esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Finite Element Simulation of Multi-Cavity Pneumatic Gripper
### Simulation of Wall Thickness and Bending Angle of Soft Actuator Cavity  
Under the action of the air pressure input on the actuator, the cavity of the excessive pressure actuator deformed to both sides of the drum with the increase in pressure, as shown in Figure 6.  
The influence of cavity wall thickness on the bending angle is shown in Figure 7. Besides, the relationship between the bending angle and air pressure of the air cavity wall was stimulated when the cavity wall thickness was 0.5 mm, 1 mm, 1.5 mm, and 2 mm, respectively. Through detailed analysis, it is found that the wall thickness exerted a great influence on the bending angle simulation, which was a key factor affecting the bending deformation performance of the actuator. When the thickness is 0.5 mm, the air pressure reaches about 30 kPa as the cavity wall thickness was thus too thin. Consequently, the bending angle would change quickly, and the simulation graphics show a red warning. When the thickness of the air cavity wall is 1.0 mm, the simulation results show that the bending angle of the actuator tends to be large, and the soft actuator bending is close to 150 degrees after compression reaches about 40 kPa. When the wall thickness is 1 mm and the air pressure is in the range of 0-30 kPa, the soft actuator is linearly related to the  
Figure 5: Soft bending actuator trajectory for bending angle from 0 kPa to 70 kPa.  
Figure 6: Diagram of increasing pressure drum in soft actuator.  
air pressure. When the air pressure is above 30 kPa, the air pressure of the soft finger is not linearly related to the bending angle of the finger, and overexpression occurred. It shows that the expansion ratio of the bending layer is close to the elastic limit of the material. In the actual experiment when other parameters remain unchanged, the wall thickness continues to increase. When it is greater than 2 mm, the bending radius is less than 2 mm. If the wall thickness is 3.0 mm, the bending change of the actuator is very small when the air pressure is too low. Even if the pressure increases to about 90 kPa, the bending amplitude of the soft gripper cannot achieve good results in the actual grasping process; thus, it is not desirable. When the wall thickness is 2.0 mm, the bending angle is in line with that of the expected results as long as the experimental operation can be carried out in the corresponding pressure range. Through comprehensive analysis, 2 mm wall thickness was selected for the experiment under the condition that other parameters remained unchanged.
## 3 The Proposed Resource Allocation Scheme for D2D Communication
### Subcarrier Assignment
2:repeat
3:\((k^{*},m^{*})=\operatorname*{argmax}_{(k,m):k\in\mathcal{K},m\in\mathcal{C} _{k}}R_{k,m}^{CD}\Big{(}p_{k,m}^{D*}\Big{)}\);
4:if\(P_{k*}^{ini}+p_{k*,m*}^{D*}\leq P_{\text{thr}}^{D}\) and \(p_{k*,m*}^{D*}>0\)then
5: Set \(\pi_{k*,m*}=1\);
6:\(P_{k*}^{ini}=P_{k*}^{ini}+p_{K,m}^{D^{*}}\);
7:\(\mathcal{M}_{k}=\mathcal{M}_{k}\cup\{m^{*}\}\);
8:\(\Omega=\Omega\backslash\{m^{*}\}\);
9:\(C_{k}=C_{k}\backslash\{m^{*}\},\forall k\in\mathcal{K}\);
10:else
11:\(C_{k*}=C_{k*}\backslash\{m^{*}\}\);
12:endif
13:until\(C_{k}\) is empty set, \(\forall k\in\mathcal{K}\)
14:if\(\Omega\
eq\varnothing\)then
15:repeat
16: Find \((k^{\prime},m^{\prime})=\operatorname*{argmax}_{(k,m):k\in\mathcal{K},m\in \Omega}R_{k,m}^{CD}\Big{(}p_{k,m}^{D^{*}}\Big{)}\);
17:if\(R_{k^{\prime},m^{\prime}}^{D}\Big{(}p_{k^{\prime},m^{\prime}}^{D}\Big{)}+R_{k^ {\prime},m^{\prime}}^{D}\Big{(}p_{k^{\prime},m^{\prime}}^{D}\Big{)}-R_{m^{ \prime}}^{C}>0\)then
18: Set \(\pi_{k^{\prime},m^{\prime}}=1\);
19:\(\mathcal{M}_{k^{\prime}}=\mathcal{M}_{k^{\prime}}\cup\{m^{\prime}\}\);
20:else
21:\(\Omega=\Omega\backslash\{m^{\prime}\}\);
22:endif
23:until\(\Omega\) is empty set
24:endif
25:return\(\mathcal{M}_{k},\forall k\in\mathcal{K}\)
