esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Their study identified 25 radiomic features significantly related to response to neoadjuvant chemotherapy including _GLCM_ and _GLRLM_, with more than half of the features belonging to the latter group [30]. Our study found ten radiomic features (five for each analysis performed); among these features, _GLCM_ and _GLRLM_ showed interesting results. _GLCM_ and _GLRLM_ are features expressing the signal heterogeneity within a lesion. They represent the relative relationship between the distribution and location of the gray level. In general, these features (_GLCM_ and _GLRLM_) are more pronounced in patients with lack of response to neoadjuvant chemotherapy. They are possible expressions of the more pronounced intra-tumoral heterogeneity in non-responders than responders. The _Cluster Shade_ represents a measure of the skewness and uniformity of the _GLCM_, and _Autocorrelation_ expresses the magnitude of the fineness or coarseness of a lesion's texture. Although our results regarding _Cluster Shade_ are in line with this trend, _Autocorrelation_ showed the opposite trend and further investigation is needed to explore this. Among _GLRLM_ features, _LongRunEmphasis_, a measure of the distribution of long run lengths, showed greater value, longer run lengths, and more coarse structural textures. Our \(\Delta\)Radiomics for this feature showed that responders had a wider delta compared to non-responders. This is explicable with a reduced _LongRunEmphasis_ after p-ChT in responders. As already reported in many studies conducted on different tumor types, the heterogeneity of radiomics features is greater in more aggressive tumors in terms of proliferation, angiogenesis, and metastatic spread. These characteristics have often resulted in chemotherapy resistance [31, 32]. The \(\Delta\)Radiomics data of our study are also in line with a study performed by [PERSON] et al. [33]. The study authors analyzed \(\Delta\)Radiomics on pre-ChT in 23 advanced gastric cancer patients. On multivariate analysis, their results showed a significant correlation between the delta _GLCM Contrast_ features and complete pathological response. Additionally, our analysis showed how _Contrast_ features have good diagnostic ability in differentiating responders from non-responders. Despite the interesting results, our study has some limitations, as follows: (a) the small cohort of patients made it impossible to perform training and validation for sub-groups and led to possible instability of the results; (b) the lack of multivariate analysis with clinical and radiological findings; (c) the lack of correlation with histologic and molecular expressions of the tumor; (d) the lack of radiomics analysis on the arterial phase; (e) the lack of different iterative reconstruction levels applied to CT acquisition that might alter radiomic features (we applied fixed iterative reconstruction percentages to avoid bias since this aspect was beyond the aim of the study); and finally (f) the manual segmentation of the whole gastric tumor.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Fundamental Concepts
#### 2.1.1 Channel Model
It is still valid to consider average values as long as the extinction ratio of the signal and the crosstalk signal is equal. This assumption applies to our case, since both link directions are designed equally and the crosstalk arises from the signal itself.
## 3 Materials and Methods
### SCT Framework
#### 3.4.3 Feature Reuse  
The common frame is used again while tracking subsequent frames as input for the SCT framework. The MSM saves the extracted features related to the current frame until reused by the next node, as illustrated in Figure 3. The copy of frame \(N\) is created to be hypothetical frame \(N+1\), highlighting the suggestion as to the last node, and consequently, the MSM reduces the computational complexity.  
Figure 3: The Memory Sharing Mechanism (MSM) shows the features extracted from each frame (not from the first one) using Resnet-50 with FPN. These features are used for current and even future nodes. The computation cost is alleviated by using the last frame N to avoid its duplicate copy in the case of \(N+1\).
