esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Electrical Stability  
The stability of electrical parameters upon continuous bias is fundamental for the development of any user application. Figure 3 reports a bias stress test consisting of a continuous acquisition of 1000 transfer characteristic curves, in double-sweep mode and in ambient conditions. From Figure 3a, it is possible to notice that measurements are almost perfectly overlapped, thus demonstrating a good stability of electrical performances during the whole experiment. To better characterize the electrical stability of the proposed devices, threshold voltage and mobility were extracted from transfer characteristic curves; results are reported in Figure 3b. It is possible to notice that the threshold voltage almost linearly increased, passing from \(-180\) to \(-30\) mV, with a shift not exceeding 150 mV upon several hours of continuous operation. Charge carrier mobility remained almost stable for the 200 cycles, then started to decrease, passing from \(1.1\) to \(1\times 10^{-2}\) cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\), with a relative reduction of about 10%. Such results are consistent with what is already demonstrated in the literature for TIPS pentacene [37].
# A Linear-Time Algorithm for the Isometric Reconciliation of Unrooted Trees
## 2 Algorithm
### Further Algorithmic Details
To obtain this expression for \(\gamma_{1}\), observe that \(d_{T}(v_{1},p_{1})=d_{T}(u_{1},v_{1})-\alpha_{1}\) and \(\gamma_{1}=d_{T}(u_{2},p_{1})=d_{T}(u_{2},v_{1})-d_{T}(v_{1},p_{1})\). Now we have both points specified in the form \(p_{i}=T(u_{2},y_{i},\gamma_{i})\), where \(y_{2}=v_{2}\) and \(\gamma_{2}=\alpha_{2}\). We now consider the node \(x_{2}\) where paths from \(u_{2}\) to \(y_{1}\) and from \(u_{2}\) to \(y_{2}\) diverge. Again, we can compute \(d_{T}(u_{2},x_{2})\) as above. If at least one of \(p_{1}\) and \(p_{2}\) is closer to \(u_{2}\) than \(x_{2}\), then both points lie on a path from \(u_{2}\) to one of the \(y_{i}\) and can be synchronized easily. Otherwise, they are synchronized on the path from \(y_{1}\) to \(y_{2}\) using similar distance transformations as above. Since the synchronization is only a simple case analysis, it can be easily computed in \(O(1)\) time, and thus we are able to compute distances between any two points in \(O(1)\) time. The next primitive is to take two points \(p_{1}\) and \(p_{2}\) of the form \(p_{i}=T(u_{i},v_{i},\alpha_{i})\) and to express point \(p=T(p_{1},p_{2},\beta)\) in the form \(p=T(w_{1},w_{2},\gamma)\) where \(w_{1},w_{2}\in\{u_{1},v_{1},u_{2},v_{2}\}\). This is also easily achieved by synchronization. Once we have both \(p_{1}\) and \(p_{2}\) expressed on the same path between some \(w_{1}\) and \(w_{2}\), we easily determine the necessary distance \(\gamma\) of the new point \(p\) from \(w_{1}\), since we know the distance of \(p_{1}\) from \(w_{1}\) and distance of \(p\) from \(p_{1}\). Again, this works in \(O(1)\) time. Distance computations can be also used to evaluate predicate \(\texttt{between}_{T}(u,v,w)\). In the absence of zero-length edges, this predicate is satisfied if and only if \(d_{T}(u,w)=d_{T}(u,v)+d_{T}(v,w)\). In our algorithm, this predicate is used only for tree \(G\), which is assumed to have strictly positive edge lengths. Finally, we need to provide a bottom-up ordering of all rooted subtrees of a tree \(T\). This is computed by two tree traversals. Note that the tree is for technical reasons rooted in an arbitrary node. First, we do a post-order traversal of the tree. After exploring node \(u\) and its subtrees, we output subtree \(\text{sub}(T,u,v)\), where \(v\) is the parent of \(u\) in the current rooting. If \(u\) is a root, we skip this output. This traversal lists for each edge one of the two subtrees corresponding to the removal of this edge--namely, the one rooted in the node, which is lower in the auxiliary rooting. The second traversal proceeds in the pre-order fashion. After reaching node \(u\) from its parent \(v\), it outputs \(\text{sub}(T,v,u)\), and afterwards it recursively traverses children of \(u\).
