esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
We expected such a range of temperature since a decrease in LCST after introducing a hydrophobic block into the PNIPAM chain was reported earlier [34] as the phase transition of polymers, used as macroinitiator in styrene polymerization, occurs between 42.8 \({}^{\circ}\)C and 44.3 \({}^{\circ}\)C. [PERSON] et al. [35] also observed a broad thermal transition for amphiphilic conetworks consisting PNIPAM cross-linked by hydrophobic polymer. They also observed that the decrease of LCST is proportional with hydrophobic polymer content. DLS also confirmed the thermosensitive properties of the synthesized copolymers in aqueous solutions. We observed that the sizes of particles detected in aqueous solution of copolymers decrease as a result of heating. For instance, in an aqueous solution of copolymer PNPS2 the determined maximum of number-averaged hydrodynamic diameter of particles was 600 nm at 25 \({}^{\circ}\)C; whereas at 45 \({}^{\circ}\)C this maximum is visible at 300 nm (see Figure S4 in the Supplementary Material). The observed decreases of particles sizes can be explained by the shrinking of PNIPAM blocks in the polymer chains under the influence of heating above their LCSTs. Such a phenomenon (aggregate contraction as the temperature is increased above LCST) was reported for thermoresponsive block copolymers [36]. DSC measurements, conducted for solid dry copolymer samples, allowed corroboration of the presence of two chemically different block phases separated in the molecules of the prepared copolymers. Figure 10 shows the DSC thermogram recorded for an exemplar copolymer sample (PNPS2). The glass transition temperatures (\(T_{\mathrm{g}}\)) determined from this curve are shown in Figure 10.  
In the thermogram of the synthesized copolymer, two separate \(T_{\mathrm{g}}\)S are observed at 96.2 \({}^{\circ}\)C and 121.3 \({}^{\circ}\)C corresponding to the PS and PNIPAM blocks, respectively. The presence of two separate \(T_{\mathrm{g}}\)S in the thermogram is direct proof that there are separated homopolymer blocks in the synthesized copolymer chains. This is because, as is well-known, a mixture of miscible homopolymers or random copolymers exhibits only one averaged \(T_{\mathrm{g}}\)[37; 38; 39]. For all synthesized copolymers, DSC thermograms showed two separate \(T_{\mathrm{g}}\)S. This demonstrates that the developed procedure indeed leads to copolymers with separated homopolymer blocks. Figure 9: The heating DSC thermogram for an aqueous solution of PNPS2 (0.5% _w/w_), heating rate of 1 K/min.
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 1 Introduction
The simplest way for the user to collect a set of their head images would be to record a selfie video covering as many various views of their head as possible. Using a general-purpose 3D reconstruction algorithm, automatic reconstruction of the head may suffer from the non-static scene and various image photometric distortions. This work proposes a methodology for the improvement of 3D head reconstruction, primarily from selfie videos, by increasing reconstruction quality and reducing the number of required computations. The novelty and contributions of this work can be summarized as follows:  
* Adaptation of a general-purpose 3D reconstruction algorithm to create head 3D point clouds from selfie videos;
* Achieved an increase of 3D head reconstruction quality by removal of background information and by selecting a subset of best quality frames from the full set of frames;
* Presented and compared methods for the selection of the highest quality frames;
* Performed comparative evaluation of feature sources (layer of convolutional neural network (CNN)) and dimensionality-reduction (DR) techniques used to order images by similarity in \(\mathbb{R}^{2}\) and \(\mathbb{R}^{3}\) with the purpose to predict the image's relative pose;
* Presented comparative results of the 3D head reconstruction improvements using mannequin head videos. Overview of the general-purpose 3D reconstruction algorithm and its proposed modifications to improve the 3D head reconstruction process is presented in Figure 1.  
The outline of the paper is as follows. In Section 2, materials and methods are described. In Section 3, computational experiments and their results and discussion are presented. Finally, Section 4 gives the conclusions of this work.
