esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 2 Design Methodology
### Steps in Design Evolution  
The key goals in optimizing the antenna design were to (1) expand the FBW, (2) attain high levels of isolation, and (3) achieve CP characteristics with an enhanced ARBW.  
The size of the radiating patch can be determined by the following equation:  
\[L=\frac{c}{2\times f\sqrt{\varepsilon_{r}}} \tag{1}\]  
where the resonant frequency is represented by \(f\), \(\varepsilon_{r}\) (2.2) is the substrate relative permittivity, the velocity of light is denoted by \(c\) (3 \(\times\) 10\({}^{8}\) m/s), and \(L\) symbolizes the effective length of the radiation patch.  
The MIMO antenna is characterized by a continuous ground plane, which is shared by both radiating units. The design steps are shown in Figure 3a,b, illustrating the comparison of the results of S\({}_{11}\), S\({}_{21}\), and AR corresponding to each design step. In Step I, a slot is introduced into the radiating patch, resulting in the antenna resonating at 2.8 GHz. It has isolation greater than 22 dB and a 3 dB ARBW of 0.14 GHz (2.53-2.67 GHz). In Step II, to shift the resonance point and the axial ratio point to the left, two L-shaped rectangular slots are integrated into the ground plane. This modification influences the antenna's impedance characteristics, resulting in a leftward shift of the resonance point  
Figure 2: (**a**) Simulation environment, (**b**) IMD architecture, and (**c**) physical and measurement setup for the antenna.  
and an expanded bandwidth. In Step III, vertical slits are added at the ends of the L-shaped rectangular slots. These slits introduce additional capacitance to the antenna, increasing its reactance. Consequently, the resonance frequency is lowered, resulting in enhanced impedance matching. However, this modification reduces the antenna's isolation and the effectiveness of the circularly polarized AR. Thus, in Step IV, a horizontal slot is incorporated to connect the two U-slots in the ground plane. Ground-plane slotting introduces an additional reactance effect on the ground plane, which helps to achieve more stable circular polarization characteristics while increasing the ARBW. The findings reveal that the antenna achieves a resonant frequency of 2.45 GHz and an isolation level exceeding 27.5 dB. Additionally, it achieves an FBW of 28.16% and an effective ARBW of 16.3%.  
Figure 3: (**a**) Steps in the design evolution process. (**b**) \(\ket{S_{11}}\), \(\ket{S_{21}}\), and AR simulations of the antenna at each stage of the design evolution.
## 5 Effective Data Augmentation Investigation Experiment
### Experimental Conditions  
As explained in Section 3.2, we applied transformation steps to the training images for data augmentation. However, it is not clear which step contributes the most to the classification. In addition, the range values, \(\theta_{r}\) and \(\alpha_{r}\), were determined a priori.  
By the data augmentation described in Section 3.2, we verified the step which contributes the most to the classification accuracy.  
In this experiment, we chose one transformation step from the steps explained in Section 3.2 and applied the chosen transformation separately to the training images.  
* Rotation: rotate the image by angle \(\theta\). Angle \(\theta\) varies randomly in the range from \(-\theta_{r}\) to \(\theta_{r}\).
* Brightness change: Randomly change the brightness of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\).
* Contrast change: Randomly change the contrast of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\).
* Saturation change: Randomly change the saturation of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\).  
Figure 8: Activation visualization by gradient-weighted class activation mapping (Grad-CAM): (**a**) _Aedes albopictus_, (**b**) _Anopheles stephensi_, and (**c**) _Culex pipiens pallens_.  
* Hue change: Randomly change the hue of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\).  
Regarding rotation, the limit value \(\theta_{r}\) varied at 5\({}^{\circ}\) intervals from 0\({}^{\circ}\) to 45\({}^{\circ}\). Therefore, the 10 conditions were prepared. Conditions A to J denote the ranges of angle \(\theta\); [0 (no rotation,\(\theta_{r}=0\))], [\(-5\) to 5 (\(\theta_{r}=5\))],..., [\(-45\) to 45 (\(\theta_{r}=45\))], respectively.  
Regarding the brightness, contrast, saturation, or hue change, the limit value \(\alpha_{r}\) varied at five intervals from 0% to 50%. Therefore, the 11 by 4 conditions were prepared. Conditions A to K denote the ranges of change rate \(\alpha\); [0 (no change, \(\alpha_{r}=0\))], [\(-5\) to 5(\(\alpha_{r}=5\))],..., [\(-50\) to 50 \(\alpha_{r}=50\))], respectively.  