## 1 Introduction
### Background and Motivation
The three possible paradigms of spectrum access in CRNs include overlay, underlay, and interweave [25; 26]. In underlay schemes, SU and PU may transmit simultaneously under the constraint of SUs' interference to the PU. On the contrary, in overlay schemes, SU not only transmits its own signal, but also assists the primary transmissions through some relaying techniques. In interweave-based schemes, SU is capable of exploiting the spectrum opportunities via spectrum sensing, hence enhancing the utilization efficiency of spectrum. In interweave CR (I-CR) systems, SUs are allowed to opportunistically exploit the primary network, without interrupting the primary transmissions significantly. The successful integration of the PU with SUs is subject to the condition that a certain QoS level for a PU must be guaranteed, and can be accomplished through spectrum sensing. Hence, reliable spectrum sensing, using software-defined radio technology and an advanced cognition engine, is the most crucial part of CRNs [25; 26; 27; 28]. Spectrum sensing is performed either independently or in a cooperative manner. If a SU detects PU transmission, it needs to release the channel and switch to another idle channel if one is available, or wait if no vacant channel is available. Energy detection (ED), spectral detection, and waveform detection are the common spectrum sensing techniques in I-CRN [27]. The ED scheme is one of the conventional channel sensing approaches. It provides relatively low computational complexity because it relies on PU signal power detection. In addition, a priori knowledge of the PU signal is not required. However, a major drawback for the ED scheme is that it has poor sensing performance in low-SNR conditions. Cooperative spectrum sensing (CSS) is one of the efficient approaches used to combat the hidden terminal problems, deep fading, and shadowing effects. CSS approaches exploit the spatial diversity by deploying multiple distributed SUs [28]. In such a case, a central node, i.e., a fusion center (FC), makes a final decision on the availability of considered channel by combining the local sensing information of multiple SUs. The FC applies data fusion techniques, such as equal gain combining, maximal ratio combination, and Bayesian fusion, or decision fusion techniques such as the _AND_, _OR_, and _K-OUT-N_ rules [27; 28]. When multiple SUs exist, better sensing performance can be achieved while allocating less time to the sensing operations [19; 29].
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 5 Data Analysis  
In the VPP, several local energy sources are connected with others and the utility as well. DGs are extensively used in such network as non-conventional sources (MT, diesel generator, FC) and renewable sources (solar, wind). Storage devices are used to meet the imbalance between generation and demand, in order to reduce the purchase of energy from the grid.  
The test system consists of various RESs such as a PV, WT, MT, FC and battery storage system. The single line diagram of the test system is shown in Figure 5. All DG units are assumed to produce only active power at unity p.f. and are to be operated only in electricity mode. The data of the load demand, forecasted generation, maintenance cost, start-up/shut-down cost and market prices are taken from [PERSON] [44]. The system includes an industrial feeder for supplying the workshop, a commercial feeder for supplying the small office and a residential feeder. A total load of 1695 kW is considered in the literature review for 24 h [44; 49], but in this paper, the scheduling is developed only for the commercial loads considered. The optimal scheduling is developed for the test system with the day-ahead market strategy. Three different scenarios are considered for a more detailed explanation of the test system. It is assumed in the first scenario that all the units are in the ON condition and act within their power limitation. In the second scenario, the PV is assumed to be in the OFF condition. The other generations are within their limits. In the third scenario, the MT and FC are assumed to be in the OFF condition. The other generators are working within their limits and the exchange of energy with the utility is also considered in a constrained manner.
## 4 Result and Discussion
### Boundary Effect Model  
#### 4.2.1 Resistivity Ratio (\(n=R2/r1\))  
To understand the influence of the boundary effect in different resistivity ratios, this study fixed the background value \(R1\) as 1000 Ohm-m, and assumed that the ratio \(n\) of \(R2\) and \(R1\) were 0.25, 0.1, 0.05, and 0.01, respectively, that is, the model of \(R2\) resistivity was 250, 100, 50, and 10 Ohm-m, respectively.  
The results in Figure 9 show that the presence of the medium in line boundary caused a resistivity profile with an unusual resistivity value near the boundary, and the unusual boundary resistivity value was higher than the background value (1000 Ohm-m). The study judged that the current encountered the low resistivity medium in the boundary when it flowed through the boundary. This creates a large amount of current that is concentrated in a low-resistivity zone, and causes the resistivity value of the boundary profile to increase abnormally.  
In the case of \(n<1\) (\(R2/R1\)), with the increase in the resistivity ratio value n, the influence of the boundary effect gradually decreased. As shown in Figure 9, there was an abnormal resistivity value zone in the resistivity profile when the resistivity ratio value n was under 0.1, and was not obvious until the value n reached 0.25. That is, when resistivity ratio is under about 0.1, there are concerns about the boundary effect, which is similar to the situation of the 3D effect.  
