esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
## 3 Proposed Method: DenseNet-LSTM
### Deep Learning Architecture
#### 3.3.3 Hybrid Model  
As shown in Figure 8, we propose a hybrid model that combines DenseNet and LSTM. The proposed model uses the structure of DenseNet to construct the first half. We use the feature map from here as input data of LSTM to reflect the sequence information on the feature and finally propose a hybrid model that classifies through the sigmoid function. Specifically, the input data are image data converted by applying DWT to the raw EEG signal and are composed of frequency (DWT level), time, and channel. The input image first passes through the Conv layer and makes an output feature map that is twice the growth rate. Next, all dense blocks each have the same number of layers, and Conv(3 \(\times\) 3) in them does 1-pixel zero-padding so that the size of the feature map does not change. After the dense block, the transition layer is used. Transition layers reduce the size of the feature map through Conv(1 \(\times\) 1) and apply average pooling. Finally, instead of a fully connected layer that increases parameters too much, global average pooling is used to create and output the feature map as a 1-D vector. Then, through reshape, it is converted into an input format suitable for LSTM and input into LSTM. Finally, the features generated through LSTM are classified into interictal and preictal states using the Sigmoid function. The detailed structure is shown in Table 1.  
Figure 8: Architecture of DenseNet-LSTM.  
Figure 7: Structure of the LSTM cell.
# Underwater Sea Cucumber Identification Based on Improved YOLOv5
## 3 Sea Cucumber Identification Network
### Loss Function  
The most commonly used calculation metric for bounding box regression loss is Intersection over Union (IoU) [17; 24], as shown in Equation (4).  
\[\text{IoU}=\frac{|A\cap B|}{|A\cup B|} \tag{4}\]  
IoU algorithms are widely used because of scale invariance and non-negativity. However, since the IoU does not consider the distance between the real box and the prediction box, the direct use of the IoU as a Loss function will expose various problems: if there is no overlapping part between the prediction box and the real box, according to the definition, the IoU is 0, and the distance between the two boxes cannot be calculated. Due to the different alignment of different angles in different directions, the IoU will be exactly the same, and it is impossible to judge the coincidence between the prediction box and the real box. The YOLOv5 algorithm uses Generalized Intersection over Union_Loss (GIoU_Loss) as a loss function for bounding box regression [25; 26]. GIoU pays attention to the overlapping parts as well as other non-overlapping parts, which can more precisely describe the various overlaps between the prediction box and the real box. Suppose \(A\) is the prediction box and \(B\) is the real box, so that \(C\) represents the smallest closed box containing \(A\) and \(B\). The calculation formula (5) of the GIoU_Loss is as follows.  
\[\text{GIoU\_Loss}=1-\text{GIoU}=\text{IoU}-\frac{|C/(A\cup B)|}{|C|} \tag{5}\]
# Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples
###### Abstract  
In this work, we develop a multi-mode microscopic hyperspectral imager (MMHI) for the detection of biological samples in transmission imaging, reflection imaging and fluorescence mode. A hyperspectral image cube can be obtained with 5 um spatial resolution and 3 nm spectral resolution through push-broom line scanning. To avoid possible shadows produced by the high magnification objective with a short working distance, two illumination patterns are designed to ensure the co-axiality of the illumination and detection. Three experiments for the detection of zebrafish and fingerprints and the classification of disaster-causing microalgae verify the good capability and functionality of the system. Based on the detected spectra, we can observe the impacts of \(\beta\)-carotene and melanin in zebrafish, hemoglobin in the fingertip, and chlorophyll in microalgae, respectively. Multi-modes can be switched freely according to the application requirement and characteristics of different samples, like transmission mode for the transparent/translucent sample, reflection mode for the opaque sample and fluorescence mode for the fluorescent sample. The MMHI system also has strong potential for the non-invasive and high-speed sensing of bio or clinical samples.  
multi-mode microscopic hyperspectral imager; epi-illumination and trans-illumination; bio-sensing; microalgae classification; principal component analysis +
Footnote †: journal: Applied Physics Letters
## 4 Simulation Results and Analysis  
We assess the effectiveness of the proposed JAS-OUS algorithm and its improved scheme JAS-PFUS, and then provide an evaluation of computational complexity. The channel is modeled as a Rayleigh fading channel. Assuming that a transmitter equipped with 16 antennas is communicating with 16 single antenna users. There are four RF chains in the transmitter, and four target users are selected. Unless otherwise specified, the system parameters are configured as above. All simulations are conducted in MATLAB (R2024b, the MathWorks, Inc., Natick, MA, USA, 2024). The parameters used in the simulations are listed in Table 2.