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Handover Latency in Non-Location-Based RSS Extensions
#### 3.3.2 Handover Latency of the Proposed Loc-PMIPv6  
The proposed Loc-PMIPv6 protocol extension utilizes the location information for eliminating early handoff, and additional latency in addition to communicating to target MAG using nMAG-ID. Regarding handover performance, the proposed scheme keeps sending packets to MN when MN is inside the pMAG domain. LMA-stored packets are provided to nMAG when MN is disconnected from pMAG. Based on the connection of MN with target MAG, the target MAG immediately sends the buffered packets by minimizing the handover latency. Therefore, the handover latency for the proposed Loc-PMIPv6 is calculated using Equation (9).  
\[\text{t}_{\text{Loc-PMIPv6}}=\text{t}_{\text{WRS}}+\text{t}_{\text{L2}}+ \text{t}_{\text{RS}}+\text{t}_{\text{w-data}} \tag{9}\]
## 2 Materials and Methods
### Experimental Validation: Three-Point Bending Test Campaign  
To verify the correct modeling of the material, quasi static three-point bending tests were simulated and performed on composite samples according to ASTM D790 (as illustrated in Figure 5). The test sample was discretized with 103 four-node (S4R) shell elements, which led to assess a stable numerical result of 32.98 GPa for the bending stiffness. A good agreement was obtained after carrying out bending experiments on a 10 kN Autograph AGS-X Shimadzu testing machine, as the evaluated modulus was 32.47 \(\pm\) 0.95 GPa. The elastic modulus was calculated from load-deflection data measured during the tests. According to the standard, five rectangular specimens, each measuring 95 mm in length and 20 mm in width, were tested. The specimens were tested with a span-to-thickness ratio of 40 and a crosshead motion rate of 1 mm/min.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Tenax-E\_TPWF-PEEK-HTA40**} & \multicolumn{2}{c}{**Tenax-E\_TPUD-PEEK-HTS45**} \\ \hline Density & \(\rho\) & 1.54 g/cm\({}^{3}\) & 1.57 g/cm\({}^{3}\) \\ \hline In plane Young modulus (0\({}^{\circ}\)) & \(E_{1}\) & 60 GPa & 140 GPa \\ \hline In plane Young modulus (90\({}^{\circ}\)) & \(E_{2}\) & 60 GPa & 9.5 GPa \\ \hline Out of plane Young modulus & \(E_{3}\) & 9.9 GPa & 9.5 GPa \\ \hline In plane Poisson’s ratio & \(\
u_{12}\) & 0.098 & 0.323 \\ \hline In plane shear modulus & \(G_{12}\) & 4.72 GPa & 3.26 GPa \\ \hline Transverse shear modulus & \(G_{13}=G_{23}\) & 2.49 GPa & 3.33 GPa \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties evaluated via Digimat.  
Figure 4: Mesh convergence analysis for: (**a**) UD carbon fiber/PEEK, (**b**) bidirectional carbon fiber/PEEK.  
Figure 5: Three-point bending tests: (**a**) numerical simulation; (**b**) experimental test. Note: vertical displacements are indicated in the figure as U2 and expressed in mm.
## 3 Development of Energy Measuring Devices
### Absorbing Cup Type of Energy Measuring Device  
The cross-sectional view of the pin puller and energy absorbing cup is shown in Figure 4. The energy absorbing cup is made by 1 Cr18 Ni9 Ti stainless steel as shown in Figure 4b, and it is installed on the end of the piston as shown in Figure 4a. After the ignitor is fired, the high-temperature and high-pressure gas is generated to push the piston downward, resulting in compression and deformation of the energy absorbing cup. The transferred energy is obtained by multiplying the compression length and the strength of the absorbing cup.  
This approach calibrated the strength of the energy absorbing cup by a drop-weight impact testing machine. The test tooling is shown in Figure 5. The hammer weighs 8.8 kg with an additional mass of 3 kg. The range of impact energy set by the drop-weight machine during the calibration process is 50 J to 250 J. The actual impact energy is less than the preset impact energy due to friction, so the impact speed of the hammer is measured by a speed sensor, and the impact energy is calculated by mv\({}^{2}\)/2.  
When using the absorbing cup to measure the transferred energy, the energy loss in the impact between the cup and the end cap in the device and the rebound of the piston is ignored. The energy introduced by the ignitor of the pin puller consists of three parts, namely the energy to cut the shear pin, the work to overcome the friction and the energy absorbed by the deformation of the cup.  