## 3 Results and Discussion
### Adopted Conditions of the Braking Maneuver  
The calculations assume that the braking process starts at the initial time t = 0 with the initial conditions:  
\[v_{s}=20\;\mathrm{m/s}\;(72\;\mathrm{km/h}),\;\omega_{ki}=40.5\;\mathrm{rad/s}\]  
In the brake system model, the input is the position of the main control valve adjuster of the pneumatic actuation system. It was assumed, for comparison, to carry out two calculation cycles for the adopted variants b1... 8, named:  
* Constant excitation (\(\alpha=\mathrm{const}=45^{\circ}\), the same position of the main control valve adjuster in variants b1... 8)--hereinafter denoted as CE.
* Constant deceleration (\(\alpha=\mathrm{var}\), the angle \(\alpha\) in each variant b1...8 is selected to obtain approximately the same deceleration value in the phase of fully developed braking)--hereinafter denoted as CD.  
The final effect of the braking system model is the time history of the given braking moments on vehicles' axes. Figure 9 presents such histories. They are examples but correspond to the real ones that can be found for the analyzed vehicles. The driver's reaction time is omitted, and the rise times are assumed at the level of the so-called emergency braking. The values of the maximum braking torques and the response times result from the catalogue data of the brake system components (for the maximum working pressure) [69, 70, 76] and the characteristics of the brake actuators and brake mechanisms ([67, 68]). The response time of the pressures, as well as the response time of the braking system and the torque rise time for the individual axes of the set, are summarized in Table 3. Reaction times on the pneumatic lines were selected so that the total response times were comparable with the available research in the literature [15, 30, 77, 78]. The course shown in Figure 9 was applied to the CE (constant excitation) calculation cycle.  
Figure 8: Characteristics of the computational braking ratio as a function of pressure in the coupling head: (**a**) A tractor with an equivalent coupling load simulating a laden and unladen semitrailer; (**b**) A laden and unladen semitrailer.  
As standard, calculations were made with the ABS switched off. In cases where the wheels were locked, the simulation was repeated with the model of the ABS turned on. The following parameters were adopted in the ABS model: \(s_{max}\)= 0.3, \(s_{min}\)= 0.1, \(\Delta p_{ABS}\) = 20/10 bar (pressure reduction/increase phase), \(\Delta t_{ABS}\) = 0.5 s.
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 4 Implementation
### Comparison with Deep Learning Models  
To further demonstrate the effectiveness of our proposed method, we conducted additional experiments comparing it with state-of-the-art deep learning models commonly used for anomaly detection. By including these comparisons, we aim to showcase the superiority of our approach over both traditional and modern anomaly-detection techniques.  
We selected the three widely used deep learning models in the task of anomaly detection, which are Autoencoders (AE), Variational Autoencoders (VAE), and Recurrent Neural Networks (RNN). These models were chosen based on their popularity and proven performance in anomaly-detection tasks. For each deep learning model, we implemented the necessary architecture and trained it using the same dataset and evaluation metrics as our proposed method. We ensured that the hyperparameters and training settings were appropriately tuned for each model. We then compared the performance of our proposed method with these deep learning models using the F1 score metrics.  
Figure 6: Original relation graph of Hadoop.  
Figure 7: Simple relation graph of Hadoop.  
In the HDFS anomaly-detection evaluation, the F1 scores for the NN, AE, VAE, and RNN models were 0.90, 0.87, 0.88, and 0.88, respectively. Similarly, in the BGL anomaly-detection evaluation, the F1 scores for the NN, AE, VAE, and RNN models were 0.90, 0.89, 0.90, and 0.89, respectively. Furthermore, in the PT anomaly-detection evaluation, the F1 scores for the NN, AE, VAE, and RNN models were 0.88, 0.86, 0.87, and 0.87, respectively. For better demonstration, we added these results into Table 6. These scores highlight the capability of the models to identify anomalies in the HDFS, BGL, and PT datasets.  