## 3 Numerical Simulation Results
### Analysis of Numerical Results
#### 3.4.2 Displacement Analysis  
Displacements in the outer rigid supporting structures were recorded in the numerical calculation to study whether BFRGC could reduce the deformation of the outer rigid supporting structures. As shown in Figure 10a, the monitored points were located on the right side, the floor, and the roof of the roadway. It is noteworthy that the monitored displacements in Case 1 were the deformation of the surrounding rocks of the roadway rather than that of the supporting structures. The displacement history curves monitored under the various supporting schemes are shown in Figure 10.  
Figure 10: _Cont._  
It can be observed from Figure 10 that under the supporting conditions, the displacement of the monitoring points for five cases had consistent features in some aspects, e.g., the deformation of the roadway floor was the largest, followed by the right side of the roadway, and then the roadway roof, which explains the reason why the deformation of the roadway floor in Cixi No.1 was so severe. When the numerical calculation reaches the equilibrium state, the major deformation of the roadway was located at the floor, and the maximum deformation was up to 22.40 cm under unsupported condition. The maximum displacements at the floor were about 13.17 cm, 9.70 cm, 9.69 cm, 12.11 cm, and 15.36 cm, respectively, under the supporting ways of Case 2, Case 3, Case 4, Case 5, and Case 6. The deformations of the roadway in Case 3 and Case 4 were obviously smaller compared with other cases. Therefore, it could be concluded that when the mass fraction of basalt fibers and rubber particles in BFRGC was 0.4% and 5-10%, respectively, the BFRGC could effectively control the deformation in the rigid supporting structures. Additionally, the slight difference of the deformations at the floor among different cases was partially due to the small support thickness of concrete.  
By extracting the displacement from four monitoring lines arranged at the roof, floor, and sides, respectively, the displacement curves of the roadway at different depths with different support schemes are shown in Figure 11. It can be seen that under the support of the BFRGC with the optimum ratios, the maximum deformations at four monitoring lines were limited at approximately 10 cm, and the floor heave was controlled effectively.  
Figure 10: Recorded displacements for different cases. (**a**) Case 1; (**b**) case 2; (**c**) case 3; (**d**) case 4; (**e**) case 5; (**f**) case 6.
## 4 Challenges and Future Research Areas
### Interference Issues  
Interference and channel allocation is another important issue that requires further study. Payload and CNPC are the two communication types that are used concurrently and incorporated in UAVs. However, they are not currently standardized, and both use distinctive operating bands, which may or may not coincide. The CNPC links are critical to ensure flight safety, and interference would be destructive. Moreover, they necessitate robust security measures and invulnerability to jamming or hacking transmissions. In any case, both the payload and the CNPC should operate in highly protected frequency bands, in order to ensure safe, secure, and interference-immune communications. Nevertheless, regulations and frequency assignments cannot always protect airborne links from interference, which would downgrade the communication efficiency of UAVs. Therefore, it is fundamental to evaluate and measure potential interference (usually unintentional), taking into consideration various environments. Interference measurements should be carried out in densely inhabited areas (e.g., urban and suburban environments), and in industrial environments, both of which present an increased likelihood of interference in different frequency bands. Different parameters have to be evaluated, such as the interferers' location, the interfering power, the antenna diagrams, the duty cycle, and even various modulation schemes. All these provide valuable knowledge that will assist in implementing proper mitigation measures. Adaptive modulation and coding algorithms could be employed as measures against interference, especially when unlicensed bands are used. However, these do not perform appropriately in highly dynamic A2A and A2S channels, where mobility is introduced and stationarity conditions do not apply.
## 3 Results
### Effect of SJFE on Weight-Bearing Index (WBI) of OA-Induced Rats  
After inducing OA on the left knees of rats, SJFE (0 to 200 mg/kg) was orally administered once a day for 28 days, and changes in body weight and WBI were monitored. The body weights did not show significant differences among the groups [Figure 4A]. On the other hand, WBI, which indicates the distribution of weight between the normal right and affected left legs in the standing position, exhibited a significant difference in 3 days. This suggests that OA was successfully induced in the left knee. However, a significant difference was observed in WBI between the control group and the group with 200 mg/kg of SJFE administration [Figure 4B]. This recovery became particularly significant on day 28 [Figure 4C]. The effect of SJFE was comparable to that of methyl sulfonyl methane (MSM), an anti-arthritic agent used as a positive control (Supplementary Table S1).  
