esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Acquisition of Modal Shapes  
In order to obtain the dynamic loads at the location of the premium connection under vibration conditions, a modal analysis of the string containing the connection is first conducted. Traditional modal analysis methods are based on a linear model, where the vibration equation of the string is given as follows [26].  
\[[M]\big{(}\ddot{u}\big{)}+[C]\big{(}\dot{u}\big{)}+[K](u)=f(t) \tag{1}\]  
In the equation:  
[\(M\)]--Mass matrix;  
[\(K\)]--Stiffness matrix;  
[\(C\)]--Damping matrix;  
[\(\ddot{u}\)]--Acceleration vector;  
[\(\dot{u}\)]--Velocity vector;  
[\(u\)]--Displacement vector;  
[\(f(t)\)]--Force vector.  
From Equation (1), the equation of motion for undamped vibration can be obtained as follows:  
\[[M]\big{(}\ddot{u}\big{)}+[K](u)=0 \tag{2}\]  
Assuming the solution of Equation (2) is  
\[(u)=(\varphi)e^{i\omega t} \tag{3}\]  
substituting Equation (3) into Equation (2) yields the characteristic equation, as follows:  
\[\Big{(}[K]-\omega^{2}[M]\Big{)}(\varphi)=(0) \tag{4}\]  
By solving the equation, the eigenvalues \(\omega\), also known as circular frequencies, and the eigenvectors (\(\varphi\)), also known as modal shapes, are obtained. The eigenvectors represent the relative amplitudes at different positions within the structure.  
If a force (such as make-up torque) has already been applied to the sealing surface of the connection before the modal analysis, the contact state is also altered, and the stiffness matrix \([K]\) varies with the load. In this case, Equation (1) is represented as follows:  
\[[M]\big{(}\ddot{u}\big{)}+[K(u)](u)=f(t) \tag{5}\]  
where \(f(t)\) includes the static load \(f_{1}(t)\) and the dynamic load \(f_{2}(t)\).  
\[f(t)=f_{1}(t)+f_{2}(t) \tag{6}\]The amplitude of the dynamic load is generally smaller than that of the static load, so it does not disrupt the structural equilibrium established by the static load. After applying the static load, the stiffness matrix \([K_{1}(u)]\) of the system is given by the following:  
\[[K_{1}(u)](u)=f_{1}(t) \tag{7}\]  
When \(f_{1}(t)\) reaches its final value, the final values of the static load (\(u\)) and the dynamic load \([K_{1}(u)]\) can be obtained. At this point, the system stiffness matrix \([K_{1}(u)]\) can be considered as the initial stiffness matrix. Therefore, Equation (5) is as follows:  
\[[M]\big{(}\ddot{u}\big{)}+[K_{1}(u)](u)=f(t) \tag{8}\]  
The eigenvalue equation is given by the following:  
\[\Big{(}[K_{1}]-\omega^{2}[M]\Big{)}(\varphi)=(0) \tag{9}\]  
By solving Equation (9), we can obtain the modal shape and modal frequency of the premium connection.  
According to the above method, we can obtain the data of the modal shape of the premium connection and apply it to the premium connection by transforming the modal shape into cyclic displacement, and then analyze the energy dissipation at the sealing surface of the premium connection.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 2 Literature Review
### Deletion Method  
Before presenting various imputation methods, one conventional method should be firstly reviewed. The deletion method is a time-saving and common choice when the cardinality of missing data is relatively small. As the name implies, the deletion method means missing data or missing variables are substituted by other data or variables. Due to its simplicity, this deletion method is extensively utilized in data preprocessing [22].  
When the cardinality of missing data becomes larger, other methods that provide estimations of the missing data should be strongly recommended. Currently, with the aim to ensure that the estimated data are closer to the real data, other imputation methods that are based on the correlation between the missing data and the other existing data are being developed. In particular, these imputation methods include the regression imputation method [23; 24; 25; 26], the k-nearest neighbor imputation (KNNI) method [27; 28; 29], the expectation maximization imputation (EMI) method [30], the knowledge-based method [31; 32], and the fuzzy C-means method [33; 34; 35].
# Generation of Stereo Images Based on a View Synthesis Network
## 3 The Proposed Approach
### Right-Eye View Generation and Inpainting  
After rotating the objects, we combined them with their own translations and the background image to generate a right-eye view. We eroded the objects because of noise produced by the view synthesis network along the edges of the rotated objects. This also made the missing regions larger. We used an inpainting technique to solve this problem.  