```  
**Algorithm 1**Greedy Algorithm for Subcarrier Assignment.
## 3 Data and Methodology
### Urban LULC Modeling
#### 3.4.3 Model Validation  
The validation is a process to assess the quality of the simulated LULC results by comparing with the reference maps from the classified images in the years of 1998, 2003, 2008, and 2011. This study used the cross tabulation based accuracy assessment to calculate an overall accuracy of each predicted map [48]. Moreover, the Kappa analysis was also used to examine the quality of results [49].
## 2 System and Channel Model  
Figure 1 shows a schematic of the multi-hop parallel hybrid FSO/RF cooperative communication system. On each branch of the relay-aided hybrid system, the signal is separated into two identical signals after subcarrier modulation, and the signals are transmitted through the FSO and RF transmitter, respectively. According to the principle of maximum SNR, any regenerative repeater will select the maximum SNR signal from the two received signals for demodulation, regeneration, and forwarding.  
For the relay-aided hybrid system with an \((N,M)\) structure, the system has \(N\) cooperation paths and one direct path. For any cooperation path, there are M-hops from the source node to the destination node. Only one relay is allowed to process the received signals at one time based on the symbol-wise DF relaying method. Based on the best path selection scheme, a cooperative path is chosen to implement the transmission of the digital signal from the source node to the destination node.  
Figure 1: Schematic of the multi-hop parallel hybrid FSO/RF cooperative communication system.
# Integrated Fiber-FSO WDM Access System with Fiber Fault Protection
###### Abstract  
In this study, we propose a new wavelength-division-multiplexing passive optical network (WDM-PON) system to support the blended fiber-free space optics (FSO) signal access. To provide the fiber and FSO traffics simultaneously, the C- and L-band channels are applied in the presented PON, respectively. Moreover, to avoid the fiber breakpoint in the fiber access traffic, the proposed WDM access architecture also can provide the self-restored mechanism by applying simple fiber routing path. In addition, the corresponding signal performances of fiber and FSO channels are also executed experimentally for demonstration.  
wavelength-division-multiplexing (WDM); fault protection; passive optical network (PON); free space optics (FSO) +
Footnote †: journal: Electronics
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
### Current Challenges in Malware Detection
#### 1.1.4 Scalability and Performance Requirements  
Modern cybersecurity environments demand real-time detection capabilities capable of processing thousands of samples simultaneously. Traditional machine learning approaches often exhibit limited scalability when confronted with large-scale datasets, leading to increased processing times and reduced effectiveness. The trade-off between detection accuracy and computational efficiency remains a critical challenge, as organizations require both high precision and rapid response times to effectively counter emerging threats.
## 2 Materials and Methods
### System Description
#### 2.3.2 Server Socket
To manage and keep track of all the clients that have successfully established a connection, the server stores the connection information in a list called \"connected_clients\". This list, depicted in Figure 2 as the variable 'conn', serves as a repository to maintain the connections and facilitates the management of multiple simultaneous clients. Once the desired number of end nodes is attained, as determined by the number of connected_clients, the server initiates the mimic mechanism by simultaneously dispatching a'start' message to all connected clients. This operation can be achieved by maintaining a list of connected clients on the server-side and systematically transmitting the'start' message to each client individually in a sequential manner. This initial synchronization of schedule messages enables the clients to commence the concurrent collection of acceleration data. As the clients transmit their respective data streams, the server, employing separate threads, diligently receives and meticulously stores the received data in their corresponding CSV files, with each client's data being stored concurrently. Figure 2: Interaction diagram of the proposed server-client based communication with mimic broadcasting interaction: illustrating the mimic mechanism and tasks between the server and clients.