We utilized the dataset with three types of mosquitoes, comprising 12,000 images for training and 900 images for validation. Training and validation were performed five times by considering each condition, and the average of classification accuracy was calculated. Generally, 270 (54 conditions by five trials) trials of training and validation were performed.
## 3 Materials and Methods
### Dataset  
In order to perform classification learning to classify surveillance videos into one of several classes of anomalies, a labeled dataset of videos is required. Various datasets are used by the research community to demonstrate anomaly detection in surveillance videos, and each of these datasets has its own characteristics [2; 39; 53; 54]. This study is based on the UCF-Crime dataset [2], which is modified to make it more useful for the demonstration of anomaly classification for low-quality surveillance videos.  
There are 128 hours of surveillance footage in the original UCF-Crime dataset. The dataset includes 1900 complete and unfiltered surveillance videos from the real world, along with thirteen actual anomalies such as assault, arrest, abuse, arson, burglary, fighting, shooting, explosion, road accident, vandalism, robbery, and shoplifting. These anomalies were included in the dataset due to their possible impact on the safety of the general public. We meticulously curated the dataset to address class imbalance by retaining a standardized set of 50 videos per class to ensure the relevance and practicality of our study. Because of this deliberate selection process, classes with insufficient representation were excluded, resulting in a focused dataset with eight distinct categories: assault, arrest, abuse, arson, burglary, fighting, explosion, and normal. This strategic enhancement to the UCF-Crime  
Figure 1: Proposed framework (TempoFuseNet) for anomaly classification for low-resolution videos.  
dataset ensures a balanced and representative collection, which improves the precision and applicability of our experimental results. All videos in each class have the same spatial resolution of \(320\times 240\) pixels, which contributes to the consistency and reliability of our analytical framework.  
Data Preparation  
In order to perform learning on low-quality videos, the original videos are downsampled by eight times to obtain a low-resolution version of the original videos. The video resolution after downsampling is \(40\times 30\) pixels. Downsampling is performed by using bilinear interpolation (refer to Equation (1)), in which the target image pixels are obtained by performing linear interpolation in both the horizontal and vertical directions.  
Let \(LR(x^{\prime},y^{\prime})\) be the low-resolution pixel values at coordinates \((x^{\prime},y^{\prime})\), and let \(HR(x,y)\) be the high-resolution pixel values at coordinates \((x,y)\). The downsampling operation can be expressed as:  
\[LR(x^{\prime},y^{\prime})=f(HR(x,y)) \tag{1}\]  
where  
\[x^{\prime}=[\;x/8],\quad y^{\prime}=[\;y/8] \tag{2}\]  
This stage results in two sets of data: one containing high-resolution (HR) videos that are the ground truth data, and the other has low-resolution (LR) videos, which are a downsampled version of the data and will be used for classification modeling.
## 4 GTP-Dependent Activation of CTPSs from Other Species
### Saccharomyces cerevisiae  
There are two isoforms of CTPS in _S. cerevisiae_ encoded by the _URA7_ and _URA8_ genes [106; 107]. For _Sc_CTPS7 encoded by _URA7_, which is responsible for the majority of CTP made in vivo [107], activation by GTP was not found to be an absolute requirement for CTPS activity but did enhance the activity 4-fold [96]. Double-reciprocal plots of the initial velocity data, obtained with Gln as the variable substrate with various fixed concentrations of GTP, exhibited concave-downward curves, consistent with negative cooperative kinetics with respect to Gln and GTP. [PERSON] and co-workers conducted a series of studies examining the phosphorylation of _Sc_CTPS7 by protein kinases A (PKA) and C (PKC) [47; 97; 98; 99; 108]. Phosphorylation by PKA at Ser 424 [58] in the GAT domain [99] resulted in a slight decrease in the \(K_{\rm A}\) value for GTP from 15.3 to 12.0 \(\upmu\)M, but did not have a significant effect on the apparent \(V_{\rm max}\) and \(K_{\rm m}\) values with respect to Gln when measured either in the absence or presence of GTP. Interestingly, the phosphorylation did eliminate the negative cooperativity exhibited with respect to Gln [98]. This behavior was also observed when the enzyme was phosphorylated by PKC, including a slight decrease in the \(K_{\rm A}\) value from 15.7 to 12.2 \(\upmu\)M [97]. Finally, to assess the role of the mobile L11 loop adjacent to the allosteric GTP-binding cleft in filament formation by _Sc_CTPS7, the abilities of the GFP-tagged variants R381M, R381P, and G382A (_cf._ Arg 356 and Gly 357 in _Ec_CTPS) to form filaments in vivo were examined. Interestingly, these variants exhibited a ~3.1-fold increase in the number of yeast cells forming filaments compared with strains expressing wild-type _Sc_CTPS7. Moreover, the median length of the filaments was increased by 33% and 15% for the R381M and R381P variants, respectively, relative to the wild-type enzyme. The G382A variant also exhibited an increased number of cells with filaments; however, the median length of the filaments was reduced by 25%. To date, this is the only report of residues at the GTP-binding site contributing to regulation of both the frequency of filament formation and filament length [58].  