We chose an inpainting method, proposed by [PERSON] et al. [30; 31], which is based on patch matching and considers brightness changes, the spatial locality of textures, and symmetric patterns. A preliminary result is presented in Figure 6a. Blurring was still evident along the edges of the objects. To improve the effect of image inpainting, we modified the method proposed by [PERSON] et al. in two parts. In their method, before iteratively searching patches in the image to minimize the energy function, [PERSON] et al. randomly set the initial values of missing regions. Instead of this, in the first part, we used the result inpainted using the method of Telea [32] as the initial image. This reduced the computation time of iterations and improved the inpainting quality. The second part of our improvement involved using a reference image. [PERSON] et al. set the range of patch-searching as the data in the image excluding missing regions. This is an intuitive and efficient strategy in general image inpainting. In our model, most of the information of the generated right-eye view can be extracted from the left-eye view. Thus, we could limit the searching region to the left-eye view; we treated the left-eye view as a reference image to increase the probability of finding similar patches. The result using our improved inpainting method, which fixes most blurred regions and artifacts, is presented in Figure 6b.  
Figure 5: Results of rotating objects: (**a**) The pillows; (**b**) bed; (**c**) rotated pillows; and (**d**) rotated bed.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Cache Management
#### 3.4.4 Cache Replacement Policy  
Cache replacement is a problem of determining which blocks to remove when a new access is requested under the \"cache capacity is full\" condition. Cache replacement is important to replace the block so that cache hit ratio is not violated. Existing heuristic  
Figure 11: The Embedding LSTM Model [14].  
Figure 12: The Clustering + LSTM Model [14].  
-based replacement algorithms have a disadvantage that they cannot adapt to data changes. To solve this disadvantage, this paper [15] proposes two neural network models as a solution. The first model is an attention-based LSTM, which is an offline learning, and the second model is an integer Support Vector Machine (ISVM), called Glider, which enables offline and online learning. The two models are Hawkeye [31]-based prediction approach, which learns optimal caching behavior as supervised learning model. The first model, an attention-based LSTM, sees the problem as a sequence labeling problem. The detailed architecture consists of embedding, single layer LSTM, and attention layer as shown in Figure 13. The embedding layer is the task of processing the input into meaningful data, the LSTM layer learns the behavior of the cache, and the attention layer learns the correlation between PCs. The output is a binary value which predicts whether the PC sequence is cache-friendly or not.  
The second model, Glider simplifies feature by designing the PC sequence as a k-sparse binary to simplify the model as shown in Figure 14. This feature has the advantage of not having to learn the order of the sequences. Glider consists of a PC History Register (PCHR) and an ISVM table. PCHR keeps the last 5 PCs seen from each core in any order, and is modeled as an LRU cache to keep the most recent 5 PCs. ISVM stores the weight of each PC and consists of 16 weights. When the current PC is accessed, it creates an index by hashing the current PC and finds the corresponding ISVM entry in the ISVM table. And, based on the indexed ISVM and the PC existing in the current PCHR, a 4-bit hash is created to find the five weights. Five weights are extracted through five indexes. The output is calculated by adding the extracted five weights. If the output is above the threshold, the model predicts the block as cache-friendly. In this case, it is inserted with a high-priority. If the output is less than 0, the model predicts the block as cache-averse. In this case, the block is inserted with low-priority. If the output is between 0 and the threshold, the model predicts the block as cache-friendly, but it is inserted with a mid-priority.  
Figure 13: Attention-based LSTM Model [15]: LSTM received a sequence of PC recurrently as an input and predicts the cache priority for current PC.  
When evaluating the online model, Glider showed an average accuracy improvement of 88.8 vs. 84.9% compared to the latest online model Hawkeye.
