esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photothermia and Magnetic Hyperthermia Experiments  
Thermal measurements were performed in 0.5 mL Eppendorf tubes, containing 50 uL of nanomaterials dispersed in water. Concentrations ranged from [Fe] = 1 mM to [Fe] = 150 mM. Samples were exposed to an 808 nm laser (Laser Components S.A.S, Meudon, France) positioned 4.5 cm above the tube, with the power density fixed at 0.3 W/cm\({}^{2}\) (corresponding to 1.82 A).  
For magnetic hyperthermia modality, the same sample preparation was used. The magnetic field was generated by the nB Nanoscale Biomagnetics device (Nanoscale Biomagnetics, Zaragoza, Spain), at a frequency of 471 kHZ with an 18 mT magnetic field intensity.  
For both instances, temperature elevation was recorded with an infrared thermal imaging camera (FLIR SC7000, FLIR Systems, Wilsonville, OR, USA) in real time and processed with ALTAIR software (Altair Engineering, Troy, MI, USA). Heating was quantified both with the plateau temperature reached after 5 min of laser treatment and the specific absorption rate (SAR), meaning the power dissipated per unit mass of iron (W\(\cdot\)g\({}^{-1}\)), according to the following equation:  
\[SAR=\frac{C\times m_{s}}{m_{Fe}}\times\frac{dT}{dt} \tag{1}\]  
where \(C\) is the specific heat capacity of the sample (\(C_{\text{water}}\) = 4.185 J/g/K), \(m_{Fe}\) is the total mass of iron in the sample (g), \(m_{s}\) is the total mass of the sample (g), and \(dT/dt\) is the temperature increase at the initial linear slope (30 s).
## 4 Experiments
### Data Set  
#### 4.1.1 OuluVS2  
We choose the OuluVS2 corpus to evaluate our scheme. The database contains 10 short phrases, 10 digits sequences and 10 TIMIT sentences uttered by 52 speakers. The corpus includes face images captured by five cameras simultaneously at \(0^{\circ}\) (frontal), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\) and \(90^{\circ}\) (profile) angles. In this study, we adopt the phrase data and digit data, uttered three times by each speaker. In our experiment, the data spoken by 52 speakers are divided into training data by 35 speakers (speaker ID:1-36), validation data by 5 speakers (speaker ID: 37-41) and testing data by 12 speakers (speaker ID: 42-53). Note that the speaker ID: 29 is missing. We conduct the same data split as previous works, such as [3; 6; 14], for a fair comparison. We also check whether the data split is appropriate by changing the different split settings, and confirm that using the data sets gives us fair results. The phrases are as follows: \"Excuse me\", \"Goodbye\", \"Hello\", \"How are you\", \"Nice to meet you\", \"See you\", \"I am sorry\", \"Thank you\", \"Have a good time\", \"You are welcome\". Each digit utterance consists of 10 digits randomly chosen. Note that, since we use a part of this  
Figure 4: A 2D CNN model for ASR. Numbers in parentheses mean data shapes. For example, (115,39,1) indicates 115 (vectors) \(\times\) 39 (MFCCs) \(\times\) 1 (channel).  
corpus to enhance model training data, the task in this work is a 10-class classification for phrase utterances.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Mobile Edge Computing with Drones  
Mobile Edge Computing is a new cellular network scheme in which BSs provide connections to UEs and computing services. This technique essentially brings cloud services closer to UEs, reducing latency for several compute-dense applications such as speech recognition and augmented reality. When MEC is supported by drone BSs, a number of issues arise. Drone BSs must have certain computing platforms, such as Graphical Processing Units (GPUs), to provide cloud services that will improve drone energy consumption and payload. Another issue is computing session continuity sincefast-moving drone BSs may create serious disconnections for the ongoing computational functions of UEs. Further research should be conducted to address the potential of MEC and its challenges regarding drones [143]. Different system settings with various mobility scenarios should be considered.