The results were obtained through rigorous cross-validation and statistical analysis to ensure reliable comparisons. By including comparisons with deep learning models, we demonstrate the effectiveness and superiority of our proposed method in detecting and localizing anomalies in unstructured logs.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis
### Verification and Comparison of Soft-Error Robustness  
To verify the soft-error recovery capability of the proposed cell, a double exponential current source is used to simulate a single-event upset (SEU). By choosing the appropriate direction of the current source, a positive transient pulse is generated at the drain of an OFF-state PMOS, and a negative transient pulse is generated at the drain of an OFF-state NMOS. Therefore, to evaluate soft-error immunity, a negative transient pulse is injected into the Q node storing logic '1', and a positive transient pulse is injected into the S0 node storing logic '0' (see Figure 10). The injected current is defined as follows [22]:  
\[I(t)=I_{\mathrm{O}}\bigg{(}e^{\frac{-t}{\tau_{\mathrm{a}}}}-e^{\frac{-t}{\tau_ {\beta}}}\bigg{)} \tag{1}\]  
\[I_{\mathrm{O}}=\frac{Q}{\tau_{\mathrm{a}}-\tau_{\beta}} \tag{2}\]  
Here, \(I_{\mathrm{O}}\) is the peak current of the pulse, \(Q\) is the total collected charge, \(\tau_{\mathrm{\alpha}}\) is the junction collection time constant, and \(\tau_{\beta}\) is the initial ion track establishment time constant. In this study, \(\tau_{\mathrm{a}}\) is set to 200 ps and \(\tau_{\beta}\) to 50 ps for computational simplicity [4; 20]. The critical charge (Q\({}_{\mathrm{C}}\)) is defined as the minimum amount of charge required to flip the stored data when a sensitive node is affected by an SEU. Soft-error immunity is evaluated by measuring Q\({}_{\mathrm{C}}\) for all sensitive nodes in the SRAM cell, and the lowest value is considered the effective Q\({}_{\mathrm{C}}\) of the cell. The effective Q\({}_{\mathrm{C}}\) values of all cells are measured and compared accordingly. The measured effective Q\({}_{\mathrm{C}}\) values are listed in Table 1. Figure 11 shows the simulated waveforms of the proposed cell when varying charges from 30 fC to 150 fC are injected into nodes Q and S0. Since LDRH16T has only two sensitive nodes when storing logic '1', only Q and S0 are used for SEU validation. Importantly, the proposed cell can  
Figure 9: Comparison of HPWR between the proposed LDRH16T and previous works.  
autonomously recover to its original state even when exposed to SEUs with charge levels exceeding 150 fC. This is because all transistors are part of a sufficiently strong feedback structure, which allows the cell to promptly restore the original data even after it has been disturbed. All other comparison cells except for EDP12T and SEA14T have finite Qc values due to partial SEU immunity. Furthermore, as described in Section 2, the layout of LDRH16T ensures sufficient spacing between nodes Q and S0, thereby preventing charge sharing between these sensitive nodes. Therefore, the proposed cell is highly suitable for operation in radiation-prone environments.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 5 Conclusions  
A TSOM method based on two-input deep-learning for nanoscale measuring is proposed in the paper. The TSOM images and the focused images are taken as the two inputs of the network and processed according to their own characteristics. In the method, the collected image information is effectively utilized so that the measurement accuracy is effectively improved. In the experiment of gold lines measuring with a linewidths range of 247-1010 nm, the MSE of our method can be reduced to 5.18 nm\({}^{2}\) and the MAE can be reduced to 1.67 nm, which is much lower than other deep-learning TSOM methods previously proposed. The two-input deep-learning TSOM method proposed in this paper is not limited by the feature extraction algorithm or the classification categories and it avoids the complex and lengthy simulation modeling process such as the library-matching TSOM method. The method shows good performance in both accuracy and convenience in the nanoscale measurement. In the future, we will further improve the accuracy of TSOM by optimizing the TSOM imaging system and the measuring network to promote the application in practice of TSOM.  
Conceptualization, Z.Z. and J.R.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z. and J.R.; formal analysis, Z.Z., J.R. and R.P.; investigation, Z.Z., J.R. and Y.Q.; resources, Z.Z., R.P. and Y.Q.; data curation, Z.Z.; writing--original draft preparation, Z.Z. and J.R.; writing--review and editing, Z.Z.; visualization, Z.Z. and Y.Q.; supervision, J.R. and Y.Q.; project administration, Y.Q.; funding acquisition, J.R., R.P. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number 52175492.  
Not applicable.  
