esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation  
The proposed algorithm is programmed using Pytorch. For training the A3C-based DNN model, we use the discount factor \(\gamma\) near to 1 and a learning rate of 0.0001. The parameters of the model are initialized as random uniform values within \([-0.08,0.08]\). There are three types of problem instances for training the proposed model: random instances (RI), linear instances (LI), and quadratic instances (QI). Random instances have uniformly random integer values in the range of \([1,10]\) for both profit and weight. Linear instances have the same weight as RI, and profit of them has weight plus random float value in \((0,1)\). Quadratic instances have the same weight as RI, and profit of them has \(weight\times(10-weight)+1\). In the problem instances, knapsack has the same capacity in \(10,20,40,80,100\). The number of items is 50, and the number of knapsacks is \(1,3,5\). To deal with diversified problems, we prepare various models, and each model solves problems of its type. Each model has one type of item set (one of RI, LI, QI), a fixed number of knapsacks, (one of 1, 3, 5), and a fixed maximum capacity \(C\) (one of 10, 20, 40, 80, 100). In the case of 5 knapsacks, there are three types of capacity (10, 20, 40) because more than 40 is sufficient to select all items. 1000 data sets are used for each problem instance, and the model's parameters are updated each five steps, with the process repeated at least 40 times. Every test result is executed on a workstation equipped with an AMD Ryzen 9 5900 X 12-Core Processor 3.70 GHz and 32 GB RAM. The comparable methods for the test are as follows.  
* Greedy algorithm: We use a simple greedy algorithm that finds an item that has maximized profit divided by weight ratio and tries assigning the item to a knapsack with maximized capacity.
* Random solution: We repeat randomly generating solution of each data 1000 times and then select a solution that has a maximum profit ratio.
* _Gurobi_ optimizer: We use a mathematical optimization tool _Gurobi_ for optimized solutions.
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 4 Discussion  
### Influence of Different Factors  
In this study, natural frequencies of a scale RCP impeller under different conditions are tested and simulated. From the comparison between Condition A and B in Table 1, it can be found that all natural frequencies are reduced by added mass by more than 10%. From the comparison between Condition A and D, it can be concluded that the effect of centrifugal force can be ignored. Condition C is related to added mass, centrifugal force and hydraulic load. Because natural frequencies under Condition C are smaller than those under Condition B, and the effect of centrifugal force can be neglected, it can be concluded that hydraulic load increases natural frequencies of the impeller. According to our data, the hydraulic load increases natural frequencies by more than 3%.  
In summary, when an impeller rotates in water, added mass reduces natural frequencies of the impeller, the hydraulic load increases them, while the centrifugal force slightly reduces them. Therefore, among the three influencing factors, added mass and hydraulic load should be considered in modal analysis of the impeller, while the centrifugal effect can be ignored. Since added mass is related to the density of the liquid and the speed of sound, and hydraulic load is related to pressure distribution around the impeller. Changes of medium, temperature or pressure distribution will lead to variations of natural frequencies of the impeller.
