esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 2 Background
Conventionally, questionnaires of self-assessment prevail in harnessing these attributes. Psychophysiological measures [1], such as Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR), etc., have been commonly used for probing _cognitive_ cues in the past decades thanks to the growth of these technologies. In this review, we differentiate **cues** and **features**, which are often interchangeable in other works. Cue refers to social, physiological, or appearance-based signals. Features stand for digitized attributes of cues to be involved in mathematical computations to derive engagement. We believe distinguishing them helps take the challenge out of understanding which are conceptual and which can be used in computations. Our review concentrates on different sensors' strengths and characteristics in acquiring cues and extracting features. We pay attention to engagement type, among _behavioural_, _emotional_, and _cognitive_, to draw practical implications of deploying sensors in various real-world applications.
## 1 Introduction
[17] introduced the application of a GPU-accelerated Support Vector Machine (SVM) in predicting ionospheric TEC with a prediction step size of one hour. In addition to the above, many practical products have been formed based on Generative Adversarial Networks [18], ANNs [19], and Recurrent Neural Networks [20]. These studies aim to improve the accuracy of ionospheric characteristic parameter analysis by developing new models or using new data. Some of these results have been incorporated into international standard models or national standards. To further improve the accuracy of TEC predictions, this paper constructs a prediction model based on Support Vector Regression (SVR). The structure of this paper is as follows: Section 2 provides an overview of the SVR method; Section 3 introduces the ionospheric TEC and solar activity index; Section 4 applies SVR for modeling and completes the model training; Section 5 compares the established SVR model with the IRI and the SML models and tests the SVR model; Section 6 summarizes the findings of this study.
# Tensor Completion Based on Triple Tubal Nuclear Norm
## 4 TriTNN-Based Tensor Completion  
### Problem Formulation  
Let \(\mathbf{\mathcal{L}}^{*}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) be the true, but unknown tensor to be completed. Suppose only a small fraction of its entries are observed and the observations are corrupted by small dense noise. Let \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) denote the observed noisy tensor of \(\mathbf{\mathcal{L}}^{*}\). Then, we have the following observation model:  
\[\mathbf{\mathcal{T}}=(\mathbf{\mathcal{L}}^{*}+\mathbf{\mathcal{E}})\odot\mathbf{\mathcal{O}}, \tag{19}\]  
where \(\mathbf{\mathcal{E}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) is the noise tensor with element-wisely i.i.d. Gaussian noise, \(\odot\) denotes the element-wise multiplication and \(\mathbf{\mathcal{O}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) denotes the binary tensor whose entry \(\mathbf{\mathcal{O}}_{ijk}=1\) if the \((i,j,k)\)-th entry is observed, otherwise \(\mathbf{\mathcal{O}}_{ijk}=0\). The goal is to estimate \(\mathbf{\mathcal{L}}^{*}\) given noisy observation \(\mathbf{\mathcal{T}}\) from observation Model (19).  
We estimate \(\mathbf{\mathcal{L}}^{*}\) by simultaneously exploiting the tube, row and column redundancy in a circular way through minimizing the proposed triple tubal nuclear norm. Specifically, we come up with the following problem:  
\[\begin{array}{rl}\min_{\mathbf{\mathcal{L}}}&\|\mathbf{\mathcal{L}}\|_{\times}\\ \text{s.t.}&\|\mathbf{\mathcal{O}}\odot(\mathbf{\mathcal{T}}-\mathbf{\mathcal{L}})\|_{ \text{F}}\leq\epsilon,\end{array} \tag{20}\]  
where parameter \(\epsilon>0\) denotes the noise level. The motivation is to recover \(\mathbf{\mathcal{L}}^{*}\) by choosing the tensor with the smallest TriTNN from a hyper-ball in \(\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) with radius \(\epsilon\). It is well known that Problem (20) in the form of convex minimization with a bounded norm constraint is equivalent to the following unconstrained problem [23]:  
\[\min_{\mathbf{\mathcal{L}}}\ \ \frac{1}{2}\|\mathbf{\mathcal{O}}\odot(\mathbf{\mathcal{T}}-\bm {\mathcal{L}})\|_{\text{F}}^{2}+\tau\|\mathbf{\mathcal{L}}\|_{\times}. \tag{21}\]  
where \(\tau>0\) is the regularization parameter.