The data presented in this study are available from the corresponding author, Z.Z., upon reasonable request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Assessment of the Experimental Results
#### 3.1.1 First Data Collection Period  
Figure 4 represents the supply air temperature and the indoor temperature of OR1. During the first 13 h, the HVAC system is still working so a steady state can be observed; the OR is ventilated and conditioned. After these hours, which correspond to off-use periods, the room is ventilated without air conditioning and is left following free-floating conditions. It is shown how the capacitance of the room flattens the profile of temperature and RH of the supply air.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Zone** & **SCENARIO 1** & **SCENARIO 2** & **SCENARIO 3** \\ \hline Total air intake & ORs & 21.5 ACH & 21.5 ACH & 20 ACH \\  & Anc. rooms & 6 ACH & 6 ACH & 6 ACH \\ \hline Outside air intake & ORs & 21.5 ACH & 10.8 ACH & 4 ACH \\  & Anc. rooms & 6 ACH & 3 ACH & 2 ACH \\ \hline Working period \({}^{1}\) hygothermal conditions & ORs & \multicolumn{2}{c}{T = 22 \({}^{\circ}\)C, 45–55\% RH} \\  & Anc. rooms & \multicolumn{2}{c}{T = 23.5 \({}^{\circ}\)C, 45–55\% RH} \\ \hline Off-use period hygrothermal conditions & ORs & \multicolumn{2}{c}{15–30 \({}^{\circ}\)CNon-controlled RH} \\  & Anc. rooms & \multicolumn{2}{c}{15–30 \({}^{\circ}\)CNon-controlled RH} \\ \hline \hline \end{tabular} \({}^{1}\) Working period: Monday to Friday, from 8 am to 3 pm.  
\end{table}
Table 3: Main simulated ventilation parameters in the different scenarios.  
Figure 5 shows the temperature and RH within OR2. When the ORs are conditioned, the indoor parameters from OR2 and OR1 are different due to requirements of operations or occupants. Despite this, when the rooms are ventilated with unconditioned fresh air, the indoor temperature and RH gets very similar values to OR1 due to the similarity of the capacitances.  
The behavior of OR3 is similar to the rest of the ORs and is shown in Figure 6. When the room is conditioned, the temperature evolution of the room when the set point temperature is 22 \({}^{\circ}\)C is similar to the temperature evolution from OR1. During the unoccupied period, the RH values in OR3 are slightly higher than in the other ORs, ranging from 19.1% to 68.9% (Table 4).  
Figure 4: Temperature and RH from OR1 during the first experimental data campaign.  
Figure 5: Temperature and RH from OR2 during the first experimental data campaign.  
Figure 6: Temperature and humidity from OR3 during the first experimental data campaign.  
Table 4 shows the maximum and minimum values of RH and temperature measured in the different ORs. The highest fluctuation of the indoor air temperature is 7.4 \({}^{\circ}\)C, reached inside OR3. The maximum oscillation of RH was 27.5%, obtained between day and night hours, also in OR3. The fluctuations of temperature and RH are in general higher in the intake air reaching maximum variation values in OR3 of 15.9 \({}^{\circ}\)C and 47.9%, respectively.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 7 Conclusions  
The aim of this study is to introduce a secure handover framework that utilizes PMIPv6 for a group of 6 LoWPAN devices that are resource-constrained. A number of secret parameters are used in the registration phase to establish confidential and robust session keys, while tickets are used to validate the initial group membership, ensuring efficient and secure handovers between mobile nodes and group leaders. SL_GAS's resilience to several security risks is confirmed by formal automated verification with the Scyther tool and SVO logic, while an informal examination shows its ability to withstand known attacks. When contrasted with most related frameworks, such as SGMS, SPAM, and SIEP, SL_GAS is reduced regarding signaling cost, transmission delay, and computation overhead, according to our mathematical analysis. The SL_GAS framework enables secure and fast handovers while protecting against malicious assaults. In addition to providing secure and efficient group handovers, SL_GAS also maintains a balance between security and privacy, making it a robust solution for managing 6 LoWPAN mobility. Our future research will address privacy concerns related to 6 LoWPAN group mobility management to prevent attackers from tracking mobile devices based on public information or transmitted messages between entities.  
