esa-sceva/satcom-chunk-collection huggingface dataset:
# Guided Spatial Transformers for Facial Expression Recognition
## 4 Experiments
### Datasets  
In this work, we employ 2 common corpora for emotion recognition: AffectNet [11] and FER-2013 [12].  
* AffectNet: AffectNet is one of the largest datasets available for visual emotion recognition in the wild. It contains approximately 450,000 images divided into 11 non-equally distributed categories: 'Neutral', 'Happy', 'Sad', 'Surprise', 'Fear', 'Disgust', 'Anger', 'Contempt', 'None', 'Uncertain', and 'Non-Face'. Besides the emotional label, each image also has its arousal and valence annotated in the range from \(-1\) to \(1\). The only categories that do not include these annotations are 'Uncertain' and 'Non-Face'. To adapt the annotations to our task and facilitate compatibility between datasets, we divide the valence axis into 3 regions. The first region, which goes from valence 1 to 0.2, represents positive emotions. The second region ranges from 0.2 to \(-0.2\), and it is considered the region of neutral valence. The third group contains the images with valence lower than \(-0.2\), which receives the negative valence label. In total, our new subset contains 325,239 images. In Figure 7, we can see the final distribution of images per class. As the plot shows, the subset is non-balanced, having more positive images than negative or neutral.
* FER-2013: The Facial Expression Recognition 2013 database contains 35,887 grayscale images with a resolution of 48 \(\times\) 48 pixels. This dataset was collected using Google by requesting images associated with key emotional terms. The downloaded images were filtered for rejecting repeated samples and resized. Accordingly to the key term used in the query, each image was assigned to one of the following categories: angry, disgust, fear, happy, sad, surprise, and neutral. One of the major difficulties of this dataset is the different nature of the images as they can contain different poses, occlusions, blurring, and other artifacts, which makes the recognition a challenging task. Again, we need to distribute the images into the same 3 label levels to homogenize both datasets. Unlike what we did with AffectNet, in this dataset, we do not have the valence annotations. For this reason, and following the theories of [PERSON] and [PERSON] [20], we assigned the positive valence to those images categorized as 'happy', the neutral valence to the 'neutral' images, and the rest of the samples to the negative valence set. Those images tagged as'surprise' were excluded from the groups since this emotion can represent positive or negative valences, depending on the situation. In total, the new subset contains 31,885 images over the original size of 35,887. In Figure 7, we can see the distribution of the labels.  
Figure 6: Spatial Transformer CNN architecture. Example for landmarks-based facial patches masks.
## 6 Unbalanced PSI-CA Protocol Based on Single-Cloud Assistance
### Definition of Main Participants and Related Symbols  
1. Large medical institution represents the party with a larger dataset and greater computational and storage capabilities.
2. Small health app developer represents the party with a smaller dataset and lesser computational and storage capabilities.
3. Cloud server represents an auxiliary server that assists the receiver in obtaining the intersection's cardinality operations, undertaking most of the computational and storage pressures.
4. \(X\) and \(Y\) represent the dataset of the large medical institution and the small health app developer, respectively.
5. \(Y^{\prime}\) represents the obfuscated dataset sent by the small health app developer to the large medical institution, used to confuse the cloud server and prevent it from obtaining the accurate cardinality of the intersection. \(k\) represents the cardinality of the set \(Y^{\prime}\).
6. \(\alpha\) represents the private key of the large medical institution in the Diffie-Hellman encryption algorithm.
7. \(r_{1}\) represents the random number generated by the small health app developer, used to blind the data.
8. \(\beta_{j}\) represents the random number generated by the small health app developer for the Diffie-Hellman encryption algorithm.
9. \(H\) represents the hash function negotiated for use by the small health app developer and large medical institution.
10. \(CF\) represents the Cuckoo Filter, \(CF\)._insert_ represents the operation to add an element to the Cuckoo filter, \(CF\)._check_ represents the operation to check if a specified element exists in the filter.
11. \(X_{i}\) represents the \(i\)-th element of the set \(X\). Similarly, \(Y_{i}\), \(C_{i}\), etc., also represent similar meanings.
12. \(C=\{c_{1},c_{2},\dots,c_{n}\}\) represents the set of \(n_{2}\) ciphertexts sent by the small health app developer to the large medical institution.
13. \(C^{\prime}\) represents the set of \(n_{2}\) ciphertexts sent by the large medical institution to the small health app developer
14. \(item_{j}\) represents the result obtained through a series of exchange and decryption operations, used to retrieve the filter to obtain the cardinality of intersection.
