esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.10 Beverage Cooling etc.  
As already discussed in the introduction, just changing the temperature to a temperature desired for consumption does not fit to the definition of food processing, referring to a transformation of natural products into food, or of one form of food into other forms, in a strict sense. But since these processes are even less belonging to storage or transport, they actually fit best to food processing and are therefore discussed here after those processes that fit in a strict sense.  
Beverage cooling is undoubtedly the most common and also most frequent application of PCM by far. And it is done by a variety of approaches, which gives a good overview on how PCM can be applied.  
Direct cooling of beverages by adding ice in form of ice cubes is today standard, especially in summer. To speed up the cooling, the ice is crushed (large surface) or the beverage stirred (forced convection). Actually, there is probably no type of beverage which is not cooled this way today. In the extreme case, small ice particles are used, and a type of beverage made of such ice and a drink is called a \"slushy\". Preparation is by mixing the ingredients including ice cubes in a blender, thereby crushing the ice, or by a commercial slush machine which uses cold water and then cools the ingredients down to form ice.  
Having ice directly added to a beverage leads to dilution with water, which is not desired in many cases. Indirect cooling, meaning cooling the beverage via heat exchange, avoids the dilution of the beverage. The easiest approach is a bucket with cold water, better ice, where the beverage is placed for cool down. This is standard for bottled wine or soft drinks in cans, thus small amounts. For big beverage containers, this can be done by a pipe heat exchanger, with the beverage flowing in the pipe and ice on the outside. To tap cold beer, so-called \"ice bank coolers\" are commercially available from several companies. They have a cold storage where water is frozen to ice by a refrigeration unit, and to tap cold beer the beer flows through coils immersed in the storage. One of the manufacturers [35] mentions that it is also useful for wine, water, and cocktails. Ref. [36] performed an experimental investigation of the energy efficiency of such a beverage cooler with ice storage. They concluded, that by modifying the control of the ice storage a reduction of 15% in energy consumption and a significant decrease of refrigeration system startups could be achieved. Further energy savings are possible by other measures.  
While cooling of beverages is standard, and commercial solutions exist and are widely used, the analysis of [36] is a clear reminder that there can be still significant demand for R&D. The higher energy costs which are experienced nowadays, compared to previous years, might initiate further R&D to optimize even already commercial products towards saving more energy.
## 2 Materials and Methods
### Performance Evaluation  
#### 2.7.1 Lopo-Cv  
In the present study, we performed leave-one-participant-out cross-validation (LOPO-CV) to assess the participant-independent MDD-control classification performance for each combination of feature, classifier, and session (resting and emotion-induction). LOPO-CV is a technique for evaluating how well the results of a method will generalize to unseen data. In each fold of the LOPO-CV, data from 54 participants were used to train the classifier, and then, the \(N\)-dimensional data from the one remaining participant were used as the test data. This step was repeated until every participant'sdata had served as the test data once. We then recorded the classification accuracy, computed as the number of correctly classified participants divided by the total number of participants (55 folds). Here, a misclassified datum in a testing fold resulted in only a small increase of error rate (\(1/55=1.82\%\)).
## 4 System Model
### Reinforcement Learning
#### 4.2.1 Markov Decision Process  
The process in which the agent observes the environment's output in parameters like a reward, next state, and then what action would be taken next is the MDP. We have illustrated the overview of MDP in Figure 4. The Figure 4 shows that when state jumps from initial or current state \(H_{i}\) to next state \(H_{i+1}\) it has taken some action \(A_{i}\). The action taken will lead to the reward \(S_{ij}\). Reward is actually the routing cost in our case and state space in our scenario are routers \(H_{1}\) to \(H_{9}\).  
\[P[H_{i+1}|H_{i}]=P[H_{i+1}|H_{1},\dots,H_{i}] \tag{1}\]  
The MDP for our router \(H_{i}\) at time t can be described as follows:  
* The router \(H_{i}\) senses its current state and obtains the current state.