## 5 Optimization of LSTM for Fall Detection System
### Hidden Layer Node and Regularization Rate  
In this section, the impact of different hidden layer nodes and different regularization rates in the STM-LSTM-based fall detection system is examined. Figure 12 shows the validation data accuracy vs. regularization rate of the STM-LSTM-based fall detection system with respect to the different numbers of hidden layer nodes. The regularization rates are 0.00065, 0.0015, 0.0065, 0.015, 0.065, 0.15, and 0.65. The black squares, red circles, blue up-triangles, green down-triangles, purple diamonds, and gold left-triangles denote different numbers of the hidden layer nodes of 6, 13, 32, 64, 128, and 256, respectively. Peak values of accuracy of the numbers of the hidden layer nodes, 6, 13, 32, 64, 128, and 256 are 94.64% at \(\lambda\) = 0.00015, 96.42% at \(\lambda\) = 0.00065, 97.32% at \(\lambda\) = 0.0015, 97.32% at \(\lambda\) = 0.0065, 98.21% at \(\lambda\) = 0.015, and 98.21% at \(\lambda\) = 0.065, respectively. As the number of the hidden layer nodes increases, the regularization rate at the peak of accuracy increases and the peak values of accuracy increase. This means that as the number of hidden layer nodes increases, both accuracy and overfitting can increase due to the increase of network complexity [26; 47], and thus the overfitting can be suppressed by increasing the regularization rate [48].  
Figure 11: Validation data accuracy vs. regularization rate of the STM-LSTM-based fall detection system with respect to different sampling intervals.  
Meanwhile, as the network complexity increases, the training time also increases, and thus it is necessary to efficiently select the number of hidden layer nodes suitable for both higher accuracy and shorter training time. Figure 13 shows the highest validation data accuracy and computation time of the STM-LSTM-based fall detection system with respect to different number of hidden layer nodes. As the number of hidden layer nodes increases, the highest validation data accuracy increases, and computation time increases rapidly at 256 nodes. It showed the same best accuracy in the number of hidden layer nodes of 128 and 256. Accordingly, it can be determined that the number of hidden layer nodes of 128 is most appropriate in terms of accuracy and time.
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
###### Abstract  
This paper reviews the progress of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs), mainly focusing in the work of the authors' group. The background to the development of the current device structure on sapphire is described and the reason for using a (0001) sapphire with a miscut angle of 1.0\({}^{\circ}\) relative to the m-axis is clarified. Our LEDs incorporate uneven quantum wells (QWs) grown on an AlN template with dense macrosteps. Due to the low threading dislocation density of AlGaN and AlN templates of about 5 \(\times\) 10\({}^{8}\)/cm\({}^{2}\), the number of nonradiative recombination centers is decreased. In addition, the uneven QW show high external quantum efficiency (EQE) and wall-plug efficiency, which are considered to be boosted by the increased internal quantum efficiency (IQE) by enhancing carrier localization adjacent to macrosteps. The achieved LED performance is considered to be sufficient for practical applications. The advantage of the uneven QW is discussed in terms of the EQE and IQE. A DUV-LED die with an output of over 100 mW at 280-300 nm is considered feasible by applying techniques including the encapsulation. In addition, the fundamental achievements of various groups are reviewed for the future improvements of AlGaN-based DUV-LEDs. Finally, the applications of DUV-LEDs are described from an industrial viewpoint. The demonstrations of W/cm\({}^{2}\)-class irradiation modules are shown for UV curing.  
AlGaN; nitride; deep-ultraviolet; light emitting diode; DUV; LED; sapphire; sterilization; UV curing; UV printer +
Footnote †: journal: Applied  
_Applied_  
_sciences_  
_Received: 15 June 2018; Accepted: 19 July 2018; Published: 31 July 2018_
## 3 Results and Discussion
### The Impact of Different Parameters on the Efficency of IME Process
#### 3.2.2 Efficiency of the m-IME and c-IME Processes  
Studies on the comparison of the efficiency of the m-IME and c-IME processes were carried out only at pH 3. The differences between the efficiency of the m-IME and c-IME processes for microelectrode doses in the range of 20/2-40/120 g/L are shown in Figure 2a-c.  