Figure 8: Pipeline stratum model showing the influence between the 3D effect range and electrode spacing.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 6 Security Evaluation
### Security Analysis
* **Man-in-the-middle attack:** By the BSPGH scheme, an adversary cannot impersonate a legitimate t-gNB to deceive an MTCD because a temporary session key \(K_{it}\) is established between them using the ECDH. The adversary cannot obtain or modify the temporary session key; thus, it is unable to establish communication with the MTCD. * **Linkability attack prevention:** In the BSPGH authentication process, the PK and TID are periodically updated, while the elements in other messages are random numbers. BSPGH employs ECDH for session key generation instead of using serial numbers, which prevents the common MAC failures or synchronization issues found in symmetric key-based AKA protocols. This approach makes it difficult for attackers to analyze the correlation between different messages or to exploit erroneous messages as vulnerabilities to track a specific device. Consequently, BSPGH effectively safeguards against linkability attacks, enhancing privacy and security in the communication process.
## 2 System Model and Problem Definition
### Multichannel Directional FANET Model  
There is one channel for the control frame and several channels for the data frame. We define a directional multichannel network structure, as shown in Figure 3. In this structure, the time is divided into fixed slots, and each slot is further divided into several channels by frequency. Note that the direction can be divided into several sectors in the same slot on the same channel.  
As the example illustrates in Figure 3, there are three data channels highlighted in blue, i.e., DC1, DC2, and DC3, along with a control channel highlighted in green, i.e., CC. A-D presents the directional communication between UAV A and UAV D. In slot1, there are six pairs of UAVs engaging in communication. Although A-D and B-C are in the same channel, there is no interference between A-D and B-C owing to the spatial reuse of directional communication, as shown in Figure 4.  
Figure 3: Directional multichannel network structure.  
Figure 2: Phased array antenna.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 2 Related Work
### Spatial Queries in Euclidean Distance
#### 2.1.2 Range Queries in Euclidean Distance  
Range queries are an essential query method in database systems [15]. Building upon the concept of a point query, the area around the query point is expanded in range queries, thus forming an area of query scope. Range queries involve three primary components: points of interest, the query point, and the query scope. To retrieve the relevant objects, range queries utilize intersection and containment operations. The distance between the query point and each point of interest is calculated by measuring the straight-line distance based on their respective coordinates. The resulting distances are then compared with the range specified by the query scope. Objects of interest that fall within this range become the query result [16]. For example, considering an area of query scope as depicted in Figure 5, the points of interest (POIs) located within that area, such as P6, P2, and P7, would be retrieved as the results of the range query (Figure 6).  
Figure 5: Red circle represent the area scope for range queries.  
Figure 6: The result of range query using the Euclidean distance method.  
In the realm of range queries, a research study explored continuous range search algorithms, thereby highlighting the limitations of traditional methods designed for static points. The paper emphasizes the inadequacy of these approaches for mobile users in motion. In response, the authors propose two methods for continuous range search, with the first focusing on Euclidean distance [17].  
Another research study by [PERSON], [PERSON], and [PERSON] [18] evaluated three access methods: the R-Tree, spatiotemporal R-tree, and trajectory bundle tree. The performance study involved experiments with spatial range, as well as navigational and combined queries.
## 4 Discussion  
### Drought Stress and Plant Performance  
Drought is one of the major abiotic stresses that causes injurious effects on the growth and productivity of wheat crops throughout the world [34]. Water deficit conditions induce changes in the morpho-physiological and yield-related traits of crop plants by increasing the production of ROS and causing oxidative damage and hormonal imbalance [35; 36]. It also damages the cellular membrane [37] and encourages ABA biosynthesis and its binding to the receptor to initiate the signal transduction that may lead to the closure of stomata [38]. Drought inhibits chlorophyll synthesis and mineral uptake by the roots and their transport to above ground parts, which ultimately results in reducing seed yield, owing to less assimilate portioning [4]. At 30 and 60% FC, drought stress caused a marked reduction in morphological traits, mainly attributed to a reduction in cell division, elongation and expansion [39]. Similar to these findings, our results also showed a severe decline in shoot fresh and dry weights under moderate- to severe-drought stress (60% and 30% FC); this decline may also cause injurious effects to physiological traits. Moreover, similarly, a significant decline in photosynthetic rate and the activity of photosynthetic enzymes and pigments under drought stress was also observed by [40].  