* Based on current state of router \(H_{i}\), agent selects action \(A_{i}\) at time \(t_{i}\).
* Based on \(H_{i}\) the environment makes a transition to new state \(H_{i+1}\) and get a reward \(S_{ij}\).
* That reward \(S_{ij}\) is feedback to the router and the process continues to next states with the same process.  
Figure 3: Overview of RL-based stretch reduction strategy.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 2 Numerical Simulation  
### Model Description  
According to the embankment cross-section parameters of the Wuhai-Maqin highway, a 3D simplified model was established. The width of the embankment surface was 26 m, the height of the embankment was 2 m, and the grade of the side slope is 1:3. In the model, the angle between wind direction and the straight-line embankment was recorded as \(\alpha\) (\(\alpha\) is taken as 30\({}^{\circ}\), 50\({}^{\circ}\), 90\({}^{\circ}\)). In order to ensure the full development of the flow field, the calculation domain of the overall model after trial calculation adopted X \(\times\) Y \(\times\) Z = 180 m \(\times\) 60 m \(\times\) 32 m [29; 30], as shown in Figure 1a. The radius of the embankment in the curved section was denoted as R (R = 400 m, 1000 m, 2000 m, 3000 m), and the computational domain adopted X \(\times\) Y \(\times\) Z = 160 m \(\times\) 300 m \(\times\) 32 m, as shown in Figure 1b.  
According to the existing literature, in order to ensure the full development of the flow field, the height of the model from the upper boundary of the computational domain should be at least 5H (H is the height of the model, and the height of the subgrade model in this paper was 2 m), at least 10H from the entrance, and at least 20H from the exit [31; 32]. Therefore, the set computational domain size was reasonable and had no effect on the simulation results.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Performance Properties of Organic Solar Cells  
The important electrical characteristics of OSC that are correlated to its performance are the open-circuit voltage, short-circuit current, maximum power point, fill factor, power conversion efficiency, and quantum efficiency. The expected values and interdependence of these parameters are demonstrated on a classical current-voltage (IV) curve (Figure 4).  
Figure 3: Energy level diagram demonstrating the operation of OSC.  
The maximum possible voltage that can be generated by a photovoltaic (PV) cell is termed as open-circuit voltage (V\({}_{\text{OC}}\)). It refers to difference between the HOMO of the donor and the LUMO of the acceptor material.  
\[V_{\text{OC}}=\frac{1}{e}\langle\left|E_{HOMO}^{donor}\right|-\left|E_{LUMO}^{acceptor}\right|\rangle-0.3 \tag{1}\]  
Current flowing in the absence of any external resistance (0 voltage) through an illuminated solar cell is termed as short-circuit current (I\({}_{\text{SC}}\)). It refers to the maximum possible current produced by the device. The point of the current voltage characteristic curve (IV) curve that renders the maximum rectangle area is termed as the maximum power point (P\({}_{\text{MP}}\)). At a maximum current and voltage, the ratio of the actual maximum power the output to its theoretical output is termed as the fill factor (FF). The FF is higher for larger inner areas (A), as shown in Figure 4.  
\[FF=\frac{P_{MP}\cdot I_{MP}}{V_{\text{OC}}\cdot I_{SC}}=\frac{area\ A}{area\ B} \tag{2}\]  
The power conversion efficiency (PCE) is undoubtedly the most important parameter of performance of any OSC. It is a ratio of the output of OSC to the input of energy from sunlight. The abovementioned parameters from the IV curve are used to calculate the PCE, where P\({}_{\text{in}}\) is the irradiance value.  
\[PCE=\frac{I_{\text{SC}}\cdot V_{\text{OC}}\cdot FF}{P_{\text{in}}} \tag{3}\]  
Quantum efficiency (QE) is the efficiency of the device as a function of the incident radiation wavelength.