## 3 Results and Discussion
### Preliminary Analyses (Test A)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Saastamoinen**} & \multicolumn{3}{c}{**Estimate ZTD**} \\ \cline{2-7}
**GNSS Combination** & \multicolumn{3}{c}{**Standard Deviation (mm)**} & \multicolumn{3}{c}{**Standard Deviation (mm)**} \\ \cline{2-7}  & \(\sigma_{\mathbf{x}}\) & \(\sigma_{\mathbf{y}}\) & \(\sigma_{\mathbf{z}}\) & \(\sigma_{\mathbf{x}}\) & \(\sigma_{\mathbf{y}}\) & \(\sigma_{\mathbf{z}}\) \\ \hline GPS & 74 & 33 & 60 & 23 & 27 & 38 \\ GLONASS & 87 & 29 & 60 & 28 & 24 & 11 \\ Galileo & 101 & 51 & 67 & 30 & 29 & 16 \\ GPS+GLONASS & 69 & 17 & 55 & 25 & 10 & 10 \\ GPS+Galileo & 160 & 40 & 63 & 10 & 10 & 6 \\ GLONASS+Galileo & 77 & 28 & 59 & 33 & 26 & 23 \\ GPS+GLONASS+Galileo & 74 & 22 & 68 & 30 & 17 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation of the considered GNSS combinations with Saastamoinen and Estimate ZTD tropospheric models (outlier rejections). is analyzed by other authors [10; 11; 52]. Indeed, the GLONASS system adopts FDMA (Frequency Division Multiple Access) techniques while, conversely, GPS and Galileo use CDMA (Code Division Multiple Access) techniques, fulfilling the signal-in-space interoperability requirement (identical center frequencies of interoperable signals). Thus, GLONASS is not \"signal interoperable\" with GPS or Galileo, but it is \"system interoperable\" (referring to the ability of GNSSs to be used together to provide better capabilities at the user level than would be reached by using a single system) [52]. In more detail, using FDMA signals, different hardware delay biases exist in the GLONASS receiving channel (also referred to as inter-frequency or inter-channel biases). In [10], the authors noticed that integrating a few GLONASS satellites (as in the current research the mean GLONASS satellites mean number is equal to 5) will not contribute to the positioning accuracy when there is a sufficient number of GPS satellites. The same authors analyzed the residuals for the IF code and phase observations from combined GPS/GLONASS PPP and the results related to GLONASS code residuals were significantly larger than the GPS one, while carrier phase residuals were very close for GPS and GLONASS. For the authors in [10], the obtained results are related to the larger GLONASS satellite clock and orbit residuals errors and to the neglected code hardware delay biases. Conversely, in [11] the RMS values of GLONASS code residuals were slightly lower than GPS. This different conclusion from [10] is related to the fact that the GLONASS inter-frequency biases were well threatened in their customized multi-GNSS positioning model developed and proposed in [11]. Finally, in Table 4 an error of few mm is obtained with the triple constellation GPS+GLONASS+Galileo.
## 2 Proposed Method for Fault Diagnosis in Milling Machines
### Continuous Wavelet Transform  
CWT is a powerful signal-processing technique used to analyze non-stationary signals, such as AE signals, in the time-frequency domain. Unlike the Fourier transform, which only provides frequency information, CWT preserves both temporal and frequency information [31], making it ideal for capturing transient and localized features in signals. Mathematically, the CWT of a signal \(x(t)\) is defined as shown in Equation (1):  
\[W_{x}(a,b)=\frac{1}{\sqrt{|a|}}{\int_{-\infty}^{\infty}x(t)\psi^{*}\left(\frac {t-b}{a}\right)\,dt} \tag{1}\]  
where \(a\) and \(b\) are the scale and translation parameters, respectively, and \(\psi(t)\) is the mother wavelet. The scale parameter \(a\) determines the frequency resolution, while the translation parameter \(b\) determines the temporal resolution. The relationship between the scale \(a\) and frequency \(f\) can be expressed as \(f=f_{c}/a\) where \(f_{c}\) is the central frequency of the wavelet. By applying the CWT to AE signals, we generated scalograms that visualize the energy distribution of the signals across various scales and frequencies. Figure 2 illustrates these scalograms under normal and distinct faulty conditions. In the scalogram of the normal condition, the energy distribution appears relatively uniform and stable, with lower intensities across frequencies, indicating consistent and steady operation without disruptions. In contrast, the scalograms of the fault conditions reveal high-energy regions concentrated at specific frequencies and time intervals, reflecting transient bursts and irregularities that correspond to the faulty states. This clear contrast in energy patterns between normal and defective conditions highlights the ability of CWT scalograms to capture localized and transient features that are indicative of faults. The differences in energy distribution across the scalograms enable us to identify distinct fault patterns, which are then used for feature extraction and classification in the subsequent stages of the fault diagnosis process.
# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
###### Abstract
With a significant amount of historical data available, mathematical models of several different stages of wheat crop development are developed using machine learning (ML) approaches (e.g., artificial neural networks, boosting ensemble methods, etc.) with respect to different, artificially created and permuted microclimate conditions correlated with identified plant growth and development indicators at different stages. A prerequisite for such a mathematical model is a large archive of correlated input and output data (i.e., climate conditions) and measurements of plant development indicators. The models are identified based on their inputs: photosynthetic photon flux density (PPFD), light spectrum, temperature, humidity, CO\({}_{2}\), and nutrients selected according to defined scenarios and maintained by corresponding control loops. The observed outputs are: stem segment length, yield, grain number and size, and spectra observed in an incremental way with regards to historical trends, aimed to be captured by high-resolution and multi-spectral cameras, then analyzed, archived, statistically processed, and, finally, modeled by ML algorithms. This is the main premise of the project AgroSPARC-Smart and Predictive Agriculture for Resilience to Climate Change. This paper presents an extension of the previously published conference paper [17] through the inclusion of relevant device design information, both on construction and software architecture, as well as additional details regarding the overall methodology for rapid plant modeling and collected dataset dissemination. The main contribution of this paper is the holistic system for the rapid modeling of plant development. The models are envisioned to be utilized in a smart agriculture setting, for support in wheat crop production and analysis of wheat culture development under climate changes. The proposed system includes the following highlights:  
* An encapsulated design (apparatus) for accelerated experiments of plant growth in an isolated environment with autonomous permutations of artificial climatic conditions. * Cloud-based software support for remote autonomous real-time data monitoring, acquisition, and archiving, suitable for AI data processing and modeling. * A methodology of plant-growth observation with rapid performance of experiments for the collection of relevant measurements and plant development model identification. The paper is organized as follows. The overall methodology of the proposed approach is presented in Section 2. Encapsulated design and its basic features are described in Section 3, while the architecture of the supporting IT system is described in Section 4. Finally, the paper is concluded in Section 5.
## 1 Introduction
Some countries (such as Sweden, Belgium, Spain, and France) intend to maintain the bentonite temperature below 100 \({}^{\circ}\)C. Other countries plan to allow higher temperatures. For example, the Swiss concept stipulates that the maximum temperature of bentonite should be less than 100-110 \({}^{\circ}\)C, but the maximum temperature near the canister is 150 \({}^{\circ}\)C. The smectic-rich clay with 40-60% water saturation exhibits low thermal conductivity, which decreases under the prevailing thermal gradient in the deposition hole. Some concepts intend to include materials with high thermal conductivity (such as graphite and silica sand) or wet buffer materials to increase the buffer material's thermal conductivity [6]. In the past decades, numerous research projects and studies have been conducted worldwide to understand the complex behavior of bentonite as a buffer material more widely and deeply, in geological radioactive waste disposal. A wide range of aspects related to the thermal-hydraulic-mechanical-chemical processes of bentonite have been studied, including high temperature influences on bentonite performance [9; 10; 11; 12], groundwater/gas flow characteristics within bentonite [13; 14; 15; 16; 17], swelling pressure and mechanical properties [18; 19; 20; 21; 22; 23; 24; 25], and the mineral chemical evolution of bentonite [26; 27; 28]. Moreover, experimental analysis and numerical simulation have been employed to study the performance evaluation of bentonite in the thermal-hydraulic-mechanical-chemical coupling process under environmental evolution conditions [29; 30; 31]. However, previous studies often did not consider the porosity change caused by smectite dehydration in bentonite since decay heat influences the transport concentration of radionuclides in buffer materials when radionuclides are released from waste canisters. The HLW repository concepts in Taiwan, the main thermal constraint placed on an EBS is that the bentonite buffer temperature should be less than 100 \({}^{\circ}\)C at the bentonite-canister interface. However, the long-term functions of buffer clay could be lost through smectite dehydration under the prevailing temperature and temperature gradient conditions that arise from waste decay heat. Therefore, the influence of higher waste decay temperatures on bentonite performance requires investigation. The chemical evolution of the bentonite buffer and bentonite degradation in the EBS of HLW disposal repositories caused by thermals affecting smectite dehydration are topics seldom discussed in the performance assessment of EBS. Therefore, in this study, we used the chemical kinetic model of smectite dehydration to calculate the interlayer water expelled from smectite that is caused by higher temperatures of waste decay heat, and study the porosity change of bentonite caused by smectite dehydration and evaluate the effect of porosity change on the transport of radionuclides through buffer materials.
