esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Comparison of Results
### Comparison of Acceleration Response Time Histories
Furthermore, calculating RMS and RMQ from the acceleration time history responses confirmed that the differences in seismic behavior of the electrical cabinet between the experimental and numerical results effectively capture the effects of anchorage damage and impulsive characteristics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{1}{c}{**Location**} & \multicolumn{3}{c}{**EQ1**} & \multicolumn{3}{c}{**EQ2**} & \multicolumn{3}{c}{**EQ3**} \\  & \multicolumn{1}{c}{**X**} & \multicolumn{1}{c}{**Y**} & \multicolumn{1}{c}{**Z**} & \multicolumn{1}{c}{**X**} & \multicolumn{1}{c}{**Y**} & \multicolumn{1}{c}{**Z**} & \multicolumn{1}{c}{**X**} & \multicolumn{1}{c}{**Y**} & \multicolumn{1}{c}{**Z**} & \multicolumn{1}{c}{**X**} & \multicolumn{1}{c}{**Y**} & \multicolumn{1}{c}{**Z**} \\ \hline \multicolumn{2}{c}{} & A1 & & 0.160 & 0.153 & 0.114 & 0.314 & 0.303 & 0.249 & 0.455 & 0.439 & 0.382 \\ \hline \multirow{4}{*}{RMS (g)} & \multirow{4}{*}{Test} & A2 & 0.205 & 0.207 & 0.136 & 0.406 & 0.421 & 0.261 & 0.716 & 0.763 & 0.498 \\  & & A3 & 0.254 & 0.268 & 0.132 & 0.517 & 0.559 & 0.258 & 1.002 & 1.174 & 0.583 \\  & & A4 & 0.302 & 0.323 & 0.133 & 0.631 & 0.692 & 0.267 & 1.328 & 1.501 & 0.620 \\ \cline{2-11}  & \multirow{4}{*}{Analysis} & A2 & 0.206 & 0.200 & 0.144 & 0.415 & 0.405 & 0.300 & 0.604 & 0.593 & 0.453 \\  & & A3 & 0.241 & 0.261 & 0.132 & 0.480 & 0.527 & 0.274 & 0.699 & 0.776 & 0.412 \\  & & A4 & 0.275 & 0.319 & 0.133 & 0.545 & 0.642 & 0.273 & 0.797 & 0.950 & 0.408 \\ \hline \multirow{4}{*}{\(\mathrm{R_{RMS}}\) (g/g)} & \multirow{4}{*}{Test} & A2 & 1.289 & 1.307 & 1.260 & 1.323 & 1.337 & 1.205 & 1.329 & 1.352 & 1.188 \\  & & A3 & 1.507 & 1.705 & 1.153 & 1.529 & 1.740 & 1.100 & 1.536 & 1.767 & 1.079 \\  & & A4 & 1.716 & 2.080 & 1.159 & 1.738 & 2.122 & 1.095 & 1.752 & 2.165 & 1.068 \\ \cline{2-11}  & \multirow{4}{*}{Analysis} & A2 & 1.278 & 1.352 & 1.187 & 1.296 & 1.391 & 1.047 & 1.574 & 1.738 & 1.304 \\  & & A3 & 1.584 & 1.747 & 1.152 & 1.649 & 1.848 & 1.036 & 2.201 & 2.673 & 1.526 \\  & & A4 & 1.885 & 2.112 & 1.162 & 2.010 & 2.286 & 1.072 & 2.919 & 3.419 & 1.625 \\ \hline \multirow{4}{*}{PD (\%)} & \multirow{4}{*}{Analysis} & A2 & 0.84 & 3.38 & 5.98 & 2.08 & 3.94 & 14.03 & 16.91 & 25.01 & 9.32 \\  & & A3 & 4.97 & 2.41 & 0.08 & 7.54 & 6.02 & 5.94 & 35.63 & 40.84 & 34.29 \\  & & A4 & 9.39 & 1.55 & 0.25 & 14.52 & 7.47 & 2.14 & 49.95 & 44.91 Figure 17: Comparison of RMS and RMQ: (**a**) EQ1; (**b**) EQ2; (**c**) EQ3. Figure 18: Comparison of RMS ratio and RMQ ratio: (**a**) \(R_{\rm RMS}\); (**b**) \(R_{\rm RMQ}\).
