorer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Correlation (\(p\)-Value)** \\ \hline BLEU-4 & 0.055 (0.08) \\ ROUGE & \(-\)0.035 (0.26) \\ METEOR & \(-\)0.017 (0.59) \\ HAN-R(CE) & 0.138 (\textless{}0.001) \\ HAN-R(MSE) & 0.128 (0.003) \\ BERT & 0.602 (\textless{}0.001) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation of different models with human scores.  
As can be seen from Table 3, the BERT model outperformed the word-overlap metrics and HAN model. Since the BERT model takes advantage of the language model and can be fined-tuned according to other dataset, in this experiment, we used BERT with pretrained weights and fine-tuned it for the HUMOD dataset, which may explain the performance difference of supervised models. In addition, correlation of BERT with human judges is performed to investigate the behavior of the network against different dialogue turns (shown in Table 4). We tested BERT performance on different turn dialogues to see how context length affects the performance of dialogue measure. 2-turn dialogues correlation is found to be lowest, which can be due to hardness to evaluate the dialogue reply score for very short dialogues since it contains very little context, which may increase humans' own judgement and bias. Similarly, for long-dialogue conversation, it is slightly harder for the system to contain the context and make a good understanding of the fitness of dialogue context and reply pairs.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 3 Methodology
### Data Processing Module
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Feature Name** & **Meaning** \\ \hline
1 & Subdomain\_Length & Subdomain Length \\
2 & Subdomain\_Entropy & Subdomain Entropy \\
3 & Subdomain\_Digit\_Ratio & Proportion of Numbers in Subdomain \\
4 & Subdomain\_Uppercase\_Ratio & Proportion of Uppercase Letters in \\
5 & Subdomain\_Word\_Count & Number of Words in Subdomain \\
6 & Max\_Word\_Length & Maximum Word Length \\
7 & Jaccard\_Index\_Bi & Jaccard Index of Adjacent Double \\
8 & Jaccard\_Index\_Tri & Jaccard Index of Adjacent Triple \\
9 & Consecutive\_Digits\_Ratio & Proportion of Consecutive Numbers in \\
10 & Letter\_Digit\_Alternation\_Ratio & Proportion of Letter-Number Switching \\
11 & Resource\_Record\_Type & Proportion of Consecutive Consonants \\
12 & Label\_Count\_QNAME & Number of Labels in QNAME \\
13 & UDP\_Payload\_Length & UDP Payload Length \\
14 & Resource\_Record & Resource Record Size \\
15 & Response\_IP\_Count & Number of Response IPs \\
16 & DNSTTL & TTL Value \\
17 & Payload\_Length & Message Payload Length \\
18 & Response\_Type & Request Type \\
19 & Entropy\_Resource\_Record\_Content & Entropy of Resource Record Content \\
20 & Data\_Injection\_Volume & Amount of Injected Data \\
21 & Additional\_Data\_in\_UDP & Whether Other Data is Carried in UDP \\
22 & Resource\_Record\_Type & Resource Record Type \\
23 & Response\_Code & Response Code \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features. As shown in Figure 4, the top features influencing the prediction include Jaccard_Index_Bi, Subdomain_Uppercase_Ratio, and Additional_Data_in_UDP, each contributing a mean SHAP value greater than 0.07. These features are consistent with domain knowledge in DNS covert channel detection, as they reflect payload similarity, encoding behavior, and protocol anomalies. Importantly, the SHAP values provide an additive explanation model, meaning the final prediction can be decomposed as a sum of individual feature contributions. This ensures that highly weighted features directly push the model towards classifying the sample as DCC or not. For example, a high Jaccard_Index_Bi indicates frequent similarity between consecutive queries, which is typical in tunneling behavior and thus increases the DCC probability. Similarly, a high Subdomain_Uppercase_Ratio or Digit_Ratio often points to base32 or base64 encoding schemes used in covert channels.
## 3 Results and Discussion
### Impedance Modelling  
The results show that the current sensor set-up can detect material transport by means of impedance spectroscopy. Next, an impedance modelling of the measuring arrangement  
Figure 11: (a) Impedance over time at resonance frequency of measurement 6. (b) Impedance change over volume change for 39 individual measurements with a fitted curve.  
was performed. To develop an equivalent circuit diagram, the following components must be taken into account: the insulation of the copper wire and the gravel-water or gravel-sand-water mixture similar to the modelling approach in [11]. This results in the equivalent circuit diagram for the flat coil, as shown in Figure 12. The influences of the cables can be neglected by the calibration and thus have no effect on the frequency range. The model shows a 98.23% agreement with the measured spectrum within the frequency range of 2-12 MHz (Figure 13).  
This approach is to be pursued further. To achieve an even better agreement, the use of constant phase elements shall be investigated in the future. Furthermore, we are going to investigate whether the model can be simplified by a more suitable choice of bandwidth.
## 4 Evaluation
### Key Results
#### 4.2.2 Training Efficiency  
The average training time per epoch was 294.48 s, with a total training duration of 427.12 s for 50 epochs. Data processing and model update latency averaged 300 milliseconds, which is critical for real-time IIoT applications.  
The blockchain in this experiment facilitated secure model updates by ensuring integrity and authenticity of aggregated model parameters. Its lightweight consensus mechanism, DPoS, minimized computational overhead, contributing to efficient training.
## 3 Full Description of the Approach
### Project Framework
#### Overview of the Approach  
Figure 1 proposes an overview of the developed approach. The approach is composed of two main parts. In the first part, aim is to segment point clouds so that structural elements of the building are identified. Indoor point clouds are first segmented into sub-spaces namely floors and rooms. Then, for each room point cloud, several segmentations into planes are performed. These segmentations are combined with the classification of points into several categories (grounds, ceilings and walls). Finally, wall point clouds are obtained by assembling planes classified as walls.  
In the second part of the approach, walls and slabs of the building are reconstructed in 3D based on wall point clouds and ground and ceiling point clouds. In order to do that, point clouds describing these structural elements are adjusted either by volumes or surfaces. The output of the approach is a file in the IFC format (BIM format) where walls and slabs are described.  
The entire approach was carried out thanks to MATLAB (MathWorks) software. In the remainder of this section the different steps of the developed approach are detailed and illustrated based on indoor point clouds acquired in a public building namely a building of the National Institute of Applied Sciences (INSA) in Strasbourg (Figure 2). The ground floor, the first floor and a part of the second floor of the building which represent approximately 2500 m\({}^{2}\) were scanned. The acquisition and the post-processing of this dataset have already been presented in [58].