esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Results and Discussion Regarding Hypothesis 1  
In the present study, we analyzed various water quality parameters for twelve samples collected from three different areas at four locations. This analysis was conducted during three distinct seasons: pre-monsoon, monsoon, and post-monsoon. We selected Nangloi, Karol Bagh, and the Okhla Industrial Area as our study districts, where we established 12 water sampling stations. These stations were used for collecting samples three times each and conducting three rounds of testing in the laboratory during the pre-monsoon, monsoon, and post-monsoon seasons of the year 2020. The results have been highlighted through spatial distribution maps created with the assistance of ArcGIS. The parameters for the 12 water samples from the four stations in the Nangloi district during the three monsoonal periods (pre-monsoon, monsoon, and post-monsoon) are shown in Figures 3-5:  
* pH--high 8.21 and low 7.4, high 8.11 and low 7.12, high 8.14 and low 7.09;
* Nitrate (mg/L)--high 67.0 and low **2.0**, high 58.2 and low **2.0**, high 58.2 and low **2.0**;
* Total Hardness (mg/L)--**832.0** and low **7.0**, high **77.2** and low **35.6**, high--**678.3** and low **39.2**;
* Chloride (mg/L)--high 831.2 and low **7.9**, high 792.4 and low **7.5**, high 657.9 and low **10.2**;
* Fluoride (mg/L)--high **1.7** and low **0.1**, high 1.3 and low **0.1**, high 1.2 and low **0.1**;
* **TDS (mg/L)--high **2263.3** and low **103.1**, high 1923.2 and low **79.2**, high 1766.5 and low 773.8;
* Calcium (mg/L)--high 115.3 and low **1.8**, high 109.7 and low **8.2**, high 107.3 and low **10.5**;
* Magnesium (mg/L))--high **130.5** and low **0.7**, high **118.3** and low **2.9**, high 98.4 and low **3.1**;
* Sulphate (mg/L)--high **221.4** and low--**17.8**, high 195 and low **14.7**, high 187.9 and low **9.8**;
* Alkalinity (mg/L)--high 224.1 and low **46.6**, high 198.4 and low **41.3**, high 179.3 and low **38.9**. The pH levels at all stations in the Nangloi district during the three monsoonal periods fall within the acceptable limits. However, for most of the stations marked in bold, the rest of the parameters exceed the limits specified in the BIS standard for drinking water The Bureau of Indian Standards (BIS) has established IS 10500: 2012 as the official Indian standard for drinking water quality. This standard was first introduced in 1983 under the title \"Indian Drinking Water Specification\" and has undergone several revisions since then, with the most recent one occurring in 2012. [[[https://www.pureitwater.com/blog/post/standards-for-drinking-water-in-india](https://www.pureitwater.com/blog/post/standards-for-drinking-water-in-india)]([https://www.pureitwater.com/blog/post/standards-for-drinking-water-in-india](https://www.pureitwater.com/blog/post/standards-for-drinking-water-in-india)) (accessed on 6 July 2024)]  
The parameters measured for the 12 water samples collected from the four stations in the Karol Bagh district during the three monsoonal periods (pre-monsoon, monsoon, and post-monsoon) are presented in Figures 6-8.
###### Abstract  
The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.  
UWSNs; energy-efficient data transmission; economic game theory; NIP problem; E-DDTMD  
2023235759
## 4 Physical Interpretation of Steric Parameters
### Parameter \(Q_{sat}\)  
Parameter \(Q_{sat}\) is the adsorption amount at saturation, whose expression is directly determined by the multilayer model when the hydrocortisone or dexamethasone concentration at equilibrium tends to infinity. This factor plays a pertinent role in showing the affinity of hydrocortisone/dexamethasone molecules towards the surfaces of MNIPs and MMIPs.  
Adsorption capacity at saturation depends on the parameters \(n\), \(D_{m}\), and \(N_{c}\) through the following relation [36; 37]:  
\[Q_{sat}=n\times D_{m}\times N_{c} \tag{5}\]  
The maximum adsorption capacities of hydrocortisone are 15.41 and 22.83 mg/g on dual-template MNIPs and MMIPs, respectively, while the adsorption amounts of dexamethasone at saturation are 18.7 and 38.01 mg/g, respectively, on MNIP and MMIP adsorbents (Table 2). It is obvious that the adsorption capacity of MMIPs is much higher than that of MNIPs at the same concentration of hydrocortisone and dexamethasone. This indicates that MMIPs show much better affinity towards dexamethasone or hydrocortisone, and that they preserve the spatial cavities of the template well. Furthermore, this difference is due to the imprinting effect of each adsorbent. Consequently, dexamethasone and hydrocortisone can undergo adsorption onto various sites of the MMIPs. This theoretical result is in agreement with the experimental data, which revealed that the MMIPs possessed a more elevated initial adsorption rate and a more elevated saturated adsorption capacity than the MNIPs [21].