Figure 2: Effect of SJFE, quercetin, and rutin on the cell viability of RAW 264.7 cells and NO production in LPS-stimulated RAW 264.7 cells. The cells were incubated with SJFE of varying concentrations, quercetin, or rutin without or with LPS. After incubation, supernatants were used for cell viability using MTT test or NO production with nitrite assay. Data are presented as mean \(\pm\) SD in triplicate experiments. LPS, lipopolysaccharide-SJF, ethanol extract of _S. japonicum_ flowers. \({}^{\#}\), \(p<0.01\) vs. no addition (student’s \(t\)-test); \({}^{\star}\), \(p<0.01\) vs. LPS (1 \(\upmu\)g/mL) (One-way ANOVA); and \({}^{\$\$}\), \(p<0.01\) vs. LPS (1 \(\upmu\)g/mL) (student’s \(t\)-test).  
Effects of SJFE on Serum Inflammatory Cytokines and Cartilage Damage Marker Proteins in OA-Induced Rats  
WBI of OA-induced rats increased at the end of the experiment (on day 28) treated with SJFE [Figure 4B], suggesting the alleviation of OA. To support this finding, the levels of serum inflammatory cytokines, such as TNF-\(\alpha\), IL-1\(\beta\), IL-6, C-terminal telopeptide 2 (a cartilage-degrading enzyme: CTX-II), and the cartilage oligomeric matrix protein (degraded cartilage products; COMP) were evaluated on the 28 th day. The serum levels of TNF-\(\alpha\), IL-1\(\beta\), and IL-6 were significantly increased in OA-induced rats. The increased inflammatory cytokines levels were decreased by the administration of SJFE, but no significant difference was observed [Figure 5A-C]. Even MSM showed no effect. In contrast, the elevated levels of CTX-II and COMP induced by OA were reduced by SJFE (Figure 5D,E). This result indicates that cartilage degradation was significantly inhibited, as was the case with MSM.
## 3 Materials and Methods
### Fractal Dimension Combination Pipeline
#### 3.2.3 Classification and Performance Evaluation  
The classification phase included five models: LinearSVM, CART, GSVM, SVM with a polynomial kernel, and SGD. These classifiers were implemented to cover a wide range of both linear and nonlinear EEG classification techniques.  
LinearSVM has been widely recognized for its ability to efficiently handle high-dimensional datasets, a characteristic relevant to EEG signals [56]. GSVM, building on the standard SVM, introduces a nonlinear kernel that can capture complex dependencies in the data [54]. CART is a decision tree technique known for its interpretability and capacity to model nonlinear relationships, although it may be prone to overfitting when dealing with a large number of EEG-derived features [55]. SGD was added to this ensemble due to its computational efficiency and effectiveness in scenarios involving incremental or large-scale learning, making it a practical choice in scenarios characterized by rapidly changing experimental conditions [57]. Finally, the polynomial kernel SVM extends the linear SVM framework to handle more complex structures in EEG signals, offering additional flexibility by mapping data into higher-order feature spaces [56].
## 4 Results and Discussion
### The Vulnerability Index (\(Kg\))  
The vulnerability index (\(Kg\)) is used to assess soil instability and earthquake-induced phenomena such as liquefaction and landslide. In our study area, the vulnerability index was used to identify areas prone to liquefaction, as an alternative to other geotechnical-based analyses (e.g., [65]). The calculated \(Kg\) values range from approximately 3 to 50 (Figure 6). According to [49], sites with higher \(Kg\) values (\(Kg\geq 10\)) are at greater risk of liquefaction. The results of our analysis suggested that the overall potential for liquefaction in the area is relatively low. However, certain regions, notably the industrial zone NE of the city and to the south of the hospital, show a higher \(Kg\) value, suggesting higher susceptibility. The boreholes show that in the industrial zone, thick sandbanks (\(\geq\)7 m) are observed at shallow depths. Also, the piezometric wells indicate the presence of ground water at shallow depths [16]. The presence of sand banks and groundwater at shallow depths indicates a high risk of liquefaction. This led the industrial zone to be considered as an area with high potential of liquefaction, as already mentioned in the Geomatrix report [16].  