The _Sc_CTPS8 isoform was also activated by GTP and, again, the requirement for GTP was not absolute [74]. Binding of GTP did not alter the enzyme's affinity for Gln and, at a saturating concentration of Gln, GTP stimulated Gln-dependent CTP formation by 12-fold (\(K_{\rm A}=26\)\(\upmu\)M).
_Membranes_**2012**, \(2\), 275-306; doi:10.3390/membranes2020275  
_Membranes for Redox Flow Battery Applications_  
**[PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 2,3* and [PERSON] 1,* * 1**  
School of Chemical Engineering, The University of New South Wales, UNSW Sydney, NSW 2052, Australia; E-Mail: [EMAIL_ADDRESS]  
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore; E-Mails: [PERSON]DRESS] (A.P.); [EMAIL_ADDRESS] (S.W.)  
School of Life Sciences and Chemical Technology, Ngee Ann Polytechnic, Singapore 599489, Singapore  
Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (M.S.K.); [EMAIL_ADDRESS] (T.M.L.); Tel.: +61-2-9385-4335 (M.S.K.); +65-6790-4876 (T.M.L.).  
_Received: 8 May 2012; in revised form: 1 June 2012 / Accepted: 7 June 2012 / Published: 19 June 2012_  
**Abstract:** The need for large scale energy storage has become a priority to integrate renewable energy sources into the electricity grid. Redox flow batteries are considered the best option to store electricity from medium to large scale applications. However, the current high cost of redox flow batteries impedes the wide spread adoption of this technology. The membrane is a critical component of redox flow batteries as it determines the performance as well as the economic viability of the batteries. The membrane acts as a separator to prevent cross-mixing of the positive and negative electrolytes, while still allowing the transport of ions to complete the circuit during the passage of current. An ideal membrane should have high ionic conductivity, low water intake and excellent chemical and thermal stability as well as good ionic exchange capacity. Developing a low cost, chemically stable membrane for redox flow cell batteries has been a major focus for many groups around the world in recent years. This paper reviews the research work on membranes for redox flow batteries, in particular for the all-vanadium redox flow battery which has received the most attention.  
energy; redox flow batteries; membrane; stability; ionic conductivity
## 4 Uncertainty Quantification
### Uncertain Stability Analysis  
In this study, the viscosity \(\mu\) and clearance \(c\) are considered random input parameters and represented by truncated gPC expansions as  
\[\mu(\xi_{1})=\sum_{i=1}^{N_{\mu}}u_{i}\psi_{i}(\xi_{1}),\quad c(\xi_{2})=\sum _{i=1}^{N_{c}}c_{i}\psi_{i}(\xi_{2}). \tag{35}\]  
Under the influence of the random input parameters, the static and dynamic characteristics of HDB become uncertain, as does the stability threshold of the coupled rotating system. On this basis, the uncertain characteristics of HDB, such as \(p_{\max}\) and \(K_{xx}\), and the uncertain stability threshold are approximated by truncated gPC expansions as  
\[p_{\max}(\varepsilon,\xi)=\sum_{i=1}^{N_{p}}p_{i}(\varepsilon)\psi_{i}(\xi), \quad K_{xx}(\varepsilon,\xi)=\sum_{i=1}^{N_{k}}k_{i}(\varepsilon)\psi_{i}( \xi),\quad\Omega_{\text{th}}(\xi)=\sum_{i=1}^{N_{\Omega}}\Omega_{i}\psi_{i}( \xi), \tag{36}\]  
where \((\varepsilon,\cdot)\) represents the eccentricity ratio dependency. The step-by-step procedure for analyzing the stability of the rotor-bearing system under uncertainty is then summarized as Algorithm 2.  