The results showed that the separation of HS in the PC step positively affected the COD removal. The m-IME process turned out to be highly efficient in COD removal--the maximum efficiency of the process ranged from 34.4 to 92.2%, depending on the doses of microelectrodes used (Fe/GAC from 20/2 to 40/120 g/g/L). A lower efficiency of the IME process was observed in the case of the process conducted without PC (c-IME); however, the differences in the efficiency of the m-IME and c-IME processes with the increase in doses were similar. In the entire range of microelectrodes, the COD removals obtained in the modified process were higher than the COD removals obtained in the classic process by approximately 5.5 to 7.0%. The PC process conducted alone allowed for a COD removal of 9.4%. The application of PC before the IME process allows for the avoidance of negative phenomena of covering microelectrodes with flocks of HS and blocking the active surface of microelectrodes. It is also worth noting that for lower doses of GAC, the oxidation processes (mainly ZVI) dominate over the sorption process. Therefore, in further studies focused on better understanding the nature of the IME process, lower doses of microelectrodes were used--mainly the Fe/GAC ratio 40/10 g/L.
## 6 Conclusions  
In this paper, we propose an algorithm to identify in real time the dynamic forces acting on a vibrating structure. The algorithm that is based on Kalman filter, consists of three steps. First, the system and the control matrices are built for the vibrating structure using the vibration differential equation for a discrete system or the mode truncation method for a continuous system. Next, the weighted least square method is used to minimize covariance unbiased estimation. Then a state vector is updated based on acceleration measurements. By minimizing the prior estimate error covariance, the excitation force is estimated. This is repeated at a fixed time interval where the applied load is updated in the time domain. As an application the proposed algorithm is used to identify the dynamic load applied on a simply supported beam. The performed numerical simulations show that the proposed method can be used to find accurate estimate for the applied force even when the measured data or the model parameters are not very accurate. To validate the proposed algorithm experimentally, a LabVIEW (National Instruments Ltd.) implementation is presented and discussed. The proposed algorithm is verified through two different types of dynamic loads. The experimental results show a good potential for the proposed approach in identifying the dynamic load applied on a structure in real time. Moreover, when compared to a standard method the proposed algorithm is more accurate and significantly more stable. The results show 95% reduction in the CPU time required with the standard method. The work presents one of the first attempts in real time dynamic load identification the and can have several future applications.  
Conceptualization J.J.; methodology, J.J.; software, Z.L.; validation, Z.L.; formal analysis, M.S.M.; investigation, J.J.; resources, S.L.; data curation, S.L.; writing--original draft preparation, J.J.; writing--review and editing, M.S.M.; visualization, Z.L.; supervision, M.S.M.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
Our research is supported by the National Natural Science Foundation of China, No.51775270. Financial support provided by the project of Qatar National Research Fund under the contract NPRP11S-1220-170112 is also gratefully acknowledged.  
The authors declare no conflict of interest.  
Figure 16: The identification results of constant frequency sinusoidal load.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Wavelength and Propagation Direction Calculation of \(n\)th Ocean Waves
#### 2.5.3 180\({}^{\circ}\) Ambiguity Removal of Wave Propagation Direction  
At present, cross-spectra is an important method to removal of 180\({}^{\circ}\) ambiguity [26]. In order to ensure high signal-to-noise ratio, the wavelength estimation needs to satisfy the time separation between two individual-look SAR images is short enough, while the propagation direction estimation needs to satisfy that is long enough [27]. It needs to calculate two SAR image cross spectra, and the time separation optimization is also a complex problem [27]. Compared with the cross-spectra method, the proposed method is simpler and does not require any additional processing to remove 180\({}^{\circ}\) ambiguity using the optimum focus setting.  