15. \(sum\) represents the variable used to help the small health app developer obtain the cardinality of the intersection, where \(sum-k\) represents the cardinality of the intersection.
# Delayed-Expansion Capsule Sealing Material for Coal Mine Overburden Isolated Grouting
## 2 Sealing Mechanism  
The SAP material is a polymer known for its exceptional water absorption and retention properties, with low cross-linking density that makes it stable and only minimally soluble in water [25; 26; 27; 28; 29; 30]. Owing to its high water absorption and high expansion capabilities, SAP incorporates a three-dimensional network structure densely packed with strong hydrophilic groups, such as amino and carboxyl groups [31; 32; 33; 34; 35; 36]. This network allows SAP to absorb water at a rate hundreds of times its own mass, beginning the expansion process immediately upon contact with water, which results in an extremely rapid water absorption rate. The highly absorbent resin SAP swells into an elastic gel with strength. The SAP material has good water retention capability under normal temperature and pressure and does not lose water easily under heating and loading conditions.  
The elastic gel volume of SAP after absorbing water can reach hundreds of times that of the raw material, which is suitable for sealing the running slurry channel. However, SAP has very fast expansion time, expanding rapidly upon contact with water, with the expansion completing in just over ten seconds, which does not satisfy the above-mentioned sealing purpose.  
In this study, a sealing material was developed by unevenly coating the surface of SAP with a thin layer of hydrophobic paraffin wax. The plugging material is composed of hydrophobic paraffin wax and SAP; the paraffin is insoluble in water and has a water-blocking effect. The paraffin wax wrapped around the surface of SAP prevents SAP from coming into contact with water and swelling do not occur. It should be noted that the film covering of SAP is not absolutely uniform, and certain SAP particles with less coating will come into contact with water through water pressure over time. Expansion then occurs in these particles, increasing the area of contact between water and SAP and aggravating the swelling effect. The increase in the expansion force will destroy more paraffin films, and eventually the SAP will be fully exposed to water, expanding rapidly in a short time. In this way, the purpose of delayed expansion can be achieved. Through the adjustment of the mixing ratio, the expansion time can be controlled, so that the sealing material expands after flowing into the leakage fracture. This time-delayed expansion mechanism is designed to satisfy specific sealing requirements, as shown in Figure 1.  
To be applied in overburden isolation grouting engineering, the sealing material prepared based on the SAP must have the following properties. The sealing material should be added to the slurry without affecting the fluidity of the slurry itself. Additionally, the sealing material should have the function of delayed expansion, that is, it should not expand in the dominant flow channel, but it should expand rapidly when the sealing material enters the slurry flow channel.
## 4 Device Characterization  
### I-V Characteristics and Frequency Response  
The I-V characteristics of the fabricated Ge p-i-n PDs with and without integrating the polymer MLs are characterized using the Keysight B1500 semiconductor device analyzer. The dark currents of 29 \(\upmu\)m diameter devices without MLs are measured to be \(\sim\)138 nA at \(-\)3 V bias. By collecting the dark currents from devices in different diameters without ML, the bulk dark current density \(\mathrm{J_{bulk}}\) and the surface dark current density \(\mathrm{J_{surface}}\) are extracted to be around 9.25 mA/cm\({}^{2}\) and 7.56 \(\upmu\)A/cm, respectively. Further measurements show that there is almost no observable difference between the dark currents measured from the Ge p-i-n PDs with and without the polymer MLs. Finally, the responsivities of the 29 \(\upmu\)m diameter devices without MLs are measured using an 850 nm fiber laser connected to a pigtailed focuser. The responsivity is measured to be \(\sim\)0.6 A/W at \(-\)3 V. The typical dark and photo I-V curves measured from the 29 \(\upmu\)m diameter devices without MLs are shown in Figure 10a, and the statistics of dark current at \(-\)3 V bias from three batches of the fabricated Ge p-i-n PDs are shown in Figure 10b. Each batch contains 600 DUTs in the box plot, and the outliers are from the ugly dies. To obtain the 3 dB bandwidths of the fabricated Ge p-i-n PDs, we use the Keysight PNA N5244B network analyzer with a working frequency ranging from 10 MHz to 43.5 GHz to measure the S parameters. An iXblue optical modulator is used to modulate the laser light at 850 nm. Figure 11a shows the frequency response of a fabricated 29 \(\upmu\)m diameter device at \(-\)3 V bias, indicating a 3 dB bandwidth of 22.5 GHz. Figure 11b shows the statistics of 3 dB bandwidth at \(-\)3 V bias from three batches of the fabricated Ge p-i-n PDs. Each batch contains 120 DUTs in the box plot, and the outliners are from the ugly dies. Figure 9: (**a**) A 45-degree view SEM image of an SU8 ML integrated on a Ge p-i-n PD. (**b**) Side-view OM image of the SU8 ML fabricated on a Ge p-i-n PD. (**c**) Top-view OM image of the four-channel Ge p-i-n PD array with integrated SU8 MLs. Table 1 summarizes the performance of the vertical Ge p-i-n PD reported in the literature in recent years. It can be seen that in this work that we have demonstrated the largest QE-BW product, and the dark current densities are comparable with some of the best results. Moreover, among the works showing their QE-BW products, we also demonstrated the largest optical aperture to date.
