esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, a built-in UHF antenna sensor for high-voltage switchgear is designed, which adequately meets the high-voltage switchgear PD detection requirements. The casing of the high-voltage switchgear is initially used as the ground plane of the antenna sensor, and the antenna sensor is tightly attached to the inner wall of the high-voltage switchgear, which integrates the sensor into the switchgear. The antenna sensor diagram is shown in Figure 1. A microstrip antenna is adopted as a sensor prototype to achieve integration of the sensor and the switchgear. The microstrip antenna possesses a two-dimensional flat structure, which does not affect the internal electrical insulation environment. However, the narrow band of common microstrip antennas is a critical defect, and conventional methods for broadening the frequency band are inapplicable because of the internal space limitations in high-voltage switchgears, so the Koch snowflake patch is adopted as the radiation patch of the microstrip antenna, which can produce several resonant points to indirectly expand the antenna detection frequency band. As a result, it can greatly improve the PD detection performance of the sensor. Figure 1: Structure of the built-in antenna sensor.
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### OR Logic Gate of RDFTA  
In the modeling of a repairable system, the reliability calculation formulae of the OR gate and the AND gate of a general repairable system are temporarily adopted [44]. \(S_{x(i)}(t)\) represents the normal working probability of the OR gate input event \(x_{i}(t)\) at time \(t\), \(F_{x(i)}(t)\) is the failure state probability of the OR gate input event \(x_{i}(t)\) at time \(t\), \(\mu_{x(i)}\) is the maintenance rate of the input event \(x_{i}(t)\), and \(\lambda_{x(i)}\) is the failure rate of the input event \(x_{i}(t)\). \(S_{y}(t)\) represents the normal working probability of the OR gate output event \(y(t)\) at time \(t\), \(F_{y}(t)\) is the failure state probability of the OR gate output event \(y(t)\) at time \(t\), \(\mu_{y}\) is the maintenance rate of the output event \(y(t)\), and \(\lambda_{y}\) is the failure rate of the output event \(y(t)\) (Figure 1).  
Figure 1: The RDFTA OR logic gate.  
Quantitative operation rule  
In the OR logic gate, the input event \(x_{i}(t)(i=1,2,\ldots,n)\) and output event \(y(t)\) have two states: state 0 is the normal working state, while state 1 is the fault state. According to the relationship between the input and output events \(x_{i}(t)\) and \(y(t)\), a list of the quantitative operation rules of the OR gate can be obtained, as shown in Table 2.  
The quantitative calculation formula of the OR gate is as follows:  
\[\left\{\begin{aligned} S_{y}(t)=\prod\limits_{i=1}^{n}S_{x(i)}(t) =S_{x(1)}(t)\times S_{x(2)}(t),...,S_{x(n)}(t)\\ F_{y}(t)=1-S_{y}(t)\\ \lambda_{y}=\sum\limits_{i=1}^{n}\lambda_{x(i)}=\lambda_{x(1)}+ \lambda_{x(2)}\ldots+\lambda_{x(n)}\\ \mu_{y}=\frac{\lambda_{y}\times S_{y}(t)}{F_{y}(t)}\end{aligned}\right. \tag{1}\]
## 4 Results and Discussion
### CTAM Performances under Different Compaction Methods
#### 4.2.1 Aggregate Degradation  
Aggregate gradation has a significant influence on the performance of CTAM. Thus, in this section the aggregate degradation after the Proctor, vibration and gyration compactions were compared. The detailed aggregate degradations of Aggregate A and Aggregate B are shown in Figure 8.  
Both Figure 8a,b indicate that aggregate particles with a size over 19 mm degraded the most seriously. This was because large-sized particles, which generally undertook skeleton effects, were crushed more easily when subjected to impact energy. Due to the combined effect of increasing particles passing a larger-sized mesh and decreasing the crushed particles with a small size, the aggregates passing 9.5 mm and the smaller mesh decreased, indicating that the small-sized aggregate particles were harder to crush.  
