esa-sceva/satcom-chunk-collection huggingface dataset:
# Uniaxial Testing of Soil-Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
## 2 The Current State of Knowledge and Basic Data on the Research Project
### Methods of Preparing Samples for Laboratory Tests  
The preparation of standard samples from test mixes in laboratory conditions consists of mechanical mixing of the soil collected in successive layers of the substrate with a hydraulic binder and mixing water (prepared in the lab using a standard concrete mixer). In the present case, the depth was divided into two ranges (up to 2 m and in the 2-6 m layer), differing essentially in the content of organic parts, which significantly affects the quality of the obtained composite. Exemplary samples taken from the \"weaker\" layer are shown in Figure 3.  
The samples have a similar shape (cubes 15 \(\times\) 15 \(\times\) 15 cm\({}^{3}\)) and surface roughness; however, they were produced using different variants of the mixing technology. Figure 3 on the left shows three samples of composite formed from the original ground and injected cement slurry. The samples shown in Figure 3 on the right were mixed with an additional assist with air stream to obtain better homogenization.  
The choice of the method of collecting soil-cement samples from the formed columns is important for the representativeness of the research. In field conditions, during the execution of contracts, the collection of material for testing is usually carried out from the ground level by collecting the so-called \"spoils\", i.e., the excess excavated material pushed out during the injection of the substrate, and mixing the cement slurry with the soil. Proper wet sampling can also be collected at depth using a special sampler. Wet sampling in fresh columns and coring of cured DSM columns are commonly used to verify strength and permeability.  
Initial considerations aimed at maximizing the objectivity of the obtained results indicated the need to collect cylindrical samples of appropriate slenderness (H/D = 2) obtained from the entire profile of the column using the \"pipe in pipe\" method, i.e., by embedding a PVC pipe in the fresh material of the column (after cycles of injection and mixing but before the soil-cement composite hardens). An example of such a continuous core in a PVC pipe is shown in Figure 4.  
Optionally, the possibility of a full-cored borehole of DSM columns was allowed after the composite obtained the required strength, allowing for continuous cores (28 days). Example cores are shown in Figures 5 and 6. It is worth noting that where thorough mixing with the binder has not taken place, there is no possibility of taking the core. Cores from well-mixed columns are also fractured, but these fractures are due to dynamic influences during drilling. With a strength below 1 MPa, even a minimal movement of the tool can cause the core to break (Figure 5a). It is worth mentioning that the entire columns could be extracted from the ground without losing their integrity (Figure 5b).  
Figure 3: Various samples prepared in real-world conditions (building site).
# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
###### Abstract  
Alzheimer's disease (AD) is a progressive neurodegenerative condition. It causes cognitive impairment and memory loss in individuals. Healthcare professionals face challenges in detecting AD in its initial stages. In this study, the author proposed a novel integrated approach, combining LeViT, EfficientNet B7, and Darto booster XGBoost (DXB) models to detect AD using magnetic resonance imaging (MRI). The proposed model leverages the strength of improved LeViT and EfficientNet B7 models in extracting high-level features capturing complex patterns associated with AD. A feature fusion technique was employed to select crucial features. The author fine-tuned the DXB using the Bayesian optimization hyperband (BOHB) algorithm to predict AD using the extracted features. Two public datasets were used in this study. The proposed model was trained using the Open Access Series of Imaging Studies (OASIS) Alzheimer's dataset containing 86,390 MRI images. The Alzheimer's dataset was used to evaluate the generalization capability of the proposed model. The proposed model obtained an average generalization accuracy of 99.8% with limited computational power. The findings highlighted the exceptional performance of the proposed model in predicting the multiple types of AD. The recommended integrated feature extraction approach has supported the proposed model to outperform the state-of-the-art AD detection models. The proposed model can assist healthcare professionals in offering customized treatment for individuals with AD. The effectiveness of the proposed model can be improved by generalizing it to diverse datasets.  