# Field Emission from Carbon Nanostructures
## 2 Field Emission from Carbon Nanostructures
### Emission from Graphene and Graphitic Materials
By using photolithography and wet etching processes, well-aligned graphene field emission arrays (FEA) have been fabricated on copper foil [176]. This method realizes high-ordered micro sized graphene pattern arrays on copper foil with a diameter of 4 mm, forming about 6000 emitting elements (of about 30 \(\upmu\)m \(\times\) 30 \(\upmu\)m), with 20 \(\upmu\)m separation. FE characterization has demonstrated a turn-on field of 7.2 V/\(\upmu\)m for a current of 100 nA/cm\({}^{2}\). In comparison, negligible current was recorded by applying similar fields on un-patterned graphene layers on copper. [PERSON] et al. [177] developed a graphene-sheets/ZnO-nanorods hybrid structure on glass substrate by means of sputtering and hydrothermal techniques. CVD-graphene on copper is transferred on ZnO nanorods to realize a hybrid structure with improved FE characteristics in comparison to the ZnO nanorods alone. The lowest turn-on field reported is 3.7 V/\(\upmu\)m at 1 \(\upmu\)A/cm\({}^{2}\), and the field enhancement factor has been estimated as \(\beta\) = 8723. They demonstrated that the presence of graphene sheets decreases the work function in the hybrid structure, and also increases the number of additional emission sites.
## 4 Experiments
### Corpora Description  
The Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus [37] is commonly utilized for evaluations. IEMOCAP is an interactive emotional binary motion capture database developed by the SAIL Lab at the University of Southern California, containing a total of 12 h of recordings. The data were recorded by ten professional actors (five males and five females) in a studio setting. Each recording is accompanied by discrete emotional labels and annotations for emotional dimensions. The IEMOCAP database consists of five sessions, each featuring dialogues between a female and a male actor, divided into two parts: improvised performances and scripted performances. The former involves spontaneous dialogues without predetermined content, while the latter follows a predefined script. This database encompasses multimodal information, including audio and text, making it suitable for various unimodal emotion recognition studies. To ensure a balanced representation of audio samples across categories, this study combines the excitement emotion into the happiness category. Ultimately, the dataset comprises 5531 audio samples, distributed as follows: 1103 instances of anger, 1084 instances of sadness, 1708 instances of calmness, and 1636 instances of happiness. Figure 6 illustrates the number of audio samples corresponding to each emotional label. As there is no predefined data split in IEMOCAP, we perform 10-fold cross-validation with a leave-one-person-out strategy to achieve comparable results.  
In addition to IEMOCAP, we also evaluate our model on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. RAVDESS consists of 24 actors (12 males and 12 females) and includes both emotional speech and song samples, covering a range of emotions such as happiness, sadness, anger, fear, surprise, and neutral. For this dataset, we adopt a random 8:2 split for the training and test sets. Figure 7 illustrates the number of audio samples corresponding to each emotional label. This split strategy aligns with the approach used in other studies on the RAVDESS dataset, facilitating comparisons of our results with those reported in the literature. The differing data split strategies between IEMOCAP and RAVDESS are intended to ensure consistency with prior research on the RAVDESS dataset.  
In this study, we used the HuBERT pre-trained model to extract speech embeddings, which served as the input to our pyramid network model. The HuBERT model, pre-trained on a large corpus of speech data, provides high-quality representations that capture both phonetic and prosodic features. These embeddings were crucial in facilitating accurate emotion recognition, as they allowed the model to focus on high-level speech characteristics rather than low-level acoustic details. By incorporating these pre-trained embeddings, the pyramid network could more effectively discern emotional cues in speech, leading to improved model performance across different emotion categories. This approach highlights the importance of leveraging pre-trained models for feature extraction in speech-based emotion recognition tasks.