```
1: Define the rotor-bearing system and identify the random input parameters;
2: Establish the polynomial basis functions for uncertain output parameters based on the distribution of the random variables;
3: Generate the collocation points by combining the roots of higher-order polynomials and also check if the origin is included;
4: Calculate the responses at the generated collocation points using the deterministic stability analysis solver;
5: Evaluate the coefficients of the gPC expansion for each uncertain output parameter using the least squares minimization technique;
6: Estimate the statistical properties of the uncertain output parameters--mean, standard deviation, and corresponding PDF.
```  
**Algorithm 2** Stability analysis of rotor-bearing system under uncertainty
# Dog Identification Method Based on Muzzle Pattern Image
## 1 Introduction
Like a typical biometric system, the proposed method consists of four main steps: preprocessing, feature extraction, matching, and postprocessing. In the preprocessing step, two techniques are proposed related to image resizing and histogram equalization. In the feature extraction step, four leading general-purpose feature extraction algorithms are applied and compared. Those algorithms are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scaling Keypoints (BRISK) and Oriented FAST, and Rotated BRIEF (ORB). In the matching step, Fast Library for Approximate Nearest Neighbors (FLANN) is used for SIFT and SURF, and hamming distance are used for BRISK and ORB. In the postprocessing, another two techniques are proposed related to removing incorrect matches. The proposed method was evaluated with 1045 dog muzzle pattern images which consist of 55 original images, and 990 images augmented by the image deformation (i.e., angle, intensity, affine, noise). The Equal Error Rate (EER) of the proposed method was 0.35%, and ORB was the most appropriate for the dog muzzle pattern recognition.
## 5 Performance Evaluation
Therefore, with the proposed RSPWVD method, the disturbing interference is highly localized in the restricted region of the TF plane, and a much improved TF localization property can be achieved in comparison to the SPWVD approach. The proposed joint TF distribution based on RSPWVD has been proven to be valid and effective in interference detection for GNSS receivers in jamming environments. This proposed joint TF analysis technique based on RSPWVD effectively solves the cross-term problem of the bilinear TF distribution and keeps good time and frequency resolution in the TF plane at the same time, which has been proven to be effective to adopt in interference detection for GNSS receivers. With this developed interference detection technique, the interfering signal can be correctly  
Figure 11: Smoothed pseudo Wigner–Ville distribution of the GPS L1 signal with sweep interference. \(C/N_{0}\) = 46 dB-Hz, JNR = \(-1\) dB. identified, and its instantaneous frequency can be precisely estimated. In the following interference excision/mitigation unit (anti-jamming device), the estimated instantaneous frequency of the interfering signal will be further used to control the coefficients of an excision filter (notch filter) that adaptively removes the disturbing interference signal.
## 3 State-of-the-Art for WirelessHART Routing Schemes
### Non-WirelessHART Routing Algorithms
The accumulation of signal levels determines the most reliable routes; the hop count avoids endless rebroadcast (the packet is discarded if the hop count is greater than the node depth); and the previous node address keeps track of the path covered by each packet. After receiving the list-update messages, the nodes of the networks report to the gateway with a list-response message. This message contains information about the neighboring nodes (siblings, parents and children) and the next hop from that device. The gateway uses this information to build the network topology. Each list-update message has a sequence number attached to it in order to avoid duplicate packets. Finally, the gateway sends list-confirm messages to all nodes with the confirmation of related node lists and scheduling decisions. The aforementioned packet forwarding part consists of a set of criteria that restricts the flooding of messages, in order to save the network resources. The first one is the definition for each node \(n\) of a local related node list \(L_{n}\), which contains a list of addresses from the nodes considered related to \(n\). A node \(x\) is considered to be related to node \(n\) if \(x\) is involved in forwarding packets between \(n\) and the gateway. Thus, the criteria define that once a node \(n\) receives a packet, the node will only consider forwarding this packet if the source or destination address from the received packet belongs in \(L_{n}\). The second one consists of marking the packets with a unique identifier. This identifier comprises the sequence number of the packet and the source address of the current node. The identifiers of the packets are stored in each node using a history table H, and once a new data packet is received, the incoming identifier is compared to those present in H. If the identifier is already present in H, the packet is a duplicate and must be dealt with accordingly. Finally, the last criterion is the packet age. Outdated packets can be of limited use in industrial networks; thus, each packet carries an age value; this age value is compared to the deadline value for that packet before being sent out. If the age value is greater than the deadline value, the packet is discarded in order to avoid buffering in the intermediate nodes. The drawback of the routing algorithm presented by the authors in [72] is the increase in the energy consumption due to the use of the flooding algorithm and the focus on reliability and real-time performance.