# The Volatile Compounds Change during Fermentation of _Saccharina japonica_ Seedling
###### Abstract  
It is important to eliminate the fishy odor and improve the aroma quality of seafood. In this study, the _Saccharina japonica_ (_S. japonica_) seedling, which is a new food material, was investigated for the effects of fermentation with _Saccharomyces cerevisiae_ (_S. cerevisiae_) through sensory evaluation, GC-MS, and odor activity value (OAV) analysis. GC-MS analysis revealed the presence of 43 volatile compounds in the unfermented _S. japonica_ seedling, with 1-octen-3-ol, hexanal, and trans-2,4-decadenial identified as the main contributors to its fishy odor. After fermentation with _S. cerevisiae_, 26 volatile compounds were identified in the _S. japonica_ seedling. Notably, the major malodorous fish compounds, including 1-octen-3-ol, hexanal and trans-2,4-decadenial, were no longer present. The results indicate that fermentation with _S. cerevisiae_ is an effective method for removing fishy malodor compounds and enhancing the volatile components with fruity, sweet, green, and floral notes in the _Saccharina japonica_ seedling. This process facilitates the elimination of fishy malodor and enhance the fruity, sweet, green, and floral notes of _S. japonica_ seeding and other seaweeds.  
Saccharina japonica seeding; aroma; volatile compounds; _Saccharomyces cerevisiae_; GC-MS
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
###### Abstract  
The Border Gateway Protocol (BGP) is the current state-of-the-art inter-domain routing between Autonomous Systems (ASes). Although BGP has different mechanisms to manage outbound traffic in an AS domain, it lacks an efficient tool for inbound traffic control from transit ASes such as Internet Service Providers (ISPs). For inter-domain routing, the BGP's destination-based forwarding paradigm limits the granularity of distributing the network traffic among the multiple paths of the current Internet topology. Thus, this work offered a new architecture to manage incoming traffic in the inter-domain using OpenFlow networks. The architecture explored direct inter-domain communication to exchange control information and the functionalities of the OpenFlow protocol. Based on the achieved results of the size of exchanging messages, the proposed architecture is not only scalable, but also capable of performing load balancing for inbound traffic using different strategies.  
Software Defined Networking; BGP; OpenFlow +
Footnote †: journal: Information Information  
1
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.5. Mean Intersection over Union (Mean IoU) [40]  
Mean IoU computes the average IoU (Intersection over Union) and is used to assess how well segmentation models work in computer vision applications. The overlap between the segmentation that is anticipated and the segmentation that is based on ground truth is measured. IoU is computed explicitly as the area of union divided by the area of intersection over the ground truth and the predicted segmentation. This yields a number in the range of 0 to 1, where 1 denotes perfect overlap.  
\[Mean\ IoU=\frac{1}{N}{\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}}}, \tag{4}\]
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.6 Replay Attack Resistance  
In the proposed scheme, messages \(\{PID_{i},M_{1},M_{3},M_{4},t_{1}\}\) and \(\{M_{5},M_{7},M_{8},t_{2}\}\) transmitted between entities all contain timestamps. In addition, before each interaction, the entity checks the timeliness of the received message, and only messages that arrive within the specified time will be received. Therefore, the scheme can effectively resist replay attack.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Silicon Waveguides
#### 4.2.2 Waveguides: Displacement Damage Effects  
No direct investigation has been conducted to determine if bulk defects cause significant transmission loss in silicon waveguides. However, the study in [PERSON] et al. [65], which irradiated _p-i-n_ PDs with 14-MeV neutrons to a fluence of 7.5 \(\times\) 10\({}^{12}\) neutrons/cm\({}^{2}\), used a silicon waveguide to guide light to the photodiode. Since no radiation-induced performance degradation was found with the photodiode, it can be inferred that the waveguides used in that study also did not exhibit any substantial DD effects.
## 2 Materials and Methods
### Decoy Calibration  
Measured shock pulses from the field do not provide the conversion of shock events from different directions to the equivalent free-fall drop heights (EFFDH) on their own. The area under an acceleration-versus-time shock pulse correlates with the total velocity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & **Shipping Route** & **Decoy Size** & **Number of One-Way Trips** \\ \hline \multirow{4}{*}{Seoul} & S1: FC—Dongjak-gu, & Small & 6 \\  & Seoul—RC & Medium & 6 \\ \cline{2-3}  & S2: FC—Gangnam-gu, & Small & 6 \\  & Seoul—RC & Medium & 6 \\ \hline \multirow{2}{*}{Busan} & FC—Saha-gu, Busan—RC & Small & 6 \\  & & Medium & 6 \\ \hline \multirow{2}{*}{Namwon} & FC—Inwol-myeon, & Small & 6 \\  & Namwon—RC & Medium & 6 \\ \hline \multirow{2}{*}{Gongju} & FC—Gyeryong-myeon, & Small & 6 \\  & Gongju—RC & Medium & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental route information and the number of trips made.  