## 4 Results and Discussion  
### Experiment Setup  
In this experiment, the model training was executed on a system equipped with an Intel Xeon Platinum 8362 processor and an NVIDIA A40 GPU. All models were implemented using Python. Comprehensive details regarding the hardware and software configuration can be found in Table 3. The dataset was divided into training and testing subsets at a ratio of 4:1.
# Analysis of Tizen Security Model and Ways of Bypassing It on Smart TV Platform
### Preliminary Analysis of Research Gap  
In [17], we discussed security issues on different smart TV platforms. One of the conclusions was that, currently, only LG and Samsung offer TVs with a significant number of security mechanisms. However, from those two, only on Tizen OS are there specific layers of security (Simplified Mandatory Access Control Kernel--SMACK; Security Framework [SF] Filter Driver [D]--SFD; Unauthorized Execution Prevention--UEP, etc.) that are not present on other platforms. Of course, some platforms also offer code signing or other countermeasures, but they never provide comprehensive security solutions.  
Since the release of first version of Tizen OS in 2012 [18] there was few papers discussing its security [22; 23; 24; 25; 26; 27]. Tizen is often called \"The OS of Everything\" [23] as it is used on a whole family of different devices including smartphones, notebooks, wearables (for example smartwatches), infotainment systems (for example in cars), and of course, smart TVs. Such flexibility has its price in huge differences in system variants for different devices. This has a huge impact on security aspect of specific device, and as a result, most of security concerns that were described in previous papers [22; 23; 24; 25; 26; 27] do not significantly (if at all) affect Tizen OS on smart TVs. For example, in [25] author describes 40 critical issues that [PERSON] was able to find in Tizen OS. We analyzed all of those vulnerabilities and confirmed that they were never present on smart TV. Moreover, most of the applications and libraries in which they were found never existed as a part of the smart TV variant of Tizen OS.  
In existing literature, we can find a description of some security mechanisms (like SMACK [22; 23; 24; 26]) that are present in smart TVs, and sometimes, even a ways of bypassing them [23; 25]. However, we could not find a paper that would describe algorithms and methods that can be used to completely disable any of the security mechanisms implemented in the smart TV variant of Tizen OS. In this paper, we will analyze the security mechanisms of Tizen OS on smart TVs and describe developed methods that make it possible to disable them. Though we found many security bugs in Tizen OS in smart TVs, we will use publicly known bugs (CVE-2014-1303 and CVE-2015-1805) to illustrate the wide applicability of the developed algorithms.
## 4 Discussion
### Heat Stress Visualization  
As shown in Figure 5, it is difficult to recognize the heat-stressed parts in the RGB images. However, the heat-stressed area could be visualized in both the model images. As the discriminant accuracies of the two models were almost the same, their color distribution patterns were also similar. Despite the above outcome (similarity), the model images clearly demonstrated that the resultant images allow improved interpretation of the chemical dynamics and distribution patterns for early detection of heat stress.  
To improve model performance and remove unwanted random noise generated from electrical instruments, various preprocessing methods such as the standard normal variate, normalization, smoothing, multiplicative scatter correction, and Savitzky-Golay first and second derivative can be applied to PLS-DA and LS-SVM models [17; 19]. In the current study, although preprocessing methods were not used for the two models, their classification accuracy was acceptable (over 92%). Hence, applying and finding appropriate preprocessing methods is suggested to establish more accurate and robust models in the future.  
Despite the significant advantages of HSI technologies for agricultural NDE, there are still some difficulties with its field application due to its high cost, complexity, and inability to perform a continuous inspection [63]. To apply the developed methods to field application, a type of miniaturized handheld imager, which is an emerging technology for a field-adaptable purpose, seems to be a promising tool for snapshot-based multispectral technology because it can provide rapid, on-site scanning and convenient handling [64; 65]. Moreover, control software, including the developed classification algorithm, should be installed on electronic devices such as laptop computers, Android- and iOS-based mobile appliances as external monitoring devices for viewing chemical distribution images [66].