## 1 Introduction
All these algorithms have been proposed to estimate a better accuracy in the proposed node analysis parameters. However, all these algorithms are not accurate in detecting the location of the unknown nodes and fail to optimize the node's coverage in the WSN [10]. Motivated by all these observations, a novel bio-inspired algorithm, namely, the Bat Algorithm (BA), is based on the way the microbats use echolocation. In terms of accuracy and efficiency, BA is far better than other current usage algorithms. Due to a bat's inability to thoroughly search every direction in the search area, the BA has an extremely low success rate. In order to solve this issue, the algorithm is modified as the Modified Bat for Node Optimization (MB-NO) algorithm to reduce the energy conservation. This is proposed, along with the Q learning method proposed in this paper, to optimize the node coverage and accurately estimate the sensor nodes' proposed localization. Q learning is one of the reinforcement learning algorithms which is not dependent on any data model, i.e., model free learning; reinforcement learning is the subfield of Machine Learning that develops a model's ability to solve issues at their best on its own. Moreover, the proposed method provides energy conservation during node coverage and localization of the nodes in WSN [11]. The layer-wise assumption in the node segments of the proposed system is shown in Figure 1 along with the node-based cluster creation. The major research contributions are:
* This paper outlines the node coverage optimization in WSN using the **Modified Bat for Node Optimization**\((MB-NO)\) algorithm to enhance **the node coverage and reduce energy consumption to provide efficient sensor node localization. **
* A novel \(MB-NO\) algorithm, along with Q learning, has been proposed to widely analyze the accuracy of the node localization and the sensor node precision coverage with the shortest hop distance nodes estimation. * The precision estimation is enhanced for the node coverage in WSN with the help of the received signal strength in the sensor nodes using the novel bio-inspired \(MB-NO\) algorithm. * **The objective function used is the number of nodes and the transmission time (ms), that is defined as:** \[O_{f}=\Sigma\ N(i)*Tt\]  
The Q learning approach to segment the sensor nodes and the localization accuracy is optimized in the proposed system. The paper has been organized as follows: Section 1 outlines the \"Introduction\", Section 2 covers the \"Related Works\", and Section 3 proposes the \"Network Model and Problem Formulation\" with energy optimization to perform the node optimization. Section 4 proposes the \"Modified Bat for Node Optimization \((MB-NO)\)\" algorithm-based node optimization with secure Q learning-based communication. Section 5 performs the \"Performance Evaluation and Results\". Section 6 includes the \"Conclusion and Future Work\".
## 2 Results and Discussion
### Chemical Composition of the Extracts After Derivatization
On the other hand, the antibacterial properties of the sesquiterpenes, such as \(\beta\)-caryophyllene, zingiberene and \(\alpha\)-curcumene, can contribute via the disruption of the cell membranes and the cell walls, thereby influencing the permeability, the release of intracellular components and the intracellular leakage [53; 54]. In conclusion, the Baklouti and Paprika extracts were demonstrated to be rich in bioactive compounds, which can exert stressing and antimicrobial effects on bacterial cells. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Baklouti**} & \multicolumn{4}{c}{**Paprika**} \\ \hline \multirow{2}{*}{**Bacterial Strains**} & \multicolumn{2}{c}{**MIC**} & \multicolumn{2}{c}{**MBC**} & \multicolumn{2}{c}{**MIC**} & \multicolumn{2}{c}{**MBC**} \\  & **24 h** & **48 h** & **24 h** & **48 h** & **24 h** & **48 h** & **24 h** & **48 h** \\ \hline _C. coli_ ATCC 11353 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 \\ _E. coli_ ATCC 35218 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 \\ _L. monocytogenes_ ATCC 7644 & 12.5 & 25 & 25 & 25 & 12.5 & 25 & 25 & 25 \\ _L. monocytogenes_ 641/6 II & 25 & 25 & 25 & 25 & 25 & 25 & 25 & 25 \\ _L. monocytogenes_ 13155/2017 & 25 & 25 & 25 & 25 & 25 & 25 & 25 & 25 \\ _[PERSON]. [PERSON] ATCC 13076 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 & 12.5 \\  [PERSON]. [PERSON] 2 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 & 50 & 50 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 \\ _S. Enteritidis_ 1 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 & 50 & 50 & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 \\ _S. aureus_ ATCC 43300 & 0.78 & 0.78 & 0.78 & 0.78 & \textless{}0.39 & \textless{}0.39 & \textless{}0.39 & \textless{}0.39 \\ _S. aureus_ aA 17 & 25 & 25 & 25 & 25 & 25 & 25 & 25 & 25 \\ _S. aureus_ aA 19 & 25 & 25 & 25 & 25 & 25 & 25 & 25 & 25 \\ \hline \hline \end{tabular}  
MIC and MBC values are expressed as mg/mL and the standard deviation is not reported, being zero for all replicates. MICs, Minimum Inhibitory Concentrations; MBCs, Minimum Bactericidal Concentrations.  