## 5 Experimental Results and Comparison
### Quantitative Performance Comparison with Baseline Methods

Table 3: Evaluation results of our method and previous methods for the video description generation on the MSR-VTT dataset. Here, “-” indicates the metric is not considered. All the numerical terms are in percentile format. Texts with bold marks are the best scores. rics in the MSVD dataset. However, the performance increased by 5.2% and 2.6% in the MSR-VTT dataset. * DenseLSTM [17]: Attention-based dense LSTM was considered for video captioning. The model had a backward cell connected with a forward cell. The exhibition of this model did not beat our proposed model. Our model increased by 0.8%, 2.8%, and 14.3% in B-4, M, and R-L measurements on the MSVD dataset. Likewise, it increased 3.1%, 2.1%, and 6.5% on the MSR-VTT dataset. * CAM-RNN [20]: CAM encoded the visual and literary highlights, and RNN kept up the decoder state to create video captioning. Our model, Att-BiL-SL (contrasted and the best score), achieved a better outcome than CAM-RNN, with 8.8%, 2.3%, 32.6%, and 2.7% increments in B-4, M, C, and R-L, separately (MSVD). Our model built the exhibition with 6.5%, 0.8%, 10.5%, and 1.6% on B-4, M, C, and R-L measurements (MSR-VTT), separately. * VRE [23]: The proposed method with refocused RNN with spatial-visual features was introduced for better video captioning. The method outperformed on B-4 and R-L metrics with 43.2% and 62.0% in the MSR-VTT dataset. Contrasted with this model, our strategy performed better than VRE, with execution increases of 1.4%, 0.2%, and 0.2% on M, C, and R-L measurements in the MSVD dataset. Nonetheless, the presentation incremented by 0.7%, and 1%, on M and C in the MSR-VTT dataset. * MR-HRNN [25]: The proposed structure ready to perceive human-related activities with objects during sports. The structure also introduced a pose attribute detection module and a description generation module. In the MSVD dataset, our model expanded the exhibition by 0.7%, 3%, and 17.3% on B-4, M, and C measurements. In the MSR-VTT dataset, the exhibition expanded by 5%, 3.1%, and 15.8% on B-4, M, and C, individually. * STA-FG-RC [32]: The method with semantic, temporal attention for a video description in textual formation. The model can expressly join with the high-level visual idea to create temporal attention. Our method performed better than STA-FG-RC, with performance gains of 1.2% on M metrics in the MSVD dataset. However, the performance increased by 0.4% and 1.3% in the MSR-VTT dataset. * Our model, Att-BiL-SL (X + I3D), scored 51.2%, 35.7%, 86.9%, and 72.1% on B-4, M, C, and R-L, respectively, in the MSVD dataset. Moreover, the model scored 41.2%, 28.7%, 49.3%, and 60.4% in the MSR-VTT dataset.
## 4 Results
### Preliminary Optimizations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Variation in the Max Number of Stirling Groups**} \\ \hline n\({}^{\text{a}}\)”String max & ORC & Stirling & Absorber & HTW storage & & & Stirling & ORC & & Boiler & Boiler & \\ (gap”s) & tot\_size & tot\_size & size & size & & FuelTot & & Penalty & production & production & & Steam & HTW Input \\ \hline kW & kW & kW & kW & kWh & kWh & 10m\({}^{3}\) & kWh & kWh & kWh & kWh & kWh \\
10 (1.0) & 600 & 500 & 8000 & 4953 & 10,292,400 & 46.13 & 58,853 & 10,757 & 46,558 & 54,677 & 51,557 \\
20 (1.0) & 150 & 1000 & 8000 & 4953 & 10,214,600 & 53.13 & 100,791 & 3132 & 59,502 & 56,554 & 52,263 \\
30 (1.5) & 0 & 1400 & 8000 & 4953 & 10,213,700 & 60.13 & 124,754 & 0 & 58,828 & 50,171 & 57,917 \\
40 (2.0) & 0 & 1700 & 8000 & 4953 & 10,149,700 & 66.13 & 121,791 & 0 & 57,434 & 51,662 & 56,622 \\ \hline \multicolumn{11}{c}{**Variation in the max number of ORC groups**} \\ \hline n\({}^{\text{a}}\)”ORC max & ORC & Stirling & Absorber & HTW storage & & & & & Boiler & Boiler & \\ (gap”s) & tot\_size & tot\_size & tot\_size & size & & FuelTot & & Penalty & production & production & Steam & HTW Input \\ \hline kW & kW & kW & kW & kWh & kWh & 10m\({}^{3}\) & kWh & kWh & kWh & kWh & kWh \\
1000 (1.0) & 150 & 1000 & 8000 & 4953 & 10,214,600 & 53.13 & 100,791 & 3132 & 59,502 & 56,554 & 52,263 \\
300 (1.0) & 300 & 1000 & 8000 & 4953 & 10,212,300 & 54.13 & 103,528 & 1043 & 73,204 & 59,832 & 42,410 \\
15 (1.0) & 300 & 1000 & 8000 & 4715 & 10,216,500 & 53.65 & 101,786 & 3880 & 60,541 & 54,763 & 53,023 \\