On the whole, the aggregates after gyration compaction degraded the least, indicating that gyration compaction could better maintain the skeleton of CTAM.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 4 Experimental Results and Discussion
### Results Based on Experimental Data
Currently, our proposed method cannot fully handle scenarios involving such extensive layout changes, and addressing this challenge remains an area for future work. Figure 4: Data distribution on selected floors. Figure 5: Actual location and predicted results of validation dataset. Additionally, a comparison between our localization results and those of the EvAAL 2015 competitors [37] is presented in Table 5. This comparison evaluates performance based on mean positioning error, floor hit rate, and building hit rate. Notably, the proposed algorithm achieved a 100% accuracy rate in building estimation and an impressive overall floor hit rate of 95.05%. Furthermore, the algorithm's performance falls between that of the top-ranking teams in the EvAAL 2015 competition, demonstrating its effectiveness and competitiveness in the field. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Building** & **Floor** & **Loos Function Value** \\ \hline  & 0 & 0.0910 \\
0 & 1 & 0.0573 \\
2 & 2 & 0.0529 \\  & 3 & 0.0314 \\ \hline  & 0 & 0.1756 \\
1 & 1 & 0.2491 \\  & 2 & 0.0058 \\  & 3 & 0.0719 \\ \hline  & 0 & 0.0289 \\
2 & 1 & 0.0407 \\  & 2 & 0.0387 \\  & 3 & 0.0131 \\  & 4 & 1.3959 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The final loss function of proposed DNN model. Figure 6: Prediction errors across selected floors. The blue boxes represent the buildings (from left to right: Buildings 0, 1, and 2).
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 3 Designed Channel Code-Book MappingNet Based on Vector Quantization  
In this section, we present the CCB MappingNet for image transmission under satellite-ground conditions. Firstly, we introduce the design principle of CCB. Then, we propose a channel information conversion module CCB MappingNet. Finally, we give the training algorithm of CCB MappingNet.
## 4 Experimental Setup  
### Terrestrial and Satellite Channel Models  
For the experimental setup of hybrid satellite-terrestrial network access, previously developed channel models [25] are used. An in-depth description of these models is out of the scope of this paper. However, information about model configurations is needed for the interpretation of the obtained results. A total of 6 different configurations for satellite and terrestrial channels were simulated, each with at least 100.000 samples. The main parameter that was adjusted for each channel model was the value of the SNR. In this experimental setup, 6 different values of the SNR were tested: 0, 3, 6, 9, 12 and 15 dB. These SNR values used for the evaluation are the same as those selected in the authors' previous works [47, 48], as introducing more SNR values would make the results difficult to interpret because of the number of different parameters that are considered. The selected SNR range with a resolution of 3 dB covers a variety of conditions and provides comparable results to the authors' previous work, which is important when integrating the logic into different environments; this is so that the algorithm performance evaluation can be consistent. The duration of the timeslot in both simulations is 5 ms and the same value is used for the SNR measurement period. In our tested scenarios, we assume that the timeslot is equal to the SNR measurement period, but note that the simulator framework allows us to define these two parameters as being different. For the satellite channel, light shadowing was simulated, and for the terrestrial channel, the parameter \(m\) is set to 10, corresponding to the good direct visibility of the base station.  
These 6 different configurations of the satellite-terrestrial channel pairs enable the evaluation of different conditions. Although there are 6 different channel configurations, there are 12 test cases in total. Test cases from 1 to 6 represent different channel conditions with different SNR values. Test cases 7 to 12 use the same channel configurations; however, the prediction of MODCOD is based on the LSTM neural network. For an easier comparison, in each test case, channel 1 is always the satellite channel and channel 2 is always the terrestrial channel. In this way, comparing the channel performance can be performed within each case, and the comparisons of the MODCOD selection algorithms can be carried out by comparing cases \(i\) and \(i+6\).
## 3 Results
### Comparison between MACS-Countes to FACS Methods
#### 3.3.2 MACS-Countess Compared with FACS
This means that among cases predicted to be eligible for ART treatment according to the reference method due to a CD4\({}^{+}\) T cell count of <350 cells/\(\mu\)L, 10.3% of the cases would be falsely predicted (deduced by MACS-Countess) to be >350 and misclassified as ineligible until the next test. Additionally, at this threshold MACS-Countess had PPV and NPV of 96.3% and 85.7%, respectively. The PIMA system has either lower or similar upward misclassification values (0-10.4%) with a sensitivity of 89.6 to 100% [29; 30], whereas it has a lower PPV at 91.2% and a higher NPV at 92.3% [31; 32]. As the cut-off increases to 500 cells/\(\mu\)L, the specificity of MACS-Countess decreases to 88.9%, whereas the sensitivity remains 89.7%, which are not much different from those of PIMA, which are 95.5% and 84.2%, respectively [30].