\end{table} 
Table 5: MICs and MBCs after 24 and 48 h of Baklouti and Paprika extracts against the bacterial strains under study.
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 3 Proposed Method
### Feature Extraction  
LR images have less information due to the difference in resolution compared to HR images, and it is important to extract and utilize various features. In order to extract various features, the attention blocks were extracted at different depths. The following Equation (13) is used to extract various features.  
\[F_{n}(x)=AB_{n}(AB_{n-1}(\cdots AB_{1}(x))), \tag{13}\]  
\(F_{n}(x)\) feature extraction, \(AB_{n}(x)\), is the nth attention block and \(x\) is the input feature map. Attention block is a structure that suppresses unnecessary information and emphasizes important information to focus on a specific part in the input feature map. If the structure of the attention block is shallow, the shape can be distinguished, but there is  
Figure 11: Attention Block.  
the problem that it is greatly affected by surrounding noise, and if the structure is deep, high frequencies such as the contour stone or texture of the object are emphasized and may cause loss of positional information. In order to extract various features, two depth structures were used in the attention block. After making attention blocks with three and 10 structures, feature extraction was performed.  
\[F_{s}(x)=AB_{3}(AB_{2}(AB_{1}(x))), \tag{14}\]  
\[F_{d}(x)=AB_{10}(AB_{9}(\cdots AB_{1}(x))), \tag{15}\]  
In Equation (14), \(F_{s}(x)\) uses three attention blocks to distinguish the shape, and \(F_{d}(x)\) in Equation (15) is a formula using 10 attention blocks to extract various features of the object. After that, in order to effectively utilize the features extracted from various depths, each extracted feature map was connected through concatenation. With this concatenation, various features can be used and shared, and fast learning is possible.
# Moving Surface Boundary-Layer Control on the Wake of Flow around a Square Cylinder
## 2 Numerical Calculation Model
### Validity Investigation
The mean drag coefficient \(\overline{C}_{d}=1.015\) of the standard square cylinder is 30.7% lower than that of the single square cylinder, the fluctuation drag coefficient \(C_{d}\prime=0.0128\) is 36.0% lower than that of the single square cylinder, and the fluctuation lift coefficient \(C_{l}\prime=0.324\) is 13.1% lower than that of the single square cylinder. The Strouhal number of the standard square cylinder (\(S_{t}=0.15\)) is slightly higher than that of the single square cylinder. It is shown that a small change in the shape of the rear corner of the square cylinder can effectively reduce the aerodynamic force. However, the vortex shedding frequency will be slightly increased. In the following sections, the numerical results will be compared with the results of the flow around the standard square cylinder. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mesh Density** & \(N_{c}\) & \(N_{mesh}\) & \(N_{nodes}\) & \(\overline{C_{d}}\) & \(C_{d}^{{}^{\prime}}\) & \(C_{l}^{{}^{\prime}}\) & \(\mathbf{S_{t}}\) \\ \hline Scheme 1, coarsest & 40 & 30,344 & 15,387 & 1.523 & 0.024 & 0.446 & 0.146 \\ Scheme 2, coarse & 55 & 36,208 & 18,349 & 1.489 & 0.022 & 0.404 & 0.148 \\ Scheme 3, normal & 70 & 42,092 & 21,323 & 1.465 & 0.020 & 0.373 & 0.148 \\ Scheme 4, dense & 85 & 48,590 & 24,602 & 1.456 & 0.019 & 0.358 & 0.149 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effect of mesh refinement on the calculation result of the flow around a single square cylinder at \(\mathrm{Re}=200\).