20 (1.0) & 150 & 1000 & 8000 & 4953 & 10,219,100 & 53.13 & 107,468 & 2841 & 76,599 & 56,537 & 54,280 \\ \hline \multicolumn{11}{c}{**Variation of the max Input/Output flow value of the HTW Storage**} \\ \hline in/out max & ORC & Stirling & Absorber & HTW storage & & & & & Boiler & Boiler & \\ (gap”s) & tot\_size & tot\_size & size & & & & & & & & \\ \hline kW & kW & kW & kWh & kWh & 10m\({}^{3}\) & kWh & kWh & kWh & kWh & kWh \\
1000 (1.0) & 150 & 1000 & 8000 & 4953 & 10,214,600 & 53.13 & 100,790 & 3132 & 59,502 & 56,554 & 52,264 \\
3000 (1.0) & 300 & 1000 & 8000 & 9886 & 10,179,700 & 64.00 & 103,380 & 2238 & 74,170 & 37,This preliminary evaluation shows that no steam storage is installed, while the hot water storage reaches an overall value of about 5000 kWh of maximum energy content, evidently linked to the value used to limit the heat flow exchanged. In fact, by raising this limit (the third part of Table 4), the maximum energy content of the HTW storage also rises. It should be noted that the electrical production by the ORC also increases with the size and thermal flow of the HTW storage at 90 \({}^{\circ}\)C, highlighting how the latter also partially contributes to feeding of the ORC groups. These are in effect connected on the line of the HTW thermal vector, which can exchange heat also with the storage. Taking into account these preliminary results, the ranges for the optimization variables managed by the external level reported below in Table 5 have been considered.
# Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
## 2 Materials and Methods
### Application of Models
#### 2.3.2 Application of Recurrent Neural Networks  
A. Data preprocessing  
The data scale influences the processing of deep neural networks, mainly when using the sigmoidal or hyperbolic tangent activation functions. An alternative standardization was used to scale the data to give an absolute minimum and maximum value for each variable with intervals [\(-\)1, 1] and [0, 1]. To scale the random variable \(y_{i}\), we used Equation (3):  
\[z_{i}=\frac{y_{i}-\min(Y)}{\max(Y)-\min(Y)} \tag{3}\]  
where \(\min\)\((Y)\) and \(\max\)\((Y)\) are the minimum and maximum values of the vector \(Y\), and \(z_{i}\) is scaled between 0 and 1.  
With the scaled data, we proceeded to build the RNN using Elman cells and LSTM. Each network parameter was then adjusted (activation functions, loss functions to minimize, number of layers, number of neurons in each layer, etc.) until better results were obtained.  
The dropout technique was applied to the neural networks to avoid obtaining a smaller network due to overtraining [31], a process where randomly selected sets of neurons during the training phase are ignored with a probability of \(1-p\). If the parameter is closer to 0, fewer neurons are deactivated, and if it is closer to 1, more variables are deactivated. The optimal dropout value obtained for this study was 0.2. Poorer results were obtained when not using the dropout technique.  
B. Elman Cell  
This network consisted of 110 time steps (inputs) to forecast the window steps (outputs) and featured two layers with 3 and 2 neurons in each. We used the R software v. 3.5.3 to code the Elman cell.  
R code: Elman cell  
_fit_elman \(\leftarrow\) elman(x = inputs, y = outputs, size = c(3,2), maxit = 7000, learnFuncParams = c(0.2))_  
Here, maxit = 7000 (the number of iterations to train the model), and learnFuncParams = 0.2 (network training speed). Figure 5 shows the network error.  
C. LSTM Cell  
This network consisted of two hidden layers with 50 units in each, a \(relu\) activation function, and an output layer (Dense). In the first layer, the neurons have three-dimensional outputs (return_sequences = True), input variables (input_shape), and independent term bias (use_bias = True). We used the software Python v. 2.7.13 to code the LSTM cell.  
\begin{tabular}{l} \hline \hline Python code: LSTM \\ \hline _model.add(LSTM(50, \\ _activation = \"relu\", \\ _return_sequences = True, \\ _input_shape = (n_steps_in, n_features), \\ _use_bias = True)_model.add(LSTM(50, activation = \"relu\"))_model.add(Dropout(0.2)) \\ _model.add(Dense(n_steps_out)) \\ _model.compile(optimizer = \"adam\", loss ='mae')_ \\ \hline \hline \end{tabular}  
In this case, the mean absolute error (MAE) was used as the loss function for the network, and (adam) was used as an optimizer. Figure 6 shows the behavior of the error as a function of the number of iterations. Here, the error is large for iterations one and two and stabilizes starting from iteration three.