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 3 The _Nack State_ Protocol
They produce a compatible measurement if [PERSON] chooses, \(X\) for \(\ket{i_{X}}\) or \(Z\) for \(\ket{i_{Z}}\) where \(i=0,1\). We sketch in **(b)** the case of quantum orthogonal states. Two cases are possible here \(\{(\ket{0_{X}},\ket{1_{X}}),(\ket{0_{Z}},\ket{1_{Z}})\}\). Table 1 shows an example of the _nack state_ protocol. Here, [PERSON] sends two biqubits to [PERSON]. The first biqubit is the orthogonal pair \(\left(\left|0_{X}\right\rangle,\left|1_{X}\right\rangle\right)\), and the second biqubit is the parallel pair \(\left(\left|1_{Z}\right\rangle,\left|1_{Z}\right\rangle\right)\). If the two states sent by [PERSON] arrive at [PERSON]'s detection system without any error, a double detection event is produced. If only one of the two states of the biqubit arrives at [PERSON]'s station, he obtains a single detection event. The _nack state_ protocol has been conceived of to use the same optical hardware of the \(BB84\) protocol; thus, it can be configured in most \(QKD\) systems as a software module application. However, two additional tasks must be implemented: the random computation of biqubits before preparing and sending the quantum states and the sifting stage of the protocol, which must include (1) sifting of single matching (compatible or non-compatible), where [PERSON] announces the number of the single detections inside the biqubit; and (2) sifting of double detection, matching or non-matching, from parallel or orthogonal states. The error correction and privacy amplification stages of the \(QKD\) protocol do not require changes. Until now, we have a protocol that behaves similarly to \(BB84\). In the _nack state_ protocol, most of the biqubits pulses sent by [PERSON] arrive at [PERSON]'s station as single pulses that behave like \(BB84\) signal pulses. However, we will see that in the presence of the \(IRFS\) attack, the eavesdropper unbalances the
## Appendix A  
The appendix provides complete definitions, units, operational roles, and the engineering significance for all 19 shield tunneling parameters and 14 geological parameters as follows:  
1. Shield Tunneling Parameters (19 parameters)
2. Grouting pressure (bar): Synchronized grouting pressure at the shield tail to fill the gap between the shield body and segments, controlling ground deformation. K-block location (dimensionless): Position index of the K-block (1-15) during segment assembly, affecting the closure stability of the segment ring. Horizontal/vertical displacement of shield head/tail (mm): Real-time displacement of the shield machine's head and tail, reflecting deviations in tunneling posture. Shield tail gap (mm): Gap distances at the top, bottom, left, and right sides between the shield tail and segments, directly influencing grouting efficiency and ground disturbance.  
Screw conveyor rotation speed (r/min) and torque (kN\(\cdot\)m): Critical parameters for controlling muck discharge, linked to earth pressure balance in the excavation chamber. Muck pressure (bar): Pressure of excavated soil in the chamber, a core indicator for maintaining face stability. Advance rate (mm/min): Tunneling speed of the shield machine; excessive rates may cause over-ex excavation or ground disturbance. Cutterhead rotation speed (r/min) and torque (kN\(\cdot\)m): Parameters governing cutterhead operation, impacting cutting efficiency and ground response. Thrust (kN): Total thrust of the shield propulsion system, directly related to ground resistance.
3. Geological Parameters (14 parameters) These parameters include groundwater level, tunnel burial depth, and stratum-specific properties (e.g., density, moisture content, cohesion, internal friction angle, and compression modulus--see Table 1), derived from geological surveys to characterize soil-layer impacts on settlement.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 5 Experimental Evaluation and Discussion  
In this section, we evaluate the algorithms proposed in Section 4.2 using real-world network traffic captures. The dataset and methods used for the evaluation of proposed algorithms are described. Finally, the results are also discussed.  
Two experiments were performed. The first (global reputation table evaluation) is targeted at evaluating the classification performance of the global reputation models produced by the algorithms proposed in Section 4.2. The second experiment (improvement of per-flow anomaly scores) is aimed at evaluating possible improvements that can be attained by using the global reputation models to strengthen the anomaly scores in individual networks.