## 1 Introduction  
### 1.1 BNCT  
The research and development of Boron Neutron Capture Therapy (BNCT), a new radiation treatment method, has progressed in recent years.  
BNCT is a treatment method that uses boron-10 (\({}^{10}\)B) and neutron reactions to destroy cancer cells [1, 2, 3], and its research and development are advancing with the goal of its widespread adoption. In BNCT, a patient is first administered a \({}^{10}\)B compound for accumulation in cancer cells. Subsequently, low-energy neutrons, either thermal neutrons (approximately 0.025 eV) or epithermal neutrons (ranging from 0.5 eV to 10 keV), are irradiated to induce the following reaction (1):  
\[\begin{array}{ll}{}^{10}\text{B}+\text{n}&\rightarrow{}^{7}\text{Li}+\alpha+ 2.79\text{ MeV (6\%)}\\ &\rightarrow{}^{7}\text{Li}^{+}+\alpha+2.31\text{ MeV (94\%)}\\ &\rightarrow{}^{7}\text{Li}^{+}\rightarrow{}^{7}\text{Li}+\gamma(478\text{ keV})\end{array} \tag{1}\]  
The alpha particles and lithium-7 (\({}^{7}\)Li) nuclei produced via this reaction are classed as high-linear-energy-transfer (high-LET) radiation with high ionization densities along their paths. Unlike low-LET radiation (such as X-rays and gamma rays), which mainly cause indirect effects and are more dependent on cellular radiosensitivity, high-LET radiation induce DNA damage, including hard-to-repair double-strand breaks [4; 5]. This makes recovery more difficult and, consequently, increases the potential for a higher cytotoxic effect against cancer cells [6].  
Several issues remain to be addressed before the practical implementation of BNCT. One of the issues that must be resolved in order to establish BNCT as a treatment method is the development of a system for the real-time observation of its effectiveness during treatment. The therapeutic effect is proportional to the number of 478-keV gamma rays produced by the \({}^{10}\)B(n,\(\alpha\))\({}^{7}\)Li reaction that occurs during BNCT treatment. Therefore, quantitatively determining the therapeutic effects by measuring these gamma rays is possible.
Sensors_**2008**, \(8\), 5270-5302; DOI: 10.3390/s8095270  
_Series of_  
ISSN 1424-8220  
www.mdpi.org/sensors  
_Article_  
Deriving Vegetation Dynamics of Natural Terrestrial  
Ecosystems from MODIS NDVI/EVI Data over Turkey  
**[PERSON] * and [PERSON]**  
Department of Environmental Engineering, Faculty of Engineering & Architecture, Abant Izzet Baysal  
University, 14280 Bolu, Turkey; E-mail: [EMAIL_ADDRESS] (O. G.)  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS];  
Tel.: +90-374-254-1000; Fax: +90-374-254-4558.  
_Received: 30 July 2008; in revised form: 25 August 2008 / Accepted: 28 August 2008 / Published: 1 September 2008_  
**Abstract:** The 16-day composite MODIS vegetation indices (VIs) at 500-m resolution for the period between 2000 to 2007 were seasonally averaged on the basis of the estimated distribution of 16 potential natural terrestrial ecosystems (NTEs) across Turkey. Graphical and statistical analyses of the time-series VIs for the NTEs spatially disaggregated in terms of biogeochemical zones and land cover types included descriptive statistics, correlations, discrete Fourier transform (DFT), time-series decomposition, and simple linear regression (SLR) models. Our spatio-temporal analyses revealed that both MODIS VIs, on average, depicted similar seasonal variations for the NTEs, with the NDVI values having higher mean and SD values. The seasonal VIs were most correlated in decreasing order for: barren/sparsely vegetated land \(>\) grassland \(>\) shrubland/woodland \(>\) forest; (sub)nival \(>\) warm temperate \(>\) alpine \(>\) cool temperate \(>\) boreal \(=\) Mediterranean; and summer \(>\) spring \(>\) autumn \(>\) winter. Most pronounced differences between the MODIS VI responses over Turkey occurred in boreal and Mediterranean climate zones and forests, and in winter (the senescence phase of the growing season). Our results showed the potential of the time-series MODIS VI datasets in the estimation and monitoring of seasonal and interannual ecosystem dynamics over Turkey that needs to be further improved and refined through systematic and extensive field measurements and validations across various biomes.  
**Keywords:** Remote sensing, vegetation indices, ecosystem classification, spatio-temporal modeling, time series analysis.