## 2 Materials and Methods
### Common Use Case Scenarios  
For a more efficient presentation of the functionality offered by the interface, the three most common use case scenarios are presented. In the results section, the implementation of the functionality in terms of the user experience (UX) choices, and the technical details are presented.  
Scenario 1: A user submits a file and uses the toolbox to determine its authenticity.  
In this common scenario, a user submits a file by uploading an audio or video file or by providing a YouTube link. After the analysis takes place on the server side, the visualizations are provided to the user. The user locates the points in time where inconsistencies are observed, listens to the audio, and makes a determination concerning the authenticity of the file, as was explained in Section 2.2 concerning the user-centered computer supported design. The user is then asked whether they are willing to contribute the file and their decision to the database. If they decide not to contribute, the file is deleted.  
Scenario 2: A user is unable to decide and asks for help from the community.  
The user submits the files, and, after they investigate the visualizations provided by the toolbox, they are unable to make a decision on the authenticity of the file. The user then decides to upload the file to the dataset unlabeled, so that it can be accessed by other users.  
Scenario 3: A user browses the database to annotate files.  
A user wants to contribute by annotating files that are already in the dataset. The interface provides randomly selected files from the database, along with the visualizations that come as outputs of the analysis. The user investigates the visualizations and makes a decision concerning the authenticity of the file, then chooses to submit their decision. Their decision is saved in the database, containing a label of the file (tampered/not tampered), and, optionally, the point in time where forging was detected and a short justification. The media file will still be available to other users for investigation after the annotation process. This means that a file may have multiple labels from different users, which is a common practice in crowdsourcing projects, since the input of one user cannot be considered totally reliable on its own.
# A Waveform and Velocity Ambiguity Resolution Method for Corner Radar
## 2 Materials and Methods
Assuming a frame period of 50 ms, and with two modes' waveforms configured within a single frame, all three modes can be accommodated within two frames. The waveform parameters can be determined by referring to Figure 3. The practical bandwidth and frame length are formulated based on the system's demand for velocity resolution (\(\Delta V\)) and range resolution (\(\Delta R\)). \(N_{FFT}\) is calculated according to \(R_{max}\). \(T_{s}\) is determined based on the sampling rate \(Fs\), and the FM slope \(\mu\) is derived. The frame length, represented by the number of chirps in a frame (\(N_{dFFT}\)), is determined considering that each chirp comprises the rising edge, descending edge, and delay time. Finally, the transmit bandwidth is determined by the utilization ratio. Figure 2: Waveform configuration in multiple modes. Corner radar necessitates superior range resolution compared to front radar due to its specific operational requirements. Consequently, this paper presents a waveform tailored specifically for corner radar, with detailed parameters outlined in Table 1.  
Corner radar primarily serves the purpose of detecting the side and oblique front areas of the vehicle, facilitating functionalities such as lane change assistance and parking assistance. For parking applications, where the radar operates predominantly in ultra-close range and low-velocity scenarios, a high-resolution waveform is provided in this paper. This waveform utilizes a large bandwidth to cater to the specific requirements of parking scenarios. To simplify the description of the velocity disambiguation method discussed later in the paper, the waveform envelope only illustrates the relationship along the time axis. Leveraging the concept of the Chinese Remainder Theorem, this paper proposes a matching method termed AB waveform disambiguation, based on dual-frequency waveforms. This method utilizes information from frames preceding and succeeding the target point to determine range, velocity, and azimuth information.
# Tensile Test of Human Lumbar Ligamentum Flavum: Age-Related Changes of Stiffness
## 1 Introduction  
The ligament flavour is a structure that covers the posterior side of the spinal canal. It adheres to the upper and lower lamina, maintains tension in both the resting and moving state, and maintains pressure to strengthen the intervertebral disc. As a result, it adapts and controls the motion of the spine, which protects the spinal cord by buffering action against rapid external force [1, 2].  
Lumbar spinal canal stenosis (LSS) is a degenerative disease with a predilection for middle-aged and elderly patients [3, 4]. Although several factors such as osteophytes, intervertebral disc herniation, degenerative slipping and instability are involved in the development of spinal canal stenosis, changes in the ligamentum flavour associated with aging is one of the most important factors. Ligamentum flavum is thickened and buckled inside the spinal canal as the disc height decreases due to degenerative changes [5, 6]. Furthermore, hypertrophy of the ligamentum flavour is thought to be most involved in the development of LSS [7].  