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
## 4 Experimental Results
### Ablation Experiment  
In order to prove the effectiveness of MFCosface, ablation experiments were performed on the generated dataset LFW_m and the real dataset MFR2. According to the experimental results of LFW_m in Table 3, the corresponding ROC (receiver operating characteristic curve) was drawn, as shown in Figure 9. The ROC graph uses the false positive rate and the true positive rate as the coordinate axes, reflecting the relationship between them, and can better represent the model's recognition ability. AUC (area under the curve) is the area under the ROC curve. The higher the AUC value, the stronger the classification ability of the model. It can be seen from the experimental results that the accuracy was improved regardless of whether a single method or a combination of multiple methods was used. The highest accuracy was obtained when the three methods were used at the same time. In terms of ROC, the MFCosface method was also significantly better than the other methods, and the addition of each method improved the performance of the model.  
It is worth noting that in our Cosface + CBAM experiment, the combination of these two methods was not as good as Cosface. This is because the training dataset did not contain masked-face images. At this time, the attention mechanism made the model focus on the entire face, not areas that would not be occluded by the mask. This had an adverse effect; that is, when the face is occluded by the mask, the extracted facial features are severely obscured, resulting in a decrease in the recognition accuracy. The masked method solves the problem of insufficient data, the Cosface method optimizes the distribution of the feature space based on the data generated by the masked method, and CBAM makes the model more capable of learning useful features and is committed to solving hard samples. These three methods complement each other, making the model have stronger masked facial recognition capabilities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Set** & **MFR2** & **RMFD** \\ \hline Facenet & VGGFace2\_mini & 84.25 & 73.77 \\ Softmax & VGGFace2\_mini & 92.50 & 90.30 \\ Cosface & VGGFace2\_mini & 92.75 & 92.03 \\ Arcface & VGGFace2\_mini & 93.11 & **92.28** \\ MFCosface & VGGFace2\_m & **98.50** & 92.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition accuracies on real datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **LFW\_m** & **MFR2** \\ \hline Facenet & 83.40 & 84.25 \\ Masked & 93.58 & 89.75 \\ Cosface & 96.82 & 96.75 \\ Cosface + CBAM & 96.70 & 96.25 \\ Masked + Cosface & 99.23 & 97.00 \\ MFCosface & **99.33** & **98.50** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.
## 5 Discussion  
The gravity and AMT data were jointly interpreted to delineate the subsurface structure, and image the extension and geometry of the granitic rocks. From the inversion results of the AMT data, resistivity maps at various depths (1, 1.5, 2.5, 3.0, 3.5, and 4.0 km) were extracted from 2D resistivity models, as shown in Figure 7.  
Figure 7: _Cont._  
In a geothermal context, the study area had two potential targets. The first was the high-heat granites, which were well-delineated and mapped beneath the study area using the geophysical methods; namely, the AMT and gravity methods. The second was the thick sedimentary sections, which were mainly detected near the coastal parts of the study area. These could act as hot sedimentary aquifers.  
The resistivity maps presented in Figure 7 show that the granitic rocks exhibited a good lateral and vertical extension, especially in the central and western parts of the study area. By estimating the volume of these granitic rocks within the optimal range of the cut-off temperature, a good reserve estimate could be executed. Assuming a cut-off grade temperature of 150 \({}^{\circ}\)C, these granitic rocks could be used for possible energy production. [PERSON] et al. [28] calculated the possible energy production.  
The presence of good fracture and fault systems as well as medium-resistivity weakened granitic rocks is a key factor in selecting the best locations for drilling wells (exploration, injection, and production), which might suggest a full exploitation program of these geothermal resources. An appropriate energy transfer medium such as supercritical CO2 can be injected into the fracture system of EGSs for possible energy production. However, more detailed geomechanical measurements are required to understand the mechanical properties of these granites.  
Two important areas were deduced from the data analysis; i.e., areas with low resistive values (e.g., the blue polygons in Figure 8) and areas of high resistive values (the red crossed lines in Figure 8). Areas with low resistivity were considered to be hot sedimentary aquifers whereas areas with high resistive values represented the extension of high-heat granitic rocks.  
The thicknesses of the sedimentary layers in these two areas were matched with the seismic reflection data of [PERSON] et al. [47], who concluded that a temperature of 150 \({}^{\circ}\)C could be reached at a depth of 2500 m in the near-shore sections. Considering the thick sedimentary section (up to 3000 m) and assuming the temperature regime provided by [47], this area could be suggested as a good geothermal target (hydrothermal system).  