Figure 5: Some examples of the experimental HVSR curves recorded at some selected sites in the studied region. The dashed lines correspond to the maximal and minimal standard deviations of the amplitudes. The gray vertical band corresponds to the fundamental frequency. Light gray corresponds to the minimum standard deviation of \(f_{0}\), and dark grey corresponds to its maximum standard deviation.  
The range of the vulnerability index for Ain T\(\hat{\text{{\em{T}}}}\)mouchen (0-50) is slightly lower than the one calculated for the city of Oran (5-75 [41]). This variation is mainly attributed to differences in the lithology and local geological conditions, with the presence of eruptive hard rocks in Ain T\(\hat{\text{{\em{T}}}}\)mouchent. Also, the thickness of the sedimentary cover directly influences the vulnerability index of the soils, as the \(Kg\) value is inversely proportional to the \(f_{0}\) value, and since the \(f_{0}\) value in Ain T\(\hat{\text{{\em{T}}}}\)mouchent is higher than that in Oran, this could explain the difference in the vulnerability index.
## 3 Preliminaries
### Core Switching
#### 3.4.3 Spectrum Constraints  
In MCF-EONs with core switching, although a connection request can occupy variable cores on different links of its selected path, the spectrum constraints of continuity, consistency, and overlapping still need to be satisfied. Spectrum continuity refers to each request occupying continuous spectrum resources on the selected core. All three requests in Figure 4 occupy the continuous FSs. Spectrum consistency means that each request occupies the same FSs (location) on different links, and cores can be switched on these selected links. Taking request 2 of three FSs as an example, it is transmitted by FSs 4, 5, and 6 of core 3 on link 1. When it is transmitted on link 2, it can occupy different cores. To meet the constraints of spectrum consistency, the FSs 4, 5, and 6 on core 1 are allocated. Spectrum overlapping means that each FS on the specific core can only be occupied by one request at a time. Generally, there should be a protection FS between two adjacent spectrum segments allocated to requests on the same core, so as to ensure that the transmitted optical signal passes through the cross nodes in EONs without being damaged and is correctly received by the receiver. For example, an FS between request 2 and request 3 on link 1 is reserved as the protection bandwidth.
## 2 System Model  
The block diagram of our proposed system is presented in Figure 1; it includes a temperature and humidity sensor module, a liquid level sensor module and a BQ34Z110 module [14]. The temperature and humidity data is estimated around the battery with the use of a chipcap-D sensor [15]. The battery's acid level, one of the most important parameters to be determined, is evaluated through an electrolyte level sensor module explained in detail in the next subsection. The battery's important parameters, namely, the full charge capacity, the remaining charge capacity, the state of charge, voltage, and average current are assessed through the BQ34Z110 module. For data transmission between the device and the microcontroller, the i2c protocol is used. We have used Philip i2c protocol [16] that is provided in the Cvavr library [17]. The ADC is also used for communication between the device and the microcontroller. The battery data from the sensors are received and the calculation and the analysis part is implemented in the microcontroller. Further, for our proposed system, the data is transferred to a Wi-Fi card (WizFi) using a UART protocol. For a clear representation and working procedure, the pin map detailed diagram of our proposed system, with the corresponding inputs and ports, is shown in Figure 2. It should be observed that in our proposed system the communication between the various parts of the system is done using WLAN technology, considering the mobility of the vehicles and the batteries in an industrial environment. The vehicles are self-governing and can connect to any of the gateways installed in the premises of the industry.
## 7 IoT-Defined Application Protocols
### Data Distribution Service (DDS)
#### 7.7.4 Enhanced Yet Simplified Security  
DDS API allows a \"many-to-many\" session update algorithm with channel security. Simple DDS searches data by either name or value and makes connections. Secure DDS documents queries and enforces certain security schemes that are already developed and coded in API for establishing a secure channel and system. DDS also supports secure multicasting. The publishers and the subscribers are connected to the topic, while the topic name and its configuration are used as security terms for information exchange. This enforces dataflow between a known publisher and a known subscriber without any intruder or data misplacement.  
Table 8 gives the qualitative comparison of different IoT communication protocols. It clearly provides evidence that DDS is a relatively better choice for decentralized fault tolerant networking and data security with configurable encryption and encoding.