Figure 3: Map of the experimented routes. Purple lines indicate the forward trip and red lines indicate the return trip.  
change of the event, encompassing both the impact velocity and the rebound velocity. However, it is specifically the impact velocity component that relates to the EFFDH. As the velocity change is the sole measurable quantity in the field, it becomes necessary to isolate the impact velocity from the shock pulse by subtracting the rebound velocity. The rebound velocity is a fraction of the impact velocity determined by the package's elasticity, quantified by the coefficient of restitution (\(e\)). Therefore, if we build a package with defined elasticity, the coefficient of restitution becomes a characteristic of the package that will not change. In this study, a package with defined elasticity is called a decoy. Prior to starting the shipping experiments, the coefficients of restitution as functions of the impact orientation for each type of decoy were experimentally determined in the laboratory through a decoy calibration process. The decoys were dropped from six different heights using a PDT-80 free-fall drop tester (Lansmont Corporation, Monterey, CA, USA). The drop heights used for the calibration process were 304.8 mm, 457.2 mm, 609.6 mm, 812.8 mm, 914.4 mm, and 1066.8 mm (12 in., 18 in., 24 in., 32 in., 36 in., and 42 in.). At each specified drop height, a single drop test was conducted on every possible impact orientation: six flat drops, twelve edge drops, and eight corner drops. The \(e\) value for each impact orientation was then determined using the known free fall drop height and resultant velocity change (vector sum of the velocity change from each axis) collected from the field data recorder through the following relationship (Equation (1) [30]):  
\[e=\frac{\Delta v}{\sqrt{he(2g)}}-1 \tag{1}\]  
where:  
\(e\) = coefficient of restitution  
\(\Delta v\) = resultant velocity change (m/s or in./s)  
\(he\) = drop height (m or in.)  
g = acceleration due to Earth's gravity (9.81 m/s\({}^{2}\) or 386.4 in./s\({}^{2}\))
## 4 Field Experiments  
Based on the design system parameters derived from simulation results and the equipment used in Table 1, we conducted outdoor tests to evaluate the system performance. In this system, it is important to note that there is a slight deviation between the wavelength of the ultraviolet light source and the peak wavelength of the optical filter. This deviation is determined by the physical properties of the ultraviolet optical filter, specifically its bandwidth. If we can choose an optical filter that matches the wavelength of the ultraviolet light source, communication performance will be further enhanced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Devices** & **Parameters** & **Values** \\ \hline \multirow{4}{*}{UV LED} & Wavelength & 265 nm \\  & Optical power & 100 mW \\  & Typical luminous Angle & 150\({}^{\circ}\) \\  & Radiant Efficiency & typ. 5.7\% \\ \hline \multirow{4}{*}{UV optical filter} & Peak wavelength & 262 nm \\  & Peak transmittance & 34\% \\  & Field angle & \(\pm 5^{\circ}\) \\ \hline \multirow{4}{*}{PMT} & Spectral response & From 160 nm to 320 nm \\  & Quantum efficiency & 20\% \\  & Aperture size & 2 cm\({}^{2}\) \\ \hline \multirow{4}{*}{ADC} & Sampling rate & 500 MSPS \\  & Resolution ratio & 12-Bit Resolution \\ \cline{1-1}  & Input bandwidth & 2.3-GHz \\ \cline{1-1}  & Clock latency & 3.5 Clock Cycles \\ \cline{1-1}  & Aperture size & 14 mm \(\times\) 14 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental devices and parameter values.
# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 5 Experiments
### Experiments Description
#### 5.1.1 Experiment 1: Matched vs. Cross-Corpus Training with EmoDb, RAVDESS, and IEMOCAP Databases  
The first experiment studies the behavior of the SER system using the cross-corpus strategy for training. Initially, a matched train-test dataset approach was employed as a baseline, utilizing the same dataset for both training and testing. The row entitled MATCHED TRAIN in Table 2 corresponds to the training set of this experiment. Subsequently, the cross-corpus strategy was applied by expanding the training set including additional datasets, corresponding to the row entitled EXTENDED TRAIN in Table 2. Both experiments employ the same test set indicated in the row TEST of Table 2.  