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 4 Experimental Results
### Comparison to State-of-the-Art Methods
#### 4.4.2 Comparative Experiments in Camvid  
To further evaluate the advancement of the proposed method, we also evaluate DGMNet on the CamVid dataset. As shown in Table 5, DGMNet achieves excellent performance. It can process input images with a resolution of 720 \(\times\) 960 at 121.4 FPS, and the mIoU value on the CamVid test dataset reaches 74.8%. The experimental results show that our proposed method achieves the highest segmentation accuracy under the premise of ensuring the real-time performance of segmentation, which further verifies the effectiveness of the proposed method.
###### Abstract  
This paper presents the results obtained for the maximum power point tracking (MPPT) technique applied to a photovoltaic (PV) system, composed of five solar panels in series using two different methodologies. First, we considered a traditional Perturb and Observe (P&O) algorithm and in a second stage we applied a Fuzzy Logic Controller (FLC) that uses fuzzy logic concepts to improve the traditional P&O; both were implemented in a boost converter. The main aim of this paper is to study if an artificial intelligence (AI) based MPPT method, can be more efficient, stable and adaptable than a traditional MPPT method, in varying environment conditions, namely solar irradiation and/or environment temperature and also to analyze their behaviour in steady state conditions. The proposed FLC with a rule base collection of 25 rules outperformed the controller using the traditional P&O algorithm due to its adaptative step size, enabling the FLC to adapt the PV system faster to changing environment conditions, guessing the correct maximum power point (MPP) faster and achieving lower oscillations in steady state conditions, leading to higher generated energy due to lower losses both in steady state and dynamic environment conditions. The simulations in this study were performed using MATLAB (Version 2018)/Simulink.  
boost converter; fuzzy logic controller (FLC); maximum power point (MPP); maximum power point tracking (MPPT); Perturb and Observe (P&O); photovoltaic (PV)  
Algorithm for the MPPT of a Photovoltaic System. Algorithms 20211424. [[https://doi.org/10.3390/a14010024](https://doi.org/10.3390/a14010024)]([https://doi.org/10.3390/a14010024](https://doi.org/10.3390/a14010024))  
1 December 2020  
Accepted: 3 January 2021  
Published: 14 January 2021
# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 3 Data and Methods  
In order to address the above issues, we propose a discriminatory speech classification method based on improved SMOTE and SVM-RF. The specific steps are as follows:  
1. Collect data about discriminatory remarks on Sina Weibo and perform preprocessing, including data cleaning, text cleaning, word segmentation, and the removal of stopwords.
2. Construct an LDA-based topic model to filter out the data related to the topic.
3. Use SMOTE to synthesize new data based on existing data.
4. Classify using SVM and RF and calculate the integration weights using the correctness of the respective classification results.
5. Calculate the weighted sum of the respective classification results of SVM and RF to obtain the final classification result.
6. Perform experiments and evaluation of the model based on machine learning methods.  
The overall framework is shown in Figure 1. From the framework, it can be seen that, first, the data are collected from Sina Weibo, then the data go through pre-processing and enhancement sessions. Next, the data are fed into the model for training, followed by testing and optimization of the model, and finally the optimized model classifies discriminatory speech.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology
### Dataset  
The UCF101 dataset contains a total of 13,320 videos from 101 human action classes extracted from videos in the wild. It has the widest variety in terms of actions, and it is the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Method** & **Optical Flow** & **Accuracy (\%)** \\ \hline
[13] & Spatial and temporal two-stream networks & ✓ & 86.9 \\
[22] & Spatiotemporal ResNet & ✗ & 93.4 \\
[23] & Late fusion of two-stream network & ✗ & 93.5 \\
[24] & Deeper two-stream ConvNets & ✓ & 95.1 \\
[28] & Two-stream ResNets with encoder/decoder setup & ✓ & 82.4 \\
[29] & Two-path network & ✗ & 90.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of performance of state-of-the-art methods on UCF101 dataset.  