## 2 The Applications in Smart Grid Based on Time Synchronization
### Traveling Wave Fault Location  
Traveling waves are high-frequency electromagnetic pulses that propagate along a transmission line in both directions from the point of the fault. These waves propagate at nearly the speed of light. During a fault, the wavefronts are detected based on their arrival times at each terminal of the transmission line, allowing the fault location to be determined using the total line length and the recorded arrival times of the wavefronts [34].  
The traveling wave fault location (TWFL) method requires high sampling rates to have good resolution and accuracy [35]. The performance of TWFL depends on the accurate detection of wavefront arrival times, the sampling frequency, and precise time synchronization [36]. To accurately locate a fault within hundreds of feet, TWFL demands synchronization accuracy about hundreds of nanoseconds [23], translating to clock synchronization within approximately one microsecond.
# The Refined Simulation Methods of Fluctuating Wind and Aerodynamic Forces Relative to Moving Trains
## 3 The Refined Aerodynamic Model of Moving Trains  
### The Aerodynamics Model of Moving Trains  
Generally, it is unreasonable to ignore the effects of the lateral and vertical fluctuating winds on the aerodynamic forces of a train. Accounting for the longitudinal, lateral, and vertical fluctuating winds, the lateral force acting on the moving train, as an example, can be expressed as follows [8]:  
\[\bar{F}_{S}=\frac{1}{2}\rho\bar{V}_{R}^{2}HL\mathcal{C}_{S}(\varphi) \tag{22}\]  
\[F_{S}(t)=\frac{1}{2}\rho\bar{V}_{R}^{2}HL\bigg{[}C_{Su}(\varphi)\frac{u(t)}{ \bar{V}_{R}}\chi_{Su}(n)+C_{Sv}(\varphi)\frac{v(t)}{\bar{V}_{R}}\chi_{Sv}(n)+C _{Sw}(\varphi)\frac{w(t)}{\bar{V}_{R}}\chi_{Sw}(n)\bigg{]} \tag{23}\]  
\[\bar{V}_{R}=\sqrt{[V_{tr}+Ucos(\varphi_{0})]^{2}+[Usin(\varphi_{0})]^{2}} \tag{24}\]  
\[C_{Su}(\varphi)=\frac{dC_{S}(\varphi)}{d\varphi}sin(\varphi_{0}-\varphi)+2C_ {S}(\varphi)\frac{V_{tr}cos(\varphi_{0})+U}{\bar{V}_{R}} \tag{25}\]  
\[C_{S^{0}}(\varphi)=\frac{dC_{S}(\varphi)}{d\varphi}cos(\varphi_{0}-\varphi)+2 C_{S}(\varphi)sin\frac{V_{tr}}{\bar{V}_{R}} \tag{26}\]  
\[C_{Sw}(\varphi)=\frac{dC_{S}(\varphi)}{d\alpha} \tag{27}\]  
where \(\bar{F}_{S}\) is the lateral force caused by the mean wind; \(F_{S}(t)\) is the unsteady lateral force caused by the fluctuating wind; \(t\) denotes time; \(\rho\) notes the air density; \(H\) and \(L\) are the height and length of the train, respectively; \(U\) is the mean wind speed; \(V_{tr}\) is the vehicle speed; \(\varphi_{0}\) is the angle between the speed \(U\) and \(V_{tr}\); \(C_{S^{0}}(\varphi)\) is the lateral force coefficient, where \(\xi=u,v,w\) correspond to longitudinal, lateral, and vertical fluctuating winds; \(\alpha\) is the wind attack angle; \(\varphi\), \(\theta\), \(\beta\) are the wind direction angles; \(\chi_{S\xi}(n)\) is the admittance function of the train; and \(n\) is the Hertz frequency.  
For consideration of the along-wind fluctuating winds, Equation (23) is consistent with the expression of the unsteady aerodynamic force of the moving train derived by [PERSON] et al. [15] as \(\varphi_{0}=90\) degrees, which is as follows:  
\[F_{S}(t)=\rho UHL\mathcal{C}_{Su}(\varphi)\bigg{[}1+\frac{1}{2C_{Su}(\varphi) }\frac{dC_{Su}(\varphi)}{d\varphi}cot(\varphi)\bigg{]}u(t)\chi_{Su}(n) \tag{28}\]  
The expression of the lift force of the moving train is similar to Equations (22) and (23), with only the subscript '\(S\)' replaced in the expression with '\(L\)'. In other words, all the aerodynamic parameters related to the lateral force should be replaced by that of the lift force.