The production of ROS is a fundamental process to transmit cellular signals in plants upon exposure to stress [41]. However, under drought stress, the equilibrium between ROS and the antioxidant defense system is disturbed, inducing oxidative damage to cellular organelles [42]. In our study, under severe and moderate drought (30 and 60% FC), a significant reduction in antioxidant enzyme activities may lead to the enhanced over-accumulation of ROS (i.e., H\({}_{2}\)O\({}_{2}\)) that damages cellular organelles. Moreover, drought stress is also reported to hamper the shoot biomass that might lead to oxidative damage and curtails the efficiency of the photosynthetic system [43].  
Figure 3: Correlation between the morpho-physiological and biochemical traits of the wheat cultivars under different drought levels. SDW: shoot dry weight; RDW: root dry weight; A: photosynthesis rate; E: transpiration rate; Ci: internal carbon dioxide concentration; gs: stomatal conductance; T. Chl: total chlorophyll; H\({}_{2}\)O\({}_{2}\): hydrogen peroxide; SOD: superoxide dismutase; POD: peroxidase; CAT: catalase.
## 4 Numerical Example
\begin{array}{l}\dot{e}_{1}=-10e_{1},\\ \dot{e}_{2}=-e_{2},\\ \dot{e}_{3}=-\frac{8}{3}e_{3},\\ \dot{e}_{4}=-e_{4}.\end{array} \right.\]  
_Its evolution over time is depicted in Figure 3 for initial conditions \(\left(e_{1}\left(0\right),e_{2}\left(0\right),e_{3}\left(0\right),e_{4}\left(0 \right)\right)^{T}=\left(9,7,12,21\right)^{T}\). Clearly, the error decays to zero given sufficient time, which means that the master and slave are synchronized._  
Figure 2: Attractors of the slave system (30) when \(q=0.94\) and \(u_{1}=u_{2}=u_{3}=u_{4}=0\). **Case 2**. : _For \(d=3<m\), we have_  
\[\mathbf{F}=\left( \begin{array}{c}y_{1}+y_{4}^{2}\\ y_{2}+y_{4}\\ y_{3}+1\end{array} \right)\text{ and }\mathbf{M}\left(t\right)=\left( \begin{array}{ ccc}1&\sin t&0\\ 2&0&\frac{1}{1+\ln(t+1)}\\ 0&\frac{1}{1+\sqrt{t}}&3\end{array} \right)\,, \tag{34}\]  
_yielding_  
\[\mathbf{D}_{1}=\left( \begin{array}{cccc}1&0&0&2y_{4}\\ 0&2&0&1\\ 0&0&1&0\end{array} \right)\text{, and }\mathbf{D}_{2}=\left( \begin{array}{c}2y_{4} \\ 1\\ 0\end{array} \right). \tag{35}\]  
_In order to satisfy control rule (22), we must first calculate \(\mathbf{D}_{1}^{-1}\), which turns out to be_  
\[\mathbf{D}_{1}^{-1}=\left( \begin{array}{ccc}1&0&0\\ 0&\frac{1}{2}&0\\ 0&0&1\end{array} \right). \tag{36}\]  
_The control constants \(\left(c_{i}\right)_{1\leq i\leq 3}\) can then be chosen as_  
\[\left(c_{1},c_{2},c_{3}\right)=\left(1,2,3\right).\]  
_The error system is described by_  
\[\left\{ \begin{array}{c}\dot{e}_{1}=-e_{1},\\ \dot{e}_{2}=-2e_{2},\\ \dot{e}_{3}=-3e_{3}.\end{array} \right.\]  
_Figure 4, displays the time evolution of the error with initial conditions \(\left(e_{1}\left(0\right),e_{2}\left(0\right),e_{3}\left(0\right)\right)^{T}= \left(36,6,3\right)^{T}\):_  
\[\left( \begin{array}{c}e_{1}\left(0\right)\\ e_{2}\left(0\right)\\ e_{3}\left(0\right)\end{array} \right)=\left( \begin{array}{c}36\\ 6\\ 3\end{array} \right). \tag{37}\]  
Figure 3: Time evolution of the synchronization error for the 4D case. **Case 3**. : _As for the two-dimensional case, we consider the matrices_  
\[\mathbf{F}=\left( \begin{array}{c}2y_{1}+y_{2}+y_{3}y_{4}\\ y_{1}+2y_{2}+y_{3}^{2}+y_{4}^{2}\end{array} \right)\text{ and }\mathbf{M}\left(t \right)=\left( \begin{array}{cc}3&0&\cos t\\ 0&1&\sin t\end{array} \right), \tag{38}\]  
_which leads to_  