Figure 1: Proposed two-stream network’s architecture.  
most complex dataset to date in this domain, with substantial differences in camera motion, object look and position, object size, perspective, cluttered backdrop, light conditions, and so on. This is a supplement to UCF50 dataset, that contains 50 activities. Because most accessible action recognition datasets are unrealistic and produced by actors, UCF101 intends to inspire future action recognition research by learning and exploring new realistic action categories. The videos are divided into 25 action groups, where each group may contain 4-7 clips. The details of the dataset are given in Table 2.
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 2 System Specification
### Optical System Parameters  
The optical system is an important part of the spectrometer, as it determines the performance and weight of the spectrometer. The main parameters of the optical system include focal length (\(f^{\prime}\)), \(FOV\), relative aperture (1/F), and dispersion width of the slit image (\(d\)). The \(FOV\) angle of the optical system is calculated to be 53.2\({}^{\circ}\) according to the coverage width and flight altitude.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
###### Abstract  
Wind power is a crucial direction for new energy transition technology in response to the challenges of global warming. However, the potential for collisions between the blades and the tower barrel remains a significant concern. To address this issue, a large number of sensors, such as lasers and cameras, are attached to the structure, but they struggle to operate in complex weather and at night. This paper presents a method of employing a 79 GHz FMCW (frequency-modulated continuous wave) mmWave (millimeter-wave) radar with circularly polarization on the top of the tower. During the design, two main considerations are raised: (1) Since the small-RCS (radar cross-section) blade experiences an oblique incidence from more than 70 m away, the channel SNR (signal-to-noise ratio) is low, so high-gain antennas and SIMO (single-input multiple-output) radar configurations are designed to increase the \(\mathrm{P_{t}}\) (transmitting power). (2) Wind turbines are often located in offshore or mountainous areas with a high level of weather interference, so a pair of circularly polarized antenna is used to reduce the interference of meteorological particles to the radar. Finally, test results from a practical wind turbine in different weather conditions prove its practicality. During tests, the wind turbine operates at a rotor speed of 6 to 12 rounds per minute, and the clearance range has an obvious inverse relationship with it, ranging from 6 to 12 m. This technology enhances safety, maximizes efficiency, and enables optimal length and weight determination during design for improved power generation.  
wind turbine; blade clearance; circularly polarized; millimeter-wave (mmWave); radar sensor +
Footnote †: journal: _electronics_  
Article
# Comparative Analysis of COL9A1 Genotyping in Oral Squamous Cell Carcinoma Diagnosis: A Pilot Study
###### Abstract  
The epidemiology of OSCC continues to increase despite the progress that has been made. More than ever, the diagnostic approach process needs to focus on genetic and epigenetic alterations. The aim of our study was to identify and correlate the presence of COL9A1 gene variants in two types of samples from OSCC patients. Methods: Our pilot study included 32 subjects diagnosed with OSCC. Fresh tumour tissue and peripheral blood samples were used in order to identify the genotypes of the COL9A1 gene. Variables, such as age, gender and tobacco and alcohol use, were also taken into consideration. The DNA analysis of the samples was based on a tagged SNP (rs550675) for the allelic discrimination. Results: The statistical significance and correlation of the COL9A1 genotypes within the two categories of samples was statistically significant (\(p<0.001\)) for the C/T and T/T genotypes, providing an important perspective on the potential identification in blood samples of the gene mutation encountered in OSCC. Conclusions: This is the first study that focused on providing preliminary results using blood samples via the identification of COL9A1 gene variants in OSCC patients. The possibility of introducing a liquid biomarker by targeting this genetic variant is an appealing perspective for screening and diagnosis.  