1.2 Experiment 2: Matched vs. Cross-Corpus Training with EmoDb, RAVDESS, IEMOCAP, and CREMA-D Databases  
The second experiment further studies the effect of dataset size and diversity on the system's performance. So, following a similar strategy to the previous MATCHED/EXTENDED TRAIN experiments, the training set was expanded also using CREMA-D dataset. This dataset includes many more speakers than the previously used datasets (see Table 1, which allows challenging the cross-corpus training in the face of plenty of variability). Table 3 presents the organization of datasets in this experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MATCHED TRAIN** & **EXTENDED TRAIN** & **TEST** \\ \hline \multirow{2}{*}{EmoDb} & EmoDb & \multirow{2}{*}{EmoDb} \\  & RAVDESS & & \\  & IEMOCAP & & \\ \hline \multirow{2}{*}{RAVDESS} & EmoDb & \multirow{2}{*}{RAVDESS} \\  & RAVDESS & & \\ \hline \multirow{2}{*}{IEMOCAP} & EmoDb & \multirow{2}{*}{IEMOCAP} \\  & RAVDESS & & \\ \end{tabular}
\end{table}
Table 2: Train and test databases used in experiment 1: cross-corpus strategy for training. Audio and speakers in the train set are not included in the test set.
## 3 Results
As far as the organic components are concerned, ToF-SIMS spectra measured for sample A in the region close to the gilding leaf show peaks which could be attributed to the acylium ions of palmitic (C\({}_{16}\)H\({}_{31}\)O\({}^{+}\), _m/z_ 239) and stearic (C\({}_{18}\)H\({}_{35}\)O\({}^{+}\)_m/z_ 267) acids and to the ion fragments of monoglycerolester of palmitic (C\({}_{19}\)H\({}_{37}\)O\({}_{3}\)\({}^{+}\)_m/z_ 313) and stearic (C\({}_{21}\)H\({}_{41}\)O\({}_{3}\)\({}^{+}\)_m/z_ 341) acids. The peaks corresponding to  
Figure 5: (**a**) OM image of sample B, reflected light, dark field. (**b**) FESEM image and EDX spectra of sample B (gold foil on tin lamina). The gold leaf is visible as a brighter, thin region on top of a thicker region corresponding to tin. (b) The EDX spectra measured on the gold leaf (spot analysis), on the tin lamina and on the preparation layer (analyzed area ca. \(20\times 20\)\(\upmu\)m\({}^{2}\)). the ion fragments of lead palmitate and stearate were also detected. Peaks ascribable to deprotonated palmitic and stearic acids were observed in the negative ion ToF-SIMS spectra. Palmitic and stearic acids are the oxidation and hydrolysis products of lineseed and other unsaturated oils during ageing of the original materials, as shown in many studies on oil painting ageing [17]. On the other hand, lineseed and other oils are not the only source of fatty acids, which are contained also in waxes and egg. Moreover, the presence of several contaminants and the altered state of the samples did not allow us to derive any firm conclusion about the nature of the adhesive materials. For sample B, in the ToF-SIMS spectra we did not observe peaks attributable to lineseed oil or to protein-based materials. This is probably due to the alteration (mineralization) of the organic materials which occurs faster when they are directly in contact with air and light as a result of the partial detachment of the gold leaf from the oxidized tin foil.
## 4 Experiments
### Dataset
in spatiotemporal prediction have demonstrated that graphs constructed with geodesic distances deliver comparable performance to those based on road-network distances, but with lower complexity and fewer external data dependencies [12; 14]. Each graph in time encapsulates information about the spatial relationships between nodes (measurement stations) and their temporal features. Six past graphs are constructed to represent the historical evolution of noise, while fourteen future graphs are what the model aims to predict. The connections between nodes reflect the distances between nearby measurement stations, thereby capturing spatial relationships within the urban environment. The dataset is organized into temporal snapshots, enabling the models to predict node features in future graphs. * Six past graphs: \(G_{T-6},G_{T-5},G_{T-4},G_{T-3},G_{T-2},G_{T-1},G_{T}\). * Fourteen future graphs: \(G_{T+1},G_{T+2},G_{T+3},G_{T+4},G_{T+5},\ldots,G_{T+14}\). * Vertex connections to the 15 closest nodes based on Haversine distance. * Edge attributes, including distance. * Geographic coordinates used for future model evaluation.