To date, there have been several reports describing the histological changes in the ligamentum flavour according to age and disease [8, 9, 10, 11, 12, 13]. Hypertrophy of the ligamentum flavum seems to be strongly associated in the development of LSS [7]. Few reports have described the biochemical changes that cause the hypertrophy [14, 15]. Although it is natural to consider that the mechanical properties of ligamentum flavour can be altered due to ligamentous hypertrophy, only few studies have mentioned on this point [1, 16, 17].  
In a cadaver ligamentum flavum tensile test experiment, [PERSON] et al. reported that values for stress at rupture decreased with age and showed significant age correlation [1]. [PERSON] also reported in cadaver experiments that the modules of elasticity at rupture decreased significantly with age [16]. Recently, [PERSON] et al. reported that healthy ligaments were significantly stiffer than LSS ligaments measured by nanoindentation, using fresh ligaments harvested during surgery [17]. However, to date, there are no reports demonstrating tensile tests with fresh human ligamentum flavum and evaluate the factors that affect the mechanical properties. Therefore, in this report, we conducted tensile tests of ligamentum flavum harvested from live patients during decompression surgery, and evaluated the mechanical properties. We focused on the stiffness of the ligaments and evaluated the factors that associated with the change in stiffness.
## 3 Results and Discussion
### Single-Factor Experiments
#### 3.1.4 Effect of the Microwave Power  
The microwave power was found to have a significant effect on the yields of the target compounds from the powdered leaves of _T. chinensis_ var. _mairei_ (see Figure 3C). When the microwave power was increased from 300 to 500 W, the yields of the target compounds increased significantly. This increase in the yields was attributed to the fact that microwave irradiation energy can enhance the extent to which the solvent penetrates into the solid matrix of the powdered sample [35]. In this way, the solvent can be efficiently delivered to the target compounds through molecular interaction with the electromagnetic field, allowing for the rapid transfer of energy to the solvent and matrix and the facile extraction of the dissolved components [36]. However, the too high power may lead to loss of volatile oil and degradation of polysaccharides [37]. When the microwave power was beyond 500 W, the yields of essential oil and polysaccharides leveled off, which is probably because that the extraction and degradation of target compounds arrive a balance at this circumstance. Hence, the optimal microwave power was determined to be 500 W and used in the subsequent reactions.
# Automatic Processing of Historical Japanese Mathematics (Wasan) Documents
## 2 Materials and Methods
### Data
#### 2.1.2 Kanji Databases  
The use of kanji in Wasan documents is unique and itself of historical importance. The script in which most Wasan documents were handwritten, called \(\mathscr{W}\)\(\times\) (\"kanbun\" or \"chinese writing\"), was initially only used in Japan by highly educated scholars and only became popular during the Edo period. The characters in Wasan documents have many similarities with modern Japanese kanji, but also present important differences. For example, the \(\frac{\text{{i}}\text{{i}}\text{{i}}}{\text{{i}}}\) symbol, that today is generally thought to mean \"string\", is used in Wasan documents to indicate the hypotenuse of a triangle. Conversely, the \(\mathbb{H}\) kanji, used to indicate the technical term for \"circle\" in Wasan documents, has acquired other meanings since then and indicates, for example, the \"yen\" Japanese currency. Furthermore, some kanji have also changed their writing since the Edo period. See Figure 3 for some examples. This is exemplified by the \"ima\" kanji. Not only is this kanji of special importance in the analysis of Wasan documents, but it is also a kanji that has seen the way it is written from the Edo period to modern times. Additionally, given its relative simplicity, it is kanji that can be confused with other similar characters easily.  
Thus, we needed datasets containing the largest possible number of example of handwritten characters adequately classified and matching those appearing in Wasan documents. Given the large number of existing kanji characters (currently the japanese government recommends that all adults should be able to recognize all the kanji in a list containing 2136 characters for the purpose of basic literacy) it is impractical to produce these databases for a research project such as the current one so it is necessary to rely on publicly available datasets. In this study, we used two of them: the ETL dataset [19] and the Kuzushiji dataset [23]. The first is a large, balanced dataset made up of over 3036 modern kanji, each with exactly 200 examples. The second contains examples of 3832 different kanji from different texts of different historical periods and is heavily unbalanced. See Section 2.6 for details on how these two datasets were used for DL classification.