OSCC; genetics; COL9A1; genotype; biomarkers; risk factors; liquid biopsy; early diagnosis +
Footnote †: journal: Medical Imaging
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 6 Conclusions  
The aim of this research was to evaluate the indoor coverage of Orbcomm satellites within a suburban setting. These satellites operate in LEO and transmit on the VHF band. By conducting multiple outdoor and indoor measurements, we examined the variations in signal strength across different environments. More specifically, the attic, the first floor, the ground floor, the basement and the garden were evaluated. Undoubtedly, the outdoor satellite signals were most strongly received. Furthermore, a clear correlation between a higher elevation angle and a higher signal strength was present. In an indoor setting, the satellite signals were received most strongly in the attic. However, a substantial difference in signal strength between outdoor and indoor was noted. Furthermore, similar signal strengths were observed on the above-ground floors. Notably, the Orbcomm satellite signals were received in the basement, which is constructed out of thick concrete walls and slabs, albeit with low power and only for a short duration of time.  
Overall, this study proves that the Orbcomm satellite signals penetrate deeply indoors. The signals even arrived with high carrier-to-noise density ratio in multiple environments. The high Power on Ground highlights the feasibility of indoor satellite communication, which is simply not possible with the current implementation of Global Navigation Satellite Systems (GNSS) in MEO. Therefore, along with the declining costs associated with launching them, LEO satellites offer an interesting opportunity for future indoor satellite communication and navigation applications.  
Methodology, software, measurements, writing, and analysis, W.V.U.; conceptualization, writing, and validation, T.J.; supervision, and writing, M.W.; supervision, and writing, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used for this study is available upon request. Due to the large size and privacy concerns the data is not publicly available.  
The authors declare no conflicts of interest.
## 3 Model Formulation
### Reward  
The cost of maintenance operations comprises the reward function in this model. [PERSON], [PERSON], and [PERSON] [6] proposed the sum of inspection, maintenance action cost (defined as a function of state and action), and failure cost as the reward function. The failure cost was computed by defining the types of failures (such as small leaks, large leaks, and ruptures) and their probability under each state. This cost integrated the risk associated with failures due to unexecuted maintenance actions. [PERSON] and [PERSON] (2014) detailed the maintenance cost, which incorporates excavation, sleeving, and recoating, which are commonly applied to repair pipelines. All the cost computation approaches discussed in the articles can be adopted for POMDP reward function computation. However, the inspection cost would have a meaningful impact if the POMDP model is formulated in such a way that different inspection intervals are considered in the problem.  
\[R_{S,A}=CM_{S,A}+CF_{S}\ (+CI) \tag{8}\]  
where  
\(CM_{S,A}\)-the cost of maintenance (costs such as excavation, sleeving, coating, labor, and equipment)  
\(CF_{S}\)-the cost of failure  
\(CI\)-the cost of inspection
## 2 Background and Motivation
### Post-Processing and Pre-Selection Algorithms  
Most of the existing PUF technologies such as DRAM PUFs utilize helper data algorithms and ECCs to improve the reliability and robustness [14; 19; 20; 28; 29; 30]. However, using helper data may leak some information about the secret keys, and ECC circuits cause significant hardware and software overheads. Temporal majority voting (TMV) is one of the simplest ECCs, which is a repetition code and is based on sampling PUF cells multiple times and selecting the majority sample. Bose Chaudhuri Hocquenghem (BCH) codes are another popular ECC that are usually utilized as a final error-correction technique. It is inefficient to use BCH codes alone when the bit-error rate (BER) of native responses is high. Thus, some additional stages are required to be applied to raw PUF responses before using ECC mechanisms due to varying characteristics and unpredictable behaviors of DRAM cells in different measurements under normal/unstable ambient conditions.  
As mentioned above, current DRAM PUFs, including latency-based ones, mask unstable and unsuitable cells using filtering processes or selection algorithms [12; 13]. However, these solutions limit the CRP space by disqualifying the unacceptable cells and cause extra time and implementation overheads. Additionally, they need specific algorithms to determine the accurate location (address information) of selected cells to indicate them in the challenges [31; 32].