When the energy absorbing cup is calibrated by the drop-weight impact testing machine, the energy loss in the collision and the rebound of the hammer is ignored. The work achieved by the kinetic energy of the hammer also includes three parts, that is, the energy to cut the shear pin, the work to overcome the friction and the energy absorbed by the cup.  
Therefore, we believe that it is reasonable and effective to use the drop-weight impact method to measure the required energy.  
Figure 4: Cross-sectional view of the (**a**) pin puller and (**b**) energy absorbing cup. 1—pin-rod, 2—shell body of pin puller, 3—shear pin, 4—pressure transducer port, 5—cartridge port, 6—energy absorbing cup, 7—cap, 8—O-rings.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion
### Results Discussion

Table 7: Average evaluation metrics for climatic variables by each scenario and benchmark method. Regarding the predicted variables, it can be seen that they generally have good results for the metrics, with values less than 1, which indicates that the proposed models are capable of predicting climate variables in the short and medium term. In general, when comparing the case studies considered, it can be seen that optimizing the characteristic parameters of the LSTM improved the metric values for all prediction horizons. The use of the GA minimized the MSE value for all variables, regardless of the horizon considered. For example, for a 3-day forecast, the MSE, MAE, and RMSE values for temperature (T_MED) decreased by 97%, 81%, and 81%, respectively, for mean wind speed (FF_MED) by 94%, 85%, and 85%, and for solar radiation (RG_TOT) by 44%, 28%, and 25%, respectively. On the other hand, for a 7-day forecast horizon, the values for T_MED decreased by 63%, 34%, and 52%, for FF_MED by 67%, 41%, and 42% and for RG_TOT by 66%, 41%, and 42%, respectively. Finally, for 15 days, the MSE, MAE, and RMSE values decreased by 38%, 18%, and 22% for T_MED, by 25%, 5%, and 13% for FF_MED, and by 82%, 61%, and 22% for RG_TOT. Comparing our results with the benchmark methods it can be observed that both cases perform better than the XGBoost methods, and perform as well as the transformer methods. In relation to the results obtained with GA, the optimization of XGBoost does not significantly improve its performance, and both models struggle with higher errors. Overall, the results show that transformers and LSTM methods are advanced neural network architectures capable of handling sequential data and capturing long-term dependencies. In fact, LSTM models, while computationally intensive, can be generally efficient, particularly for smaller datasets. Therefore, LSTMs are a good solution for weather forecasting. They are likely to provide accurate predictions, especially when optimized with techniques like GA.
## 5 Case Study
### Parametric Causality Inference and Anomaly Detection on a Real Satellite Telemetry Dataset
#### 5.2.3 Anomaly Detection  
To verify the performance on telemetry anomaly detection of our method, we compare it with biSPOT and biDSPOT methods to verify the improvement. The 3\(\sigma\), Local Outlier Factor (LOF) [35], One-class support vector machine (OCSVM) [36], and Isolation Forest [37] methods also serve as baselines, all of which do not require predicting the telemetry data. Furthermore, we compare our method with three data-driven anomaly detection methods based on prediction models, Long Short-term Memory-Variance Auto Encoder (LSTM-VAE) [38], Generative Adversarial Networks (GAN) [39], and Temporal Convolutional Network (TCN) [40].  
Figure 11: Parametric causal network inferred by MESS and IRESS. MESS leads to 95 edges, and IRESS leads to 43 edges. The width of the edge in the causal network represents the maximum causal time lag.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Contents** \\ \hline Number of parameters & 20 \\ Data sampling duration & 236 days \\ Data sampling frequency & 5 min \\ Length of dataset & 67,968 \\ Parameters to detect anomaly & B, D, Q, T \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic information of the satellite telemetry dataset.  
We employ precision, recall, and F1-score as the evaluate metrics (see Equation (18)). Figure 9b shows the confusion matrix of anomaly detection experiment. We also recorded the time-consumption of each method in the anomaly detection phase (experimental environment configuration: Windows 10 + python 3.8 + CUDA 10.1 + CUDNN 7.6.5 + tensorflow 2.5.0).  