## 2 Materials and Methods
### Data
#### 2.2.2 Auxiliary Datasets  
For the attribution analysis of the spatiotemporal GPP variation, there are two kinds of datasets: the meteorological datasets and human intervention indices.  
The meteorological datasets used in this study came from the China Meteorological Forcing Datasets [42], which can be acquired from the Cold and Arid Regions Science Data Center [43]. Specifically, the air temperature dataset was produced by merging observations from 740 meteorological stations of China Meteorological Administration (CMA) into the corresponding Global Land Data Assimilation System version 1 (GLDAS-1) dataset [44]. The precipitation dataset was produced by combining the observations from the 740 CMA stations, the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation products [45], and the GLDAS-1 rainfall rate data. The downward solar radiation was constructed by correcting the Global Energy and Water Cycle Experiment-Surface Radiation Budget shortwave radiation dataset [46] with radiation estimates from meteorological station observations.  
We downloaded reanalyzed meteorological data (including daily average temperature, daily precipitation rate, and daily solar radiation) with monthly temporal resolution and spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). First, daily average temperature and daily radiation data are averaged within the growing season of each year, and the daily precipitation rate were translated into accumulated precipitation within the growing season of each year to obtain yearly accumulated precipitation from 1982 to 2015. In the next step, the meteorological datasets were downscaled to 0.05\({}^{\circ}\) to match the spatial resolution of GPP datasets for correlation analysis. Finally, time series of yearly average temperature, yearly accumulatedprecipitation, and yearly average radiation within the growing season from 1982 to 2015 were acquired for further analysis.  
Human intervention indices are also considered in this study since crops are heavily influenced by human activities, so the assessment of the impacts of human intervention on crops is necessary. This study considered three indices, which can quantify the impacts of human interventions, i.e., Cropland Area (CA), Crop Yield per Area (CYA), and Irrigated Area (IA). All these indices were provided by the National Agriculture Database (Statistics Bureau of China) [47]. The detailed impacts of human intervention on GPP are shown in Section 4.1 as an additional analysis.
## 2 Materials and Methods
### Center of Pressure (COP) Measurement  
The center of pressure (COP) is defined as \"the position of the applied force vector that is influenced by the shear forces produced by body segment acceleration\" [24]. The COP is often adapted to measure postural stability because it reflects the orientations of the body segments and the movements of thebody (including joint angle velocities and accelerations) employed to keep the center-of-gravity over the base-of-support [18]. The COP has a bivariate distribution; it is jointly defined by ML (mediolateral, x-axis) and AP (anteroposterior, y-axis) coordinates, which can be measured with the AMTI force platform and AMTI amplifier devices: AMTI OR6-7-2000 [19]. The mean COP is defined by the arithmetic means of the AP\({}_{0}\) and ML\({}_{0}\).  
\[\begin{array}{c}\text{where }\overline{\text{AP}}=\frac{1}{N}\sum_{n=1}^{N} \text{AP}_{0}[\text{n}],\ \overline{\text{ML}}=\frac{1}{N}\sum_{n=1}^{N}\text{ML}_{0}[\text{n}],\ \text{n}=1,\ \dots,N \end{array}\]  
Measurements of the area ellipse and mean velocity of sway for static posture show that those who have poor balance have larger COP displacements than those who have better balance [25].  