\[\mathbf{D}_{1}=\left( \begin{array}{ccc}2&1&y_{4}&y_{3}\\ 1&2&2y_{3}&2y_{4}\end{array} \right),\ \mathbf{D}_{2}=\left( \begin{array}{cc}y_{4}&y_{3} \\ 2y_{3}&2y_{4}\end{array} \right)\text{ and }\mathbf{D}_{1}^{-1}=\left( \begin{array}{cc}1&0 \\ 0&1\end{array} \right). \tag{39}\]  
_In this case, the control constants can be chosen as \(\left(c_{1},c_{2}\right)=\left(0.1,0.2\right)\) and yielding the error system_  
\[\left\{ \begin{array}{c}\dot{e}_{1}=-0.1e_{1},\\ \dot{e}_{2}=-0.2e_{2}.\end{array} \right.\]  
_The time-evolution of the error is depicted in Figure 5 for \(\left(e_{1}\left(0\right),e_{2}\left(0\right)\right)^{T}=\left(31,10\right)^{T}\)._
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 4 Proposed Spoken Language Identification System
### Selected Features
#### 4.3.2 GTCC  
Gammatone Cepstral Coefficient (GTCC) is often referred to as Frequency Cepstrum Coefficient (GFCC) or Gammatone Cepstrum Coefficient (GTCC). The Gammatone filter is inspired by biological life, in which the representation of the human auditory filter response in the cochlea is very similar to the magnitude response of a Gammatone filter [44]. The method of extracting GTCC is similar to MFCC, with two main differences. The first is the frequency scale used, where GTCC provides greater resolution at low frequencies than MFCC. This is because it is based on the equivalent rectangular bandwidth scale, while MFCC is based on Mel-scale. The GTCC uses the cubic root, whereas the MFCC employs the log.  
Due to its high accuracy and low complexity, MFCC is frequently used for sound processing systems. However, it does not have high resistance to noise. Recent investigations have revealed that the properties of GTCC, by contrast, are that it is particularly resistant to noise and acoustic change [45]. With these considerations in mind, the primary goal is to combine the characteristics of GTCC and MFCC to improve the overall testing accuracy of the system.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
###### Abstract  
The lattice Boltzmann method (LBM) is an alternative method to the conventional computational fluid dynamic (CFD) methods. It gained popularity due to its simplicity in coding and dealing with a complex fluid flow such as the multiphase flow. The method is based on the kinetic theory, which is mesoscopic scale. Hence, applying the LBM method for macroscopic problems requires a proper conversion from the physical scale (conventional units) to the mesoscopic scale (lattice units) and vice versa. The Buckingham \(\pi\) theorem and the principle of corresponding states are the popular methods used for data reductions and unit conversion processes in the LBM. Nevertheless, those methods have some issues, such as difficulty in converting specific quantities, such as thermophysical properties. The current work uses a novel dimensional analysis method systematically for mapping properties' units between scales. Moreover, the approach has the flexibility in selecting parameters to ensure the stability of the method of solution. Several benchmark examples are used to evaluate the feasibility and accuracy of the proposed approach. In conclusion, the proposed approach showed the flexibility of the mapping between meso-scale to macro-scales and vice versa on solid bases rather than ad-hoc methods.  
lattice Boltzmann method; unit conversion; advection-diffusion; phase change; differentially heated cavity; surface tension +
Footnote †: journal: Applied Sciences