When using biSPOT, biDSPOT, and DCDSPOT for anomaly detection, the setting of the risk parameter \(q_{1},q_{2},q_{3}\) and \(p_{1},p_{2},p_{3}\) are very critical, because they directly affect the detection effect. In Ref. [12], \(q\) is manually set to be \(10^{-3}\) and \(10^{-4}\), but in actual application, we need to determine the most reasonable \(q\) value based on historical detection results. In our experiment, value of \(q\) is selected by hyperparameter optimization, that is, we use grid search to find the \(q\) that can achieve the best detection performance. For different target parameter sequences, the optimal \(q\) settings are different, because these parameters have different operating modes. The parameter \(d\) of DSPOT is also determined by the same approach.  
When applying LSTM-VAE, GAN, and TCN for anomaly detection, it is necessary to set thresholds for residual to determine whether a value is anomalous. We use the SPOT to set thresholds for the residual sequence, because the residual sequence is often considered stable.
## 4 Discussion  
In this study, a model was developed for deepfake detection using a combination of the QTL and ViT architectures. The primary aim of the study was to overcome the limitations of existing methods by proposing a system that ensures both computational efficiency and high accuracy rates. Although the QTL and ViT architectures have been used in various fields, no prior study has combined these two technologies for deepfake detection. The proposed method aims to provide a more generalized solution capable of detecting spatial and temporal inconsistencies by merging these innovative technologies.  
The results obtained indicate significant success in deepfake detection. With a 90% accuracy rate, the model demonstrated substantial performance improvements compared to other studies based on previous research. Table 5 presents a comparative analysis of the proposed model with other prominent works in the literature. For instance, the study \"Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers\" reported 92.02% accuracy and a 97.61 AUC. Similarly, the study \"Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model\" achieved strong performance in cross-dataset adaptability, with a 90.9 AUROC on the DFDC dataset. However, considering the high hardware requirements and resource demands of these models, their applicability in real-world scenarios remains limited.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Methods** & **DFDC Accuracy** & **DFDC ROC AUC** \\ \hline Quantum Transfer Learning and & & & \\ Class-Attention Vision Transformer & QTL and CaiT & 90\% & 0.94 \\ Architecture (This Study) & & & \\ \hline Towards More General Video-based & & & \\ Deepfake Detection through Facial & & & \\ Feature Guided Adaptation for & & & \\ Foundation Model [6] & & & \\ \hline Deepfake Detection with Deep Learning: & & & \\ Convolutional Neural Networks versus & ResNet, Swin Transformer & 92.02\% & 0.9761 \\ Transformers [10] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of deepfake detection studies on the DFDC dataset.
## 5 Experiments
### Effectiveness Test
#### 5.1.2 Characteristics of Uncertain Attitude Error  
Here, we only considered the sample data without attitude correction. The characteristics of uncertain attitude error were found out.  
From mode 1 to mode 7, there is an obvious decreasing trend for the value of the uncertain attitude error. According to Section 2, an important reason for the uncertain attitude error of the 3D point cloud is the uncertain time deviation between the starting time of the 2D lidar and the starting time of the motor). Although the value of this time deviation is uncertain, it changes within a certain range. Therefore, in general, the faster the motor shaft rotates during this time deviation, the larger the attitude error of the 3D point cloud tends to be. The red part of Figures 8-10 verify this analysis. From mode 1 to mode 7, the scanning resolution of a rotating 2D lidar is getting higher and higher, and the speed of the motor shaft is getting slower and slower. Therefore, there is a tendency that the attitude error is reduced. However, individual cases that violate this trend still  
Figure 10: The maximum, average, and minimum values of angle \(\varphi\)error of 5 sampling points in each mode.  
Figure 9: The values of angle \(\varphi_{\text{error}}\) of 70 3D point clouds.  
exists. There is a sampling point in the segment corresponding to mode 5 of the red line in Figure 9 (the 22 nd point), and its attitude error is much larger than other sampling points in the mode 5 segment, and also much larger than the sampling points in the mode 4 segment. During our tests, cases like this are not unique. This is because although there is a strong correlation between the uncertain attitude error of the 3D point cloud and the speed of the motor shaft, other factors also count. First, the value of the time deviation is uncertain. The response time of the photoelectric switch and the acceleration characteristics of the motor may also have impacts on the uncertain attitude error. We have analyzed this in detail in Section 2.  