[PERSON] et al. proposed several stabilogram metrics for estimating postural steadiness and the COP stabilogram, including the mean resultant distance (MDIST), total excursion (TOTEX), RMS distance (RDIST), mean displacement velocity (MVELO), sway area (AREA_SW), and 95% confidence cycle area (AREA_CC) [18], expressed as follows. A variety of metrics are used to characterize COP stabilograms. There are two related stabilogram metrics, AREA_CC and AREA_SW, which are used to estimate the area of a stabilogram. The computations of these stabilogram metrics are given below [26; 27]:  
\[\begin{array}{c}\text{AREA\_CC}\ (95\ \%\ \text{CC AREA})=\pi\times(\text{MDIST}+Z_{0.95}\times S_{\text{ RD}})^{2}\\ \text{where }S_{\text{RD}}=\sqrt{\text{RDIST}^{2}-\text{MDIST}^{2}}\\ \text{RDIST}=\sqrt{\frac{1}{N}\sum_{n=1}^{N}RD^{2}(n)}\\ \text{AREA\_SW}=\frac{1}{2\pi}\sum_{n=1}^{N-1}\bigl{|}y(n+1)x(n)-y(n)x(n+1) \bigr{|}\end{array}\]
## 2 pH Control Equipment and Control Model  
### Equipment System Components  
The structure of the water and fertilizer integration control equipment is shown in Figure 1. When mixing fertilizer, the main control module with a computer as the core controls the opening and closing time of the solenoid valves at the outlets of the fertilizer tank, irrigation water source and acid tank, thus regulating the proportion of irrigation water and fertilizer in the mixing tank. A stirring pump is used to mix the water and fertilizer in the tank. The fertilizer tank is judged by a pH sensor to determine whether the pH value of the liquid inside meets the pH value for crop growth, and if not, the solenoid valve of the acid tank is opened to replenish the tank with acid. Fertilizer application is carried out mainly through the computer-based main control module, which controls the fertilizer pump and solenoid valve.  
Figure 1: Water and fertilizer integration and control equipment structure diagram: 1—check valve, 2—flow meter; 3—computer control; 4—water source; 5—solenoid valve; 6—acid tanks; 7—hose pump; 8—mixing tank; 9—level meter; 10—pH sensor; 11—monitoring and adjustment of the upper side; 12—pressure gauge; 13—irrigation and fertilization pump; 14—holding valve; 15—drip irrigation belt; 16—filter; 17—stirring pump; 18—fertil tank.
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 4 Network Design  
Based on the analysis of NTN-mMTC, DCS equipment, and the hybrid constellation, a space-ground integrated network design is proposed in this chapter. The traditional 5G networks consist of an access network, a bearer network, and a core network. An improved network structure combining NTN and B5G needs to be proposed. A B5G-based NTN can be divided into various application scenarios.  
As shown in Figure 3, there are four different application scenarios. In the access network, there are direct access mode and indirect access mode. In the former mode, signals are directly transmitted from ground sensors to satellites without any relay equipment. In the later mode, signal is collected by gateway and relayed to satellites. Such a collection gateway can be realized by a terrestrial base station or a high-altitude platform station.  
In bearer network, there are transparent forwarding model, regenerating forwarding mode and onboard forwarding mode. In first mode, the radio frequency (RF) signal is filtered and amplified in satellites. This method is simple and energy-efficient. In secondary mode, the signal will be decoded, error corrected, and re-encoded in satellite. This method is complex and expensive but can greatly increase the signal-noise-radio (SNR). This mode is more suitable for the multi-hop relay scenario. In the last mode, more advanced algorithms are introduced in the signal process. This is the most complex and smartest signal process.  
In the core network, NTN can be classified as network elements. User plan function (UPF) is the most relevant to data-plane functions among all core network elements. UPF  
Figure 3: Four different sceneries of network design.  
should be responsible for data transfer and QoS guarantees. It can be allocated via satellite or ground station gateway.  
For compatibility with the B5G terrestrial network, network slicing (NS) and network function virtualization (NFV) are utilized in different sceneries. The QoS assurance, routing algorithm, and interface control strategy are supported by the UPF block in Figure 4. The N9 interface and UPF element can be allocated on satellites or ground station gateways.  
In order to increase system efficiency, the distribution of UPF should be dynamically adjusted. In a unified hardware platform of DCS equipment, different functions should be realized according to the requirements. Software defined radio (SDR) architecture is able to meet these requirements.