## 4 Open Issues: Vulnerable People and Special Environments
[178] summarized the impact on patients of HVAC-related parameters such as indoor air temperature, pointing out that medications and drugs affect the patient's thermoregulatory system. As discussed by [PERSON] et al. in their recent review on hospital environments [179], only a small number of papers investigated the relationship between patients with specific conditions and the thermal environment, but thermal comfort dependency on patient category still has to be explored. The need to reconcile thermal comfort requirements of different types of occupants, including operators, makes the identification and control of thermal comfort conditions particularly problematic in practice. A typical example is the operating room [180], where the patient would need a warm environment due to being under anesthesia and wearing only a gown, whereas the medical staff prefer a cool, well-vertilated room due to the mentally and physically demanding procedures they have to sustain for a long time. It is worth noting that the full comfort spectrum should be evaluated in sensitive indoor environments. This suggests considering a wide range of possibly intertwined indicators, and to apply diverse control strategies, even at the design stage. [PERSON] et al. [181] modeled the effect of internal building covering materials in a region with very high relative humidity throughout the year; the authors used the \"percentage of dissatisfied due to decreased respiratory cooling\" [182] and the \"percentage of persons dissatisfied with the air quality\" [183] as local comfort indicators, and related the results to expected energy consumption. Although the paper focused on office buildings, this is an example of an integrated strategy that could be well suited also to healthcare facilities. The cases presented above represent some of the situations in which a wise assessment of thermal comfort and the implementation of adequate indoor environmental control systems can make a difference for the occupants. In these and many other cases, however, designers have limited support from the literature and the reference standards; thus, they must perform specific investigations and resort to their experience.
# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 2 Materials and Methods
The curvature rate was calculated and inflection points were evaluated on its derivative, which enabled the identification of greenup, maturity, senescence and dormancy dates. The uncertainty of phenological metrics was evaluated according to the bootstrap approach proposed by [PERSON] et al. [36] and implemented in the phenopix R package [36]. Fitting was applied recursively to randomly-noised original data, providing an ensemble of fitted curves and derived metrics, allowing an uncertainty estimate of both fitting parameters and phenological metrics. The uncertainty measure was the width of the interval, which contains 95% of the metric values. Two hundred NDVI time series were modelled for each availability pattern, and the uncertainty values were averaged (see lower part of Figure 1). Image search and processing in the Google Earth database was performed through the Earth Engine Code Editor ([[https://code.earthengine.google.com/](https://code.earthengine.google.com/)]([https://code.earthengine.google.com/](https://code.earthengine.google.com/)); accessed on 16 February 2021) [23]. Statistical analyzes, graphs and maps elaboration were made using the R software [37]. The R packages raster [38], RStoolbox [39] and phenopix [36] were particularly useful.