Conceptualization, R.A.; Methodology, M.K.H.; Formal analysis, F.F.A.; Investigation, F.F.A. and E.A.S.; Resources, M.K.H. and R.A.; Data curation, R.A.; Writing--original draft, F.F.A.; Writing--review & editing, M.K.H. and R.A.; Supervision, E.A.S.; Project administration, E.A.S.; Funding acquisition, E.A.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Higher Education (MOHE) for funding under the Fundamental Research Grant Scheme (FRGS) under FRGS grant number FRGS/1/2022/ICT11/UKM/02/2.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 Results
### Evaluation Metrics and Loss Functions  
Mean absolute error calculates the average of the absolute differences between the predicted and actual values [33]. It reflects the overall prediction accuracy of the model. The formula for calculating _MAE_ is as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{3}\]  
In the formula, \(y_{i}\) represents the actual blood pressure value, \(\hat{y}_{i}\) represents the blood pressure value predicted by the model, and \(n\) represents the sample size. A smaller _MAE_ value indicates more accurate predictions made by the model.  
In the context of blood pressure estimation models, standard deviation is typically used to measure the variability of prediction errors [34], indicating the stability of the model's predictions. The formula for calculating _STD_ is as follows:  
\[STD=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i}-ME)^{2}}}} \tag{4}\]  
where _ME_ represents the mean error of prediction errors, and its calculation formula is as follows:  
\[ME=\frac{1}{n}{\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})}} \tag{5}\]  
Mean absolute percentage error (_MAP_) measures the average percentage error between the predicted values of the model and the actual values [35]. It provides an understanding of the magnitude of prediction errors relative to the actual values. The formula for calculating _MAP_ is as follows:  
\[MAPE=\frac{1}{n}{\sum_{i=1}^{n}{\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|} \times 100\%} \tag{6}\]  
When there are zeros or values very close to zero in the actual values \(y_{i}\), _MAP_ may become extremely large or impossible to calculate. Therefore, when using _MAPE_ as an evaluation metric, special attention needs to be paid to the characteristics of the dataset. A smaller _MAPE_ value indicates a lower percentage of prediction errors made by the model, suggesting better model performance.  
In the experiments of this chapter, _MSE_ was chosen as the loss function during model training. The _MSE_ loss function is one of the commonly used loss functions in regression problems, which measures the difference between the predicted values of the model and the actual values. The calculation formula for the _MSE_ loss function is as follows:\[MSE=\frac{1}{n}{\sum_{i=1}^{n}{(BP_{i}-\hat{BP_{i}})}^{2}} \tag{7}\]  
where \(BP_{i}\) represents the actual blood pressure value, \(\hat{BP_{i}}\) represents the blood pressure value predicted by the model, and n represents the sample size, which is the number of samples in the dataset used for training. \(MSE\) calculates the average of the squared differences between the predicted values of the model and the actual values. The squaring operation gives larger errors more weight in the loss function, encouraging the model to pay more attention to predictions that deviate further from the actual values. The average ensures that the loss function is comparable across different sample sizes.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 3 Photonics-Based Subsystems for All-Optical Processing
### Reconfigurable Photonics
These are implemented by MZI’s and prescribed length delay of \(\Delta\)L (inset); (**d**) normalized frequency response of an FIR filter having 38 filter taps. divided by the number of taps, such that an implementation comprising a large number of taps (e.g., \(\geq\)32) be able to attain improved performance. The cascade of FIR filters, as illustrated in Figure 15c, longitudinally increases the chip size, which in essence is a single input-multiple output interferometer, albeit delivering a better-quality spectral response (as shown in Figure 15c). It is worth mentioning that such PIC also requires integrating a large count (\(\sim\)100) of fast phase modulators to adapt the filter taps and allow for reconfiguration and better control of the filter response, as is the case with other Reconfigurable PICs. The basic building block will consist of the tunable band drop filter, implemented by photonic FIR filters. As discussed above, the design of reconfigurable FIR filters has already been demonstrated in [62]. In our opinion, reconfigurable photonic circuits may be both the enabling technology for visionary photonic architectures yet to be realized and provide an immense boost for the all-optical processing needed for optical communications. Finally, several other research fields lack the know-how of photonic integration and design, and we believe that they will benefit from the capabilities of reconfigurable photonic technologies.