## 3 Results
### Reliability and Sensitivity  
The descriptive and reliability statistics for all the outcome variables are presented separately for the steady-state phase, acceleration phase, and the average over 80 m skating, in Tables 1-3. Only three variables (velocity, double support time, and length), obtained during the acceleration phase, differed statistically between the trials (\(p<0.05\), \"trivial\" d). There was a 1.6% improvement in the velocity, and a shorter double support time and length (6.2% and 4.6%, respectively) from trial 1 to trial 2. The absolute reliability for all the spatiotemporal variables was shown to be acceptable (CV \(<\) 10%), but poorer in terms of the double support time and length, irrespective of the skating phase. The relative reliability for all the outcome variables averaged over an 80 m skating distance was \"moderate\" to \"very high\" (ICC = 0.73-0.97). It was \"high\" to \"very high\" (ICC = 0.76-0.90)during the acceleration phase and \"moderate\" to \"very high\" (ICC = 0.57-0.98) during the steady-state phase for all the variables, except for the step frequency that showed \"low\" relative reliability (ICC = 0.26). For all the variables, except the glide time, typical error values were lower than \(<\)5% of the mean values. For all the measures obtained during the acceleration phase and as the average over 80 m of skating, the typical errors were lower or equal to their respective SWCs(0.5), showing a \"good\" to \"satisfactory\" test sensitivity. The results obtained during the steady-state phase indicated \"marginal\" sensitivity [TE \(>\) SWC(0.5)] for the glide time, step frequency, double support time, and length, while they showed \"good\" sensitivity [SWC(0.5) \(>\) TE] for the velocity, glide, and step length (Tables 1-3).
## 5 Experimental Results and Analyses
### Hyperparameter Analysis  
#### 5.2.1 Label-Ratio  
The ratio of negative to positive labels in the dataset, Label-Ratio, will have a certain impact on the effect of the model, because a change of Label-Ratio may lead to too little label data or long tail problems. In order to verify the influence of Label-Ratio on the effect of our proposed model and to help us set the appropriate Label-Ratio parameter value in the experiment, we designed multiple sets of experiments based on the BND-GCN model; the experimental results are shown in Figure 3.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Model** & **Score** & \multicolumn{2}{c}{**CA-GrQc**} & \multicolumn{2}{c}{**CA-HepTh**} & \multicolumn{2}{c}{**p2p-Gnutella04**} & \multicolumn{2}{c}{**p2p-Gnutella08**} & \multicolumn{2}{c}{**p2p-Gnutella25**} \\ \hline LR & Accuracy Precision & 0.6667 & 0.5000 & 0.6860 & 0.7857 & 0.7731 & **0.9043** & 0.5971 & **1.0000** & 0.8007 & 0.8591 \\  & Recall & F1 score & 0.0141 & 0.0274 & 0.0866 & 0.1560 & 0.3574 & 0.5123 & 0.0276 & 0.0537 & 0.5903 & 0.6998 \\ SVM & Accuracy Precision & 0.6230 & 0.3333 & 0.6887 & 0.6667 & 0.8293 & 0.8777 & 0.6728 & 0.8000 & 0.8832 & 0.8013 \\  & Recall & F1 score & 0.0141 & 0.0270 & 0.1417 & 0.2338 & 0.5670 & 0.6889 & 0.0276 & 0.0533 & 0.8641 & 0.8315 \\ MLP & Accuracy Precision & 0.7789 & **0.7647** & 0.5955 & 0.4661 & 0.7292 & 0.5925 & 0.7604 & 0.8361 & 0.7676 & 0.6105 \\  & Recall & F1 score & 0.5493 & 0.6393 & 0.9213 & 0.6190 & 0.8694 & 0.7047 & 0.3517 & 0.4951 & 0.9777 & 0.7516 \\ Inf-GCN & Accuracy Precision & 0.7418 & 0.5889 & **0.8285** & **0.6802** & 0.8832 & 0.7723 & 0.7742 & 0.7238 & 0.8683 & 0.7199 \\  & Recall & F1 score & 0.7465 & 0.5684 & 0.9213 & **0.7826** & 0.9210 & 0.8401 & 0.5241 & 0.6080 & 0.9907 & 0.8339 \\ \hline BND-GraphSAGE & Accuracy Precision & 0.7559 & 0.6044 & 0.8047 & 0.6448 & 0.8305 & 0.7527 & 0.7604 & 0.5945 & 0.7789 & 0.6189 \\  & Recall & F1 score & 0.7746 & 0.6790 & 0.9291 & 0.7613 & 0.7320 & 0.7422 & 0.8897 & 0.7127 & 0.8771 & 0.7257 \\ BND-GAT & Accuracy Precision & 0.7277 & 0.658  
Through the experimental results, we can see that with the increase of Label-Ratio, the overall effect of the model generally tends to decline. This is due to the increase in the number of negative labels, and the long-tail problem becomes more and more significant, resulting in a poorer model classification effect. A relatively balanced Label-Ratio can improve model performance; this may have good guiding significance for us when choosing data to build a training set.