## 3 Results and Discussion
### Reactivity of Precursor Compounds in the Maillard Reaction
The effect could be caused by the increased concentration of amino acids (Figure 2), which was already observed during wort boiling [19]. This fact indicates that the FL and ML levels could have already been increased in the final wort before fermentation. As main dicarbonyl compounds, 3-DG (7.9-19.5 mg/L), 3-DGal (1.5-4.7 mg/L), and 3-DM (1.5-9.9 mg/L) were found in concentrations comparable to previous studies [31; 32]. B2 showed slightly higher contents of dicarbonyl compounds. Regarding the 3-DG and 3-DGal content, a decreasing trend during natural beer aging could be observed, as has  
Figure 3: Concentrations of Amadori products ((**A**) ML and (**B**) FL) and dicarbonyls ((**C**) 3-DG, (**D**) 3-DM, and (**E**) 3-DGal) at fresh (FR), forcibly aged (FO), and naturally aged (M3, M6, and M9) beer at different malt modification levels (P1: white bar; P2: grey bar; P3: black bar) at two barley varieties (B1 and B2); \(n=3\). been previously reported [15; 31]. This decrease is caused by the degradation of dicarbonyls, indicating the _de novo_ formation of Strecker aldehydes. Furthermore, dicarbonyl compounds can undergo conversion to 5-hydroxymethylfurfural (HMF), as previously reported during beer aging [15]. Nevertheless, the formation of both dicarbonyls cannot be excluded during aging. Exemplarily, [PERSON] et al. observed the formation of 3-DG and 3-DGal by forced aging model experiments of maltotetraose [31]. Therefore, the degradation trend of both compounds tends to be underestimated. A higher malt modification level showed a consistent increase in 3-DG concentration during natural and forced aging, whereas 3-DGal content was less affected. However, higher 3-DG content could be caused by a stronger formation during word boiling at higher malt modification levels [19; 33]. The resulting higher absolute degradation of 3-DG promotes the _de novo_ formation of Strecker aldehydes during natural beer aging. Furthermore, this effect can decrease flavor instability, as already shown by artificially increased 3-DG content in pale lager beers [15]. Dicarbonyl 3-DM showed a slightly increasing tendency during natural beer aging. This could be caused by a new formation from maltose. This phenomenon has already been described in the aging of pilsner beer [31]. Thus, the participation of 3-DM in Strecker degradation can be confirmed, but the degradation of \(\alpha\)-dicarbonyl can occur simultaneously with the dominant formation during beer aging. In summary, no clear indication for the formation of Strecker aldehydes from 3-DM and Amadori products could be observed. It should be considered that only free dicarbonyls were used for the observation and that only \(\varepsilon\)-terminal Amadori products were quantified. In contrast, the occurring degradation of 3-DG and 3-DGal indicated a possible _de novo_ formation of Strecker aldehydes.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 2 Related Work
### Two-Dimensional vs. Three-Dimensional Object Model and Identification  
Three-dimensional object recognition is to identify and produce the 3D information, such as pose and shape, of some objects from a picture frame or video stream. Many previous works have designed algorithms to identify 3D objects in pictures [4,5,6,29]. However, most 3D object recognition algorithms have high computational complexities and require large memory space. For example, [30]recommends systems with 200 GB free storage, 10 GB memory and 5 GB of GPU memory for running their programs. It may be too costly for low-end IoT devices.  
In comparison, 2D picture recognition is a problem with much more efficient solutions. Unlike 3D objects, 2D pictures usually do not deform in camera views and its features do not suffer physical change when captured from different angles since it is a planar surface. Therefore, the complexity of 2D picture detection and matching algorithms is much lower than 3D object recognition. Traditional 2D pictures matching approaches are usually based on color thresholding [31; 32] or corner/contour feature extraction methods [33; 34]. These matching methods have a high efficiency and accuracy on pictures that have similar sizes and simple backgrounds. However, they may have problems when a picture has illumination change, motion blur and complex background in the view since both color and texture features may be different from the original picture. It is especially difficult when there are other similar pictures or objects inside of a camera view since it may not differentiate similar objects [35].  