From mode 1 to mode 7, there is an obvious decreasing trend for the uncertainty of the uncertain attitude error. During the uncertain time deviations, the faster the shaft of the motor rotates, the greater the uncertainty of the time deviation will be transformed into the uncertainty of the attitude of the 3D point cloud. The red part of Figure 10 validates this analysis, which shows the maximum, average, and minimum attitude errors of the 5 3D point cloud collected in each mode. Obviously, there is an overall trend that the higher the resolution of the scanning mode, the closer these three values are, which means that the uncertainty of the uncertain attitude error turns smaller.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 3 Overview and Operation of IEEE 802.11 and IEEE 802.15.4
### IEEE 802.15.4
#### 3.2.2 IEEE 802.15.4 Superframe Structure  
IEEE 802.15.4 uses a superframe structure, as shown in Figure 5, to control channel access and the network coordinator (sink node) transmits beacons at predetermined intervals [36]. The coordinator splits the superframe into active and inactive periods. The Beacon Interval (BI) is defined as the time interval after which the superframe is repeated. The Superframe Duration (SD) is defined as the time interval of the active period. The active period (or SD) is the sum of the Contention Access Period (CAP) and Contention Free Period (CFP). Each SD consists of 16 time slots, of which CFP is allocated 7 time slots. By using two parameters, namely the Beacon Order (BO) and the Superframe Order (SO), the coordinator controls the duty cycle of the superframe, i.e., BI and SD. During the CAP, the sensor nodes must contest with each other using slotted CSMA/CA to transmit the GTS request (for data transmission) to the coordinator. The coordinator then allocates GTS in the CFP of the next BI. In every BI, the nodes go into inactive mode to preserve energy [37].  
To evaluate Beacon Interval and Superframe Duration, the following equation can be used:  
\[BI=960\times 2^{BO}, \tag{3}\]  
Figure 4: IEEE 802.15.4 basic media access control scheme; slotted CSMA/CA contention-based channel access. CCA = Clear Channel Assessment.  
Figure 5: IEEE 802.15.4 superframe structure. CAP = Contention Access Period; CFP = Contention Free Period; GTS = Guaranteed Time Slot.  
\[SD=960\times 2^{\text{SO}}. \tag{4}\]  
The Duty Cycle is given by the following equation:  
\[DC=\frac{SO}{BO}=2^{(\text{SO}-\text{BO})}, \tag{5}\]  
where \(0\leq SO\leq BO\leq 14\).
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 3 Secrecy Evaluation  
In this section, we first present the probability distribution function (PDF) of the horizontal distance from the selected UAV relay \(R_{0}\) to the destination \(D\). Then, we derive the closed-form expressions of the TP and SOP with and without the on-off transmission strategy. Furthermore, the effect of beam alignment error on the secrecy performance is analyzed.
# The XTRA Option at the NEUTRA Facility--More Than
## 3 Examples of Applications
### Multi-Modal Observations of Water Transport in Soils  
Finally, bi-modal X-ray and neutron imaging has been established as a prominent tool for investigations of water transport in geological materials, in particular in soils [26].  
Figure 11: 3D visualization of corrosion products around rebar in reinforced concrete (image from a test run at PSI, partly published in [24]). The diameter of the steel rebar in the core of the specimen is 8 mm.  
Figure 12: Sample of bog ore (from **left to right**): photograph, section from neutron tomography, corresponding section from X-ray tomography (Adapted with permission from ref. [25], Copyright John Wiley and Sons, 2010). The sample dimensions are approximately 30 mm \(\times\) 50 mm \(\times\) 100 mm.  
Figure 13: Sample of pyrite (from **left** to **right**): photograph, section from neutron tomography, corresponding section from X-ray tomography (Adapted with permission from ref. [25], Copyright John Wiley and Sons, 2010). The dimension of the sample is approximately 40 mm.  
Here, a specific example shall be presented, where a third modality, namely magnetic resonance imaging (MRI), complemented NEUTRA's bi-modal N/X capability.  
[PERSON] et al. [26] and [PERSON] et al. [27] realized this tri-modal non-destructive imaging approach to study quasi-steady state flow instabilities in structured soil as well as flow dynamics during recurrent ponded infiltration on undisturbed soil samples, respectively. Figure 14 shows corresponding tomographic slices of a soil sample observed by all three modalities at two different states of wetting. The provision of X-ray images allowed for the distinction between air voids and sand grains that are difficult to be identified correctly by the other modalities (N, MRI). The tri-modal imaging approach proved to be a well-suited tool for understanding the effects of entrapped residual air bubbles on water transport in soil.