## 4 Materials and Methods
### Tumor Injection, Drug Administration, and Urine Collection  
All procedures conducted were approved by the Indiana University Animal Care and Use Committee and complied with the Guiding Principles in the Care and Use of Animals, supported by the American Physiological Society (APS). Twenty female BALB/c mice (6 weeks old) were acquired from Harlan Laboratories (Indianapolis, IN, USA) and injected in the iliac artery with 4T1.2 mammary tumor cells obtained from Dr. [PERSON] at the Peter MacCallum Cancer Institute (Melbourne, VIC, Australia). Pitavastatin (Livalo) was administered each day via intraperitoneal injection at high (8 mg/kg body weight) and low (4 mg/kg body weight) doses to subsets of mice after tumor injection. All tumor-bearing mice were sacrificed on day 21 after tumor injection, as this timeline allowed for sufficient tumor progression and treatment effect. Physical changes due to tumor progression and treatment effect were evaluated by measuring tumor-induced bone damage by micro-CT, X-ray analysis, mechanical testing, and histological imaging [29]. Prior to tumor injection, urine from all 20 mice was obtained to serve as the control group. Following tumor injection, urine was collected from both treated (at low and high doses) and untreated tumor-bearing mice for three weeks. Mice were caged at room temperature and fed the same diet (mouse chow ad libitum) during the experiment. Urine was collected over dry ice using Pasteur pipettes into glass centrifuge tubes. Furthermore, 50 \(\upmu\)L aliquots of urine were transferred to a 10 mL headspace vial and stored in a \(-\)80 \({}^{\circ}\)C freezer before SPME GC-MS QTOF analysis was implemented.
## 3 Results and Discussion
### Integration of Renewable Energy Sources and Hybrid Microgrid Configurations
#### 3.2.1 Challenges of Renewable Integration and Variability Mitigation  
The integration of RES such as solar photovoltaic and wind power into microgrids introduces considerable variability, posing challenges to system stability and reliability [12; 15; 79]. These issues become more pronounced in configurations with high renewable penetration, where generation fluctuations must be rapidly compensated to maintain power quality [80; 81]. Several studies emphasize the importance of advanced forecasting models and energy management systems to mitigate the effects of intermittency [24; 26]. Moreover, the absence of standardized methodologies for modeling and predicting the dynamic behavior of RES within microgrids complicates the design of effective control strategies [25; 51; 82].  
Hybrid solutions that combine RES with advanced energy storage systems and DR programs have been proposed to smooth out generation fluctuations and ensure continuous energy supply [41; 52; 53]. However, the economic viability of these solutions remains a significant barrier, particularly in remote or islanded microgrid environments where implementation costs are higher and financial resources are limited [21; 54; 57].
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 4 The Role of lncRNAs beyond Metastasis
### Drug Resistance  
LncRNAs have emerged as critical players in the development of drug resistance in BC, significantly impacting the efficacy of chemotherapy, hormone therapy, and targeted therapies. For instance, elevated levels of HOTAIR increase ER protein levels and chromatin occupancy, thus potentiating ER downstream gene regulation and promoting tamoxifen-resistant cell growth [26]. In addition, silencing HOTAIR in doxorubicin-resistant BC cells reduces cell proliferation and increases apoptosis by inhibiting the PI3K/AKT/mTOR pathway, suggesting that targeting HOTAIR could be an effective strategy to overcome drug resistance in BC [27]. Furthermore, one of the key mechanisms of chemoresistance associated with MALAT1 is doxorubicin resistance, which mediates by targeting miR-570-3p [28]. The high expression of lncRNA H19 is linked to paclitaxel resistance in ER\(\alpha\)-positive BC cells by inhibiting pro-apoptotic genes BIK and NOXA, suggesting that the ER\(\alpha\)-H19-BIK signaling axis is crucial in promoting chemoresistance [29]. Another study focused on identifying biomarkers to predict the response to neoadjuvant chemotherapy in patients with locally advanced BC within luminal B-like phenotype. GATA3-AS1 was the only lncRNA that was overexpressed in all nonresponder patients. This study revealed GATA3-AS1 as the first potential molecular biomarker with a use in clinical practice in the prediction of neoadjuvant chemotherapy treatment response in BC [30].