To enhance the robustness of picture detection and matching, researchers have recently worked on training 2D pictures as 2D objects using deep learning based methods [36]. Multiple photos that capture the target 2D picture under different angles, illuminations, backgrounds and motions are used as samples for training. In general, deep learning based 2D picture detection has a higher robustness on picture detection than traditional methods, whereas traditional methods have better capabilities on extracting detailed pixel-level patterns and features from a camera view.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
###### Abstract  
Currently, accidents in civil engineering buildings occur frequently, resulting in significant economic damage and a large number of casualties. Therefore, it is particularly important to predict the trend of early damage to building structures. Early structural damages are difficult to correctly identify, and obtaining the required accuracy using a single traditional time-series prediction method is difficult. In this study, we propose a novel method based on the integration of support vector regression (SVR) and long short-term memory (LSTM) networks to predict structural damage trends. First, the acceleration vibration signal of the structure is decomposed using the variational mode decomposition (VMD) method, and the decomposed components are transformed with Hilbert transform to obtain the instantaneous frequency. Then, the instantaneous frequency is input into the LSTM-SVR integrated model for damage trend prediction. The results indicate that the VMD method effectively eliminates modal aliasing and decomposes various intrinsic components of the signal. Compared with individual LSTM and SVR models, the integration model has a higher prediction accuracy for small samples in a chaotic time series that is 6.5%, 2.5%, and 3.7%, respectively. The standard deviation of the absolute percentage error (SDAPE) values of the three operating conditions under the integrated method decreased 0.0994, 0.0869, and 0.0921, which improved the stability of prediction. The mean absolute percentage error (MAPE) of the integration method is an order of magnitude higher than that of the LSTM model.  
support vector regression (SVR); long short-term memory networks (LSTM); integration; structural damage trend; prediction
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 2 Background and Related Work
### Entity Linking
[29] proposed a sequential graph attention network to synthesize the advantages of the graph model and the sequence model, which dynamically encodes the preceding and following entity mentions, and assigns different weights to these entity mentions. The global model of this article refers to the work of [7]. **Entity embedding. ** Entity embedding is a key component in entity linking to avoid manual features and enhance model effects. There is also a lot of work for entity embedding. [PERSON] et al. [30] proposed to map words and entities to the same continuous vector space. They used two models to extend the skip-gram model. The KB graph model uses the link structure in the KB to learn the relevance of entities. The anchor context model aims to use KB anchor text and context words to align vectors so that similar words and entities are close in the vector space. [PERSON] et al. [31] further proposed to jointly learn distributed representations of text and entities. Given a piece of text in the knowledge base, a model is trained to predict entities related to the text; that is, using a large amount of text extracted from Wikipedia and their entity annotations to train the model. [PERSON] and [PERSON] [6] used pre-trained word embeddings and word-entity co-occurrence counts to obtain entity embeddings so that words and entities were represented in the same low-dimensional vector space. [PERSON] et al. [32] proposed a fill-in-the-blank task to learn context-independent entity representations from the text context. [PERSON] et al. [8] proposed incorporating fine-grained semantic information into entity embedding to reduce uniqueness and promote the learning of contextual commonality. [PERSON] et al. [27] used the pre-trained model BERT [33] to generate the representation of words and entities, and the results were greatly improved compared to the previous method. This paper also uses the entity embeddings of [6].
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Current Measurement
#### 4.1.1 Shunt Resistor  
In this measurement method, a low-resistance high-precision resistor in combination with a high precision voltage measurement system is used to determine the current. The resistor is located in the current conducting path so that a voltage drop proportional to the current can be measured. Due to the current flowing through the shunt, a power loss is caused \((i^{2}R)\), which leads to a temperature rise of the shunt resistor. The need to keep the losses as small as possible conflicts with the necessity to select the value of the resistor to be high enough to generate a suitable voltage drop for the measuring system [11]. Furthermore, the thermal drift is an important characteristic of a shunt resistor. If very high measurement accuracy is required, the precision resistor should have a small temperature coefficient and a high long-term stability.  
The method (shunt) can be used to measure direct currents and alternating currents. Further advantages are simplicity and linearity of the measurement system as well as the possibility to achieve high bandwidths. The measurement range is limited by the accuracy of the voltage measurement [10; 31].  
Low-Side vs. High-Side Measuring  
When using the shunt resistor method for measuring current, it is crucial to choose the right placement for the resistor in the current path. If the shunt resistor is located between the positive pole of the battery and the load, one speaks of high-side measuring. When the resistor is placed between the load and ground or the negative pole of the battery, we are talking about low-side measuring.  