Figure 7: Extracted resistivity maps from AMT data inversion at various depths.
# How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
###### Abstract  
As non-humanoid robots develop and become more involved in human life, emotional communication between humans and robots will become more common. Non-verbal communication, especially through body movements, plays a significant role in human-robot interaction. To enable non-humanoid robots to express a richer range of emotions, it is crucial to understand how humans recognize the emotional movements of robots. This study focuses on the underlying mechanisms by which humans perceive the motion arousal levels of non-humanoid robots. It proposes a general hypothesis: Human recognition of a robot's emotional movements is based on the perception of overall motion, and is independent of the robot's mechanical appearance. Based on physical motion constraints, non-humanoid robots are divided into two categories: those guided by inverse kinematics (IK) constraints and those guided by forward kinematics (FK) constraints. Through literature analysis, it is suggested that motion amplitude has the potential to be a common influencing factor. Two psychological measurement experiments combined with the PAD scale were conducted to analyze the subjects' perception of the arousal expression effects of different types of non-humanoid robots at various motion amplitudes. The results show that amplitude can be used for expressing arousal across different types of non-humanoid robots. Additionally, for non-humanoid robots guided by FK constraints, the end position also has a certain impact. This validates the overall hypothesis of the paper. The expression patterns of emotional arousal through motion amplitude are roughly the same across different robots: the degree of motion amplitude corresponds closely to the degree of arousal. This research helps expand the boundaries of knowledge, uncover user cognitive patterns, and enhance the efficiency of expressing arousal in non-humanoid robots.  
human-robot interaction; non-humanoid robot; emotional expression; arousal expression +
Footnote †: journal: Applied Sciences
# A New General Type-2 Fuzzy Predictive Scheme for PID Tuning
## 2 Literature Review
In [28], the wavelet NNs are used to construct a predictive scheme for PID. The PSO algorithm is developed in [29], to design a predictive PID. The Hagglund's controller [30] is developed for second-order processes and it is called a predictive PID and the effect of dead time is investigated. In [31], a predictive sequence of output of a power system is obtained and then a PID is designed. In [32], the bacterial foraging algorithm is used to determine the parameters of the PID and it is combined with a predictive controller in parallel. In the most of above reviewed papers, the designed predictive controllers can not handle the high noisy nonlinear conditions and also the designed controller just can be applied for special case-study systems. Also, some optimization algorithms with high computational cost are used that are not appropriate for high speed practical plants. The main contributions are as follows:  
* A simple practical controller is proposed based on good features of popular PID and MPC methods. * A type-2 fuzzy approach is suggested for online modeling that improves the MPC performance. * An online optimization scheme is presented to handle the online unpredicted disturbances. * The better performance is shown by several simulations.
## 6 Application to a Real Dataset  
This section evaluates the performance of MCDO and SARIMA models on the widely recognized AirPassengers dataset, a benchmark in time series forecasting. The dataset contains monthly totals of international airline passengers from 1949 to 1960, originally introduced by [PERSON] et al. [40], and is available in many statistical software libraries as well as public repositories such as Kaggle. Due to its well-documented structure and periodicity, it has become a standard resource for evaluating forecasting models. Previous studies, including those by [PERSON] & [PERSON] [41], [PERSON] [42], and [PERSON] [43], have extensively analyzed this dataset, confirming its relevance in both traditional and machine learning-based forecasting frameworks.
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 2 Antenna Design
### Design Evolution Methodology
Through a parametric study, multiple design options were considered and their effectiveness was tested to arrive at this conclusion. Therefore, the current design appears to be the most efficient and effective option for achieving the desired bandwidth.
## 6 Results and Comparison  
### Performance Results  
From all monitored signals, the outputs of the equalizer and demapper modules are exported and recorded. The saved results corresponding to all frame symbols are then compared with reference results. For all system configurations, the results acquired from the FPGA prototypes have been verified to match exactly the performance of the corresponding reference software model. Figure 16 and Figure 17 show, respectively, the receiver bit error rate (BER) performances obtained after on-chip evaluation of the designed equalizer and demapper. Furthermore, the figures show the equivalent reference software model of 1536 source bits, using 4 \(\times\) 4 MIMO over a fast fading Rayleigh channel for different numbers of iterations with code rate \(R_{c}=\frac{1}{2}\) and considering the QPSK and 16-QAM modulation schemes. The hardware-measured BER shows acceptable performance degradation when compared to floating-point C simulations.  