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 3 Results
### Effect of Training Data on Model Performance  
The upper plots in Figure 10 show the root-mean-square error (RMSE) for the linear regression in which GMF feature no. 1 was predicted by using the shaft speed values within different selected operating ranges. Models were identified by using the datasets with the sizes of 400, 1200, 2200 and 3000 samples. The selection of training subsets was conducted by using the 'adaptive training subsets' approach, where all the models were identified at the same time. Therefore, the presented models used the same operating ranges, as shown on the right in Figure 9.  
The plots show that the modeling error was typically high when the speed range was selected from the higher end of distribution. This can be explained by the higher variation of values there, as shown by the plot on the lower left corner in Figure 7. However, such a correlation between RMSE and shaft speed cannot be generalized to every feature.  
Figure 10: Errors in prediction of GMF feature no. 1 using selected training subsets (above) and 95% confidence intervals of regression coefficient \(\beta_{1}\) (below). Number of samples in datasets increases on the plots from left to right. Selected shaft speed ranges from each dataset and number of samples within ranges are written below.  
Moreover, the plots reveal that the number of samples within the selected speed ranges had only a minor effect on the modeling error. This is illustrated on the plots with different sample sizes (N) but similar speed ranges, such as range no. 2 on the plot with N = 400 and range no. 6 on the plot with N = 2200 with 212 and 660 samples within the ranges, respectively.  
The lower plots in Figure 10 show the 95% confidence intervals for the regression coefficient (\(\beta_{1}\)), see Equation (1). They were estimated based on the Wald test implemented in the _coef_I function in MATLAB(r). The plots reveal that the narrowest speed ranges (6-7 rpm wide) resulted in a high uncertainty of the regression coefficient values. However, this may be insignificant for monitoring purposes because the main idea in residual monitoring is to detect the increasing modeling error. At a narrow range, the predictor values vary only slightly. The plots also indicate that the number of samples within a range had no observable influence on the width of confidence intervals. A similar analysis was conducted for the intercept (\(\beta_{0}\)), but due to the lack of additional value, it was omitted from here.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 4 Reduced Overhead Routing Protocol
### Enhanced P2P Routing and Forwarding
#### 4.1.1 P2P Routing Problem in RPL
The RPL standard proposes the use of multicast DAO (\(M_{DAO}\)) messages to eliminate the deficiencies presented above. Nodes multicast (\(M_{DAO}\)) messages so that nodes in the network can maintain forwarding table entries for their direct neighbors. The P2 MP and P2P route construction process using \(M_{DAO}\) messages is shown in Figure 2. The figure only shows forwarding tables at root A and node D. Now, if node D needs to communicate with node E, it can directly forward the packet to node E resulting in lower number of transmissions. As this approach uses an extra control message apart from DIO and unicast DAO messages, it is also not as energy efficient as it could be.
## 3 The Use of Agro-Wastes in Concrete Production
### Use of Wheat Strav in Concrete Production  
Another AW used in the construction sector is WS. Straw concrete, a bio-based composite, is designed for thermal insulation in new buildings and thermal retrofitting of existing ones. WS is combined with lime or gypsum binders and natural additives (such as hemoglobin, casein, and gelatin) to enhance the material's thermal and mechanical properties. These composites exhibit attractive thermal conductivity and acceptable mechanical strength [62].  
WS additives are considered suitable for non-load-bearing building insulation materials. Optimizing the orientation of straw fibers in bales reduces thermal conductivity by 38% compared to conventional agricultural bales, allowing for similar reductions in insulation thickness while achieving equivalent performance. Additionally, optimizing fiber orientation reduces water vapor permeability, moisture buffering, and moisture ingress by up to 76% [63].  
WS was also evaluated as an insulation material for fired clay hollow bricks, with significant energy efficiency contributions. Walls containing densely compacted strawachieved energy savings of up to 69.08%. This finding highlights WS's potential to insulate hollow clay bricks used in building walls, offering energy and cost savings and environmental advantages [64].  
WS Ash is another AW with potential for concrete use. Adding WS Ash to concrete improves mechanical properties such as compressive strength, flexural strength, modulus of elasticity, and tensile strength. Compared to conventional concrete, mixes with 5% and 10% cement replacement with WS Ash showed compressive strength increases of 8% and 12%, respectively. However, higher Ash dosages reduce mechanical performance due to decreased flowability. Depending on the source, aggregates, water-to-binder ratio, and mix design, the optimal dose of WS Ash is between 10% and 20% [65].