The main advantage of the low-side measuring is the low input common mode voltage so that a large number of current sense amplifiers can be used. In addition, only low supply voltages are required for the amplifier circuits and the system can be built in a very simple and low-cost way. Disadvantageous in this arrangement is the disturbance of the ground path that leads to lifting the load from the system ground to a higher level due to the resistance of the shunt. Furthermore, high load current bypassing the low-side monitor--for instance caused by accidental short-circuit--cannot be detected [10].  
In contrast, high-side measuring allows connecting loads to the ground without additional disturbances in the path. Moreover, short-circuits, as mentioned above, can be detected. The need of level shifting the output of the amplifier down to system operating voltage levels is the essential disadvantage of this method. Compared to low-side measuring, the high-side current sense amplifier must be able to withstand high common mode voltages.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Results Based on Parameter Tuning
#### 6.2.2 Route Reliability Results Based on Population Size POP Parameter  
In this section, we analyzed the efficacy of the proposed JMA algorithm with varying population sizes POP; the results of route reliability are shown in Figure 5. Based on the above observations, we fixed \(M_{R}=0.2\) to observe the efficacy of the JMA algorithm in varying population sizes. The population size \(POP=50\) achieves better scores in the fast-run range and stays the same until the long-run range. In realistic scenarios, due to high computational complexity and high demand on memory resources, it is difficult to prolong the long-run range, therefore obtaining the best result within fast-run and mid-run range ensures the efficacy of the algorithm. On the other hand, using a higher population size in metaheuristic algorithms does not achieve better accuracy. It can be easily observed in different batch computations \(\Theta\) over the computational process is achieved as \(\Theta=POP_{\lambda}/POP\), where \(POP_{\lambda}\) is the total number of fitness computations. Therefore, it is clearly noticed that using higher \(POP\) value will minimize the total number of batch evaluations, thus result in a faster computation time that yields reduced route reliability scores. Based on this observation, we noticed that \(POP=50\) achieves more accurate results than different \(POP\) values.  
Figure 5 displays the best mean route reliability score using \(POP=50\) in the mid-range run, whereas a different \(POP\) value achieves the best score in the large-run range. In addition to that, Figure 5 shows the best mean route for \(POP=50\) population size over the set of worst routes. This route was determined in the fast- and mid-run ranges.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Data Acquisition and Preprocessing  
The EEG signals used in this study are selected from PSG recordings stored in European Data Format (EDF) files [31], a widely adopted standard for the storage of biomedical data. These files contain both physiological signals and relevant metadata, including the sampling rate, signal characteristics, and patient information [32]. First, the EEG signals are extracted from the PSG files, and the associated sleep stage annotations are obtained from the corresponding HYP files [33]. These annotations denote the different stages of sleep, such as W, R, S1, S2, S3, and S4, typically labeled in 30 s intervals. The extracted data are then organized into a matrix in which each temporal sample is associated with its corresponding EEG signal and the sleep stage label. Several conditioning steps are applied to align with the AASM guidelines for the classification of sleep stages [18]. As a result, the final sleep stages used for classification are N1, N2, N3, R, and W. Since sleep stages are represented as categorical labels, these labels are converted using ordinal label encoding, assigning a unique integer to each stage [34].  
In addition, the signals are normalized to ensure consistency between different subjects and recordings. Min-max normalization is employed to scale each sample \(x_{i}\) of the EEG signal to a uniform range, typically [0, 1][35]:  
\[x_{i}^{\prime}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}}, \tag{1}\]  
where \(x_{i}\) is the original EEG sample; \(x_{\min}\) and \(x_{\max}\) are the minimum and maximum values of the signal within the recording, respectively; and \(x_{i}^{\prime}\) is the normalized value. This normalization standardizes the signal scales across individuals and constitutes the only preprocessing step applied, in order to avoid extensive feature engineering and to preserve the raw signal input. By minimizing intermediate transformations, this approach aims to retain the intrinsic structure of the signals and enable direct feature learning.