Figure 16: Comparison between the simulated reference BER and measured BER after on-chip evaluation of the designed equalizer for 1536 source bits, a \(\frac{1}{2}\) code rate, 4 \(\times\) 4 MIMO and a fast fading Rayleigh channel.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 7 Countermeasures for Threats in Cloud Computing
### Digital Signatures and Message Digests
#### 7.6.2 Digital Signatures  
Digital signatures, based on asymmetric cryptography, provide robust authentication. Combining Single Sign-On (SSO) with Lightweight Direct Access Protocol enhances authentication strength. Additionally, various other methods are proposed for digital signatures, offering anonymity and traceability, addressing issues like free riders of Software as a Service (SaaS) and proof of data possession.
## 3 Materials and Methods
### VIT Module  
VIT (Vision Transformer) is an image processing model based on the Transformer architecture, first proposed by [PERSON] et al. in 2020 [32]. It processes image data through the self-attention mechanism, as shown in Figure 5.  
Unlike traditional Convolutional Neural Networks (CNNs), VIT does not rely on convolution operations. Instead, it captures the relationships between different regions of the image by modeling global dependencies. In remote-sensing image processing tasks, VIT is capable of effectively handling high-resolution remote-sensing data, capturing spatial information and global contextual relationships. In this study, VIT is used as the encoder to extract spatial features from the input remote-sensing images. Specifically, we divide the remote-sensing images into fixed-size patches, flatten each patch into a one-dimensional vector, and apply linear projection. Then, the self-attention mechanism and feed-forward networks are used to generate feature representations. This approach allows VIT to capture global dependencies in remote-sensing images, overcoming the limitations of traditional convolutional networks when capturing large-scale features. The process is represented by the following equation (see Formulas (1)-(4)):  
\[N=HP\times WP \tag{1}\]  
where P is the patch size (e.g., P = 16), and N is the total number of patches.  
\[z_{((t,i))}=W_{\text{proj}}\text{-flatten}\Big{(}x_{((t,i))}\Big{)}+b_{\text{ proj}} \tag{2}\]  
\[z^{\prime}_{((t,i))}=z_{((t,i))}+PE(i) \tag{3}\]  
where \(x_{((t,i))}\) is the flattened vector of the \(i\)-th patch; \(W_{\text{proj}}\in R^{((D\times(P^{2}\cdot C)))}\) is the projection weight matrix; \(b_{\text{proj}}\in R^{D}\) is the bias vector. PE(\(i\)) is the positional encoding vector corresponding to the patch position \(i\).  
\[\text{Attention}(Q,K,V)=\text{Softmax}\Bigg{(}\frac{(QK^{T})}{\sqrt{((d_{k}) )}}\Bigg{)}V \tag{4}\]  
Figure 5: Principle of the VIT technology, where * stands for positional encoding.  
where \(Q\) is the query matrix, \(K\) is the key matrix and \(V\) is the value matrix, all generated from the input embeddings through linear transformations. \(d_{k}\) is the dimension of the key vector, used for scaling to prevent excessively large values.  
In this study, the advantage of the VIT module lies in its ability to fully utilize the spatial information and global context in remote-sensing data, thereby enhancing its ability to recognize the growth stages and temporal changes in non-food crops. In this way, VIT not only extracts fine-grained spatial features but also increases the model's sensitivity to the spectral characteristics of crops at different growth stages.
## 2 Materials and Methods
### Service System Unit  
The service system boards were installed in the service system unit on four racks. The preliminary sequence of installing the boards in each rack is shown in Figure 5. The racks are mounted on a large control board. The service systems were developed taking these factors into account during the small spacecraft flight tests [48].  
Due to the requirements for the orientation of the photodetector at Nadir, the satellite has an active three-axis altitude control system consisting of flywheels [49] with unloading  
Figure 4: The basic arrangement of the detector element and the viewing area.  
Figure 5: Racks of boards with service systems.  
by magnetic coils. Two magnetic coils are located on each of the external panels on the X and Y axes and four are located on the Z axis of the control board. To determine the orientation of the spacecraft, data from the star sensor, solar sensor, accelerometer and magnetometer are used.  
The power supply system provides 80 W of power and a voltage of 12 V to the equipment. The batteries are LiFePO4 elements of the ANR26650 type. These elements are characterized by a large resource and a high current output. Solar panels are built on the base of AsGa solar cells with an efficiency of 28.8 % [50].  
For transferring the target information to the ground control center, radio communication systems in the UHF, S and X bands are used.  
To change the satellite's orbit, three electric propulsion engines are used [51; 52]. It is proposed to use Teflon as a working medium, since it has a high density (2.21 g/cm\({}^{3}\)) and a low after-evaporation, a factor that reduces the characteristics of an ablative pulsed plasma engine.