## 3 Results
### K-Means of DEGs
#### 3.2.2 Globally Up-Regulated Genes  
Similarly, we followed this procedure for the up-regulated genes. In particular, the optimal number of clusters was calculated to be 6 and 7 (Figure 5A), presented as k-means clusters (Figure 5B,C). The clusters B1, B2, B6, and B7, manifested possibly globally up-regulated genes (Figure 5C), whose HCLs are presented in Figure 6. We identified 30 genes, which were globally up-regulated in >80% of all tumor samples (Figure 6). The identified genes included YES1, PMM1, IRF3, ARL6 IP1, CYTH2, LASP1, PSMD2, PSMD7, DDOST, NDUFV2, RALY, ADRM1 (Figure 6B), HMGA1, RPN1, TXN, TYK2, UNG, EBNA1 BP2, IGF2R, PCNA, PTMA, SNRPB, USP7, and VDAC1 (Figure 6C), CDC20, KRT14, APOBEC3B (Figure 6D), YWHAB, STMN1, and MCM5 (Figure 6E). The globally up-regulated genes are summarized in Table 2.  
Figure 6: HCL of the k-means clusters presented in Figure 2D (cluster B) (**A**) and Figure 5C (cluster B1, cluster B2, cluster B6, and cluster B7) (**B–E**).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Inv.** & **Gene Name** & **Cluster** & **_n_ & **(\%)** \\ \hline
1 & YES1 & B1 & 125 & 95.42\% \\
2 & PMM1 & B1 & 123 & 93.89\% \\
3 & CDC20 & B6 & 123 & 93.89\% \\
4 & IRF3 & B1 & 122 & 93.13\% \\
5 & MCM5 & B7 & 122 & 93.13\% \\
6 & ARL6 IP1 & B1 & 121 & 92.37\% \\
7 & CYTH2 & B1 & 121 & 92.37\% \\
8 & LASPI & B1 & 121 & 92.37\% \\
9 & PSMD2 & B1 & 121 & 92.37\% \\
10 & PSMD7 & B1 & 121 & 92.37\% \\
11 & DDOST & B1 & 120 & 91.60\% \\
12 & NDUFV2 & B1 & 120 & 91.60\% \\
13 & RALY & B1 & 120 & 91.60\% \\
14 & STMN1 & B7 & 120 & 91.60\% \\
15 & ADRM1 & B1 & 119 & 90.84\% \\
16 & HMGA1 & B2 & 119 & 90.84\% \\
17 & RPN1 & B2 & 119 & 90.84\% \\
18 & TXN & B2 & 119 & 90.84\% \\
19 & TYK2 & B2 & 119 & 90.84\% \\
20 & UNG & B2 & 119 & 90.84\% \\
21 & EBNA1 BP2 & B2 & 118 & 90.08\% \\
22 & IGF2R & B2 & 118 & 90.08\% \\
23 & PCNA & B2 & 118 & 90.08\% \\
24 & PTMA & B2 & 118 & 90.08\% \\
25 & SNRPB & B2 & 118 & 90.08\% \\
26 & USP7 & B2 & 118 & 90.08\% \\ \hline \end{tabular}
\end{table}
Table 2: Globally up-regulated genes in \(\geq\)80% of all tumor samples.
## 2 Evaluation Method of Inertia Distribution Characteristics  
When the power system experiences a large disturbance, the inertia of generators helps to stabilize the frequency and prevent its deviation. The synthetic inertia includes the rotational inertia of SGs and the frequency support of RESs and ESSs through the power electronic devices. Due to the differences in inertia support capability of generators, the key features of frequency dynamic process including RoCoF and frequency nadir are significantly different, and these can be described as inertia distribution characteristics. Considering the network equation of power systems, an evaluation method for inertia which can evaluate the inertia level of generators in the power system is proposed in this section. Compared with SGs, the mode of GFM-ESSs participating in the frequency dynamic process is different. In this section, inertia response model of GFM-ESSs will also be established.