The relationship between the optimum focus setting and the wave propagation direction is explained below at first. When SAR observes the ocean, the Doppler frequency shift caused by
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 6 Results and Discussion
### Baseline Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Valid** & **Test** & **Unique Syllables** \\ \hline LSTM-syl (sum) & 39.082 & 39.082 & 18,440 \\ LSTM-syl (concat) & 59.399 & 63.625 & 18,440 \\ LSTM-syl (cnn) & 37.509 & 38.935 & 18,440 \\ Bi-LSTM (char) & 68.968 & 70.976 & – \\ Bi-LSTM (trigram) & 62.181 & 64.548 & – \\ \hline LSTM-syl (sum + WFSE) & 54.189 & 58.189 & 210 \\ LSTM-syl (concat + WFSE) & 87.546 & 95.906 & 210 \\ LSTM-syl (cnn + WFSE) & **43.567** & **45.867** & 210 \\ Bi-LSTM (WFSE) & 63.196 & 65.548 & 213 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The baseline results for the LSTM-syl [21] and Bi-LSTM [25]. Our Model (WFSE) posts very competitive results to state-of-art on the LSTM-syl (cnn + WFSE) model. The unit of measure is perplexity; the lower is the value, the better is the representation. The bolded values represent the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Word** & **Characters** & **Trigrams** & **Syllables** \\ \hline walikuja & w, a, l, i, k, u, j, a & wal, ali, lik, kuj, uja, walikuja & wa, li, ku, ja \\ atalipa & a, t, a, l, i, p, a & ata, tal, ali, ipa, atalipa & a, ta, li, pa \\ mchezo & m, c, h, e, z, o & mch, che, hez, ezo, mchezo & m, che, zo \\ mimi & m, i, m, i & mim, imi, mi, mimi & mi, mi \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of how Swahili words are split when characters, trigram and syllables are used as compositional components. The first two are verbs with subject prefix, tense prefix and root while the last two are noun and pronoun, respectively.
## 4 Classification Algorithms
### Normal Bayes (NB)  
Normal Bayes (NB) is a simple probabilistic classification model that assumes the normal distribution of the feature space for each class. This means that the whole data distribution function is assumed to be a Gaussian mixture with one component per class [31]. Based on selected training instances, the NB algorithm estimates mean vectors and covariance matrices for each class and utilizes them for predictions. A detailed mathematical description of the classification model can be found, for example, in Fukunaga [32].
# New Candidate Multicomponent Chalcogenide Glasses for Supercontinuum Generation
## 5 Nonlinear Optical Property Variation
The relationship is consistent with our experimental data where an increase in PbSe concentration from 20 to 40 mol% induces a red-shift of band-edge energy (dominated in part by scatter loss), an increase in linear refractive index, and an increase in nonlinear refractive index. These data are included for comparison in Table 3, along with a calculation of the two bulk glass' FOMs which are the same order of magnitude with that of Ge with high nonlinearity in MWIR [78]. As one can see, optical property tuning with the parent bulk glasses (prior to any further conversion to a glass ceramic) offers a variety of tailorable options for SC material design. Characterization of these same attributes in bulk glasses that have been partially converted to transparent optical nanocomposites are ongoing. Parallel studies to quantify other thermal and mechanical properties [55] in identical bulk glass samples are also in progress. To assess how GAP-Se thin film glasses behave differently compared to their bulk counterpart in nonlinear regime, endeavors to explore and quantify the nonlinearity of films are expected to exhibit similar trends; however, we expect the magnitude of such changes and variation are likely different. These experiments, currently in progress, will help to define where further insight into the influence of material dimension on optical properties can be found. It is envisioned, as seen in planar base GAP-Se films, that similar tailoring of the resulting effective refractive index (while maintain low loss) and dispersion in a post-processed glass ceramic (dependent on volume fraction of phases formed) will enable effective nonlinear property modification as well. These enhancements, with a corresponding attention to the loss induced by a secondary phase will need to be evaluated, as a trade-off in assessing whether or not GAP-Se materials can be seriously considered as viable options for SC media.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
###### Abstract  
The rapid evolution of intelligent vehicle technology has significantly advanced autonomous decision-making and driving safety. However, the challenge of predicting long-term trajectories in complex urban traffic persists, as traditional methodologies usually handle spatiotemporal attention mechanisms in isolation and are typically limited to short-term trajectory predictions. This paper proposes a Habit-based Temporal-Spatial Attention Long Short-Term Memory (HTSA-LSTM) network, a novel framework that integrates a dual spatiotemporal attention mechanism to capture dynamic dependencies across time and space, coupled with a driving style analysis module. The driving style analysis module employs Sparse Inverse Covariance Clustering and Spectral Clustering (SICC-SC) to extract driving primitives and cluster trajectory data, thereby revealing diverse driving behavior patterns without relying on predefined labels. By segmenting real-world driving data into fundamental behavioral units that reflect individual driving preferences, this approach enhances the model's adaptability. These behavioral units, in conjunction with the spatiotemporal attention outputs, serve as inputs to the model, ultimately improving prediction accuracy and robustness in multi-vehicle scenarios. The model was evaluated by using the NGSIM dataset and real driving data from Wuhan, China. In comparison to benchmark models, HTSA-LSTM achieved a 20.72% reduction in the root mean square error (RMSE) and a 24.98% reduction in the negative log likelihood (NLL) for 5 s predictions of long-term trajectories. Furthermore, HTSA-LSTM achieved R\({}^{2}\) values exceeding 97.9% for 5 s predictions on highways and expressways and over 92.7% for 3 s predictions on urban roads, highlighting its excellent performance in long-term trajectory prediction and adaptability across diverse driving conditions.  
t
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 3 Collection Methodology
### Sigfox Antwerp

Table 3: The structure of the urban Sigfox dataset. Every row represents a Sigfox message and indicates its receiving base stations (BS), the receiving time of the message (RX Time) and the GPS coordinates at transmission time.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 4 Results and Discussion  
### Static Threshold  
The empirical analysis involved using 1000 samples to assess the efficacy of warning the driver when either the ECR or MAR met the drowsiness criteria. The static threshold method required static frames for testing, hence the use of 1000 samples to validate this study. Sensitivity and accuracy were the primary metrics evaluated for this method. Figure 13 demonstrates instances of the static threshold algorithm, depicting ECR values (0.03 and 0.06) which are lower compared to the threshold and, hence, identified as indicating drowsiness.  
Figure 14 illustrates MAR values of 1.0 and 0.86, categorized as drowsy due to surpassing the predefined threshold of 0.4.  
Figure 13: Driver with closed eyes.  
Figure 15 depicts drivers who are both yawning and have closed eyes. The first image displays the values ECR: 0.08 and MAR: 0.65, while the second image shows the values ECR: 0.14 and MAR: 0.57.  
Table 3 presents the confusion matrix based on one-thousand (1000) data samples. Among these samples, 627 were labeled as 'drowsy'. Of these, 605 were correctly classified as 'drowsy' and 22 were incorrectly classified as 'not drowsy'. Also, there were 373 images labeled as 'not drowsy'. The classifier identified 289 of these correctly as 'not drowsy', misclassifying 84 as 'drowsy'.  
We achieved a sensitivity of 96.50% and an accuracy of 89.40% in our evaluations. A sensitivity of 96.50% indicates that, out of a sample of one-hundred (100) 'drowsy' instances, approximately 96 images were correctly predicted as 'drowsy', with four (4) misclassified. Additionally, the accuracy was satisfactory, as out of a total of one-hundred (100) samples, including both 'drowsy' and 'not drowsy' images, the classifier accurately identified approximately eighty-nine (89.40) as either 'not drowsy' or 'drowsy'.  
Figure 14: Driver yawning.  
Figure 15: Driver exhibits both yawning and eyes closed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted State**} \\ \cline{2-4}
**Total Samples = 1000** & \multicolumn{1}{c}{**Drowsy**} & \multicolumn{1}{c}{**Not Drowsy**} \\  & & **689** & **311** \\ \hline \multirow{4}{*}{**Tot**} & Drowsy & \multirow{2}{*}{605} & \multirow{2}{*}{22} \\  & & 627 & \\ \cline{1-1} \cline{2-4}  & Not drowsy & & \\ \cline{1-1} \cline{2-4}  & 373 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for 1000 samples.