feature extraction; deep learning; transformer; LeViT; hyperparameter tuning; model optimization; neuroimaging; neurodegenerative diseases
# Down the Rabbit Hole: Fostering Active Learning through Guided Exploration of a SCADA Cyber Range
## 3 Development of the Cyber Range Environment
### SCADA ICS Automation Process  
The SCADA ICS testbed scenario used for the cyber range environment scenario is based on a solar-based heating system that uses thermal oil for heat transport and exchange  
Figure 1: CANVAS laboratory environment.  
between a set of panels and a network of heating coils. The thermal fluid is heated on heat pipes contained within the solar collector panels and, once it reaches an initial setpoint (40 degrees Celsius), an electric pump is started at a fixed speed, making the fluid circulate in a closed circuit that comprises an auxiliary tank and two heaters (continuing the heat exchange coils). A second setpoint (80 degrees celsius) activates a second speed preset for the pump, which increases the speed of the thermal fluid flow. Figure 2 depicts the Human-Machine Interface (HMI) for this specific process.  
The SCADA system deployed for this cyber range monitors the operation of the whole process, obtaining information from the temperature sensors and electricity consumption probes, but also controlling the emulated valves and a real 3-phase motor for the pump. This cyber-physical process is emulated using a mix of real equipment and virtualized component instances, with the physical setup being shown in Figure 3.  
Figure 4 depicts the logical testbed topology. The fluid pump is simulated by an electric 3-phase motor driven by a Variable Frequency Drive (VFD), allowing multiple speeds. A Modicon M340 Programmable Logic Controller (PLC) controls the VFD, via  
Figure 3: Automation testbed.  
Figure 2: HMI for the SCADA process.  
its discrete I/O channels. The motor speed is controlled by the PLC, based on a set of predefined liquid temperature setpoints.  
Temperature measurements are provided by a Modbus Remote Terminal Unit (RTU) device providing a temperature gauge--this device provides the RTU functionality as well as the temperature probe emulation, whose value is controlled by a potentiometer. The Modbus RTU is based on an Arduino microcontroller connected to a wired ethernet network shield equipped with a Wiznet W5100 ASIC [20].  
The PLC establishes an horizontal communication pattern with the RTU, providing insights of how this type of communications may play a crucial role on the ICS. The PLC also communicates with the Human-Machine Interface (HMI), which provides the supervisory interface for the system. Communications between the RTU, the PLC and the HMI are supported by means of the Modbus/TCP protocol [21].
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### Summary  
Table 8 summarize the characteristics of the above-discussed networking systems in terms of network capacity, coverage size, reliability, service cost-effectiveness, infrastructure safety and feasibility for underwater coverage. Service cost-effectiveness is mainly determined by the costs of network construction and operation. Maritime radio systems are designed for maritime transport andeven difficult to satisfy their own ever-increasing data communication demands due to the limited bandwidth. The service provided by satellites are not cost-effective to the ordinary users, and such situation may last until technological breakthrough is made. Although WANETs can be set up at a very low cost, their performance reliability is an issue. Thus far, mobile systems only provide occasional services without systematical deployment in marine environments. Cabled underwater observatory systems are designed for specific missions, and their infrastructures are not open to the public. For UANs, their transmission rates also fail in supporting many Internet applications for large coverage. The HAP is a promising technology for cost-effective communication in oceans, while more investigation is still needed for practical applications.
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 2 Theoretical Background
### Thermal Sensors  
FIR technique and WGM displacement are techniques that can be used to develop thermal sensors. In order to characterize the performance of a thermal sensor, two parameters need to be introduced: relative sensitivity and resolution. The relative sensitivity, \(S\), represents the variation of the measured parameter, \(MP\), with temperature relative to its magnitude:  
\[S=\frac{1}{MP}\frac{dMP}{dT} \tag{4}\]  
According to this definition, it is straightforward to calculate the sensitivity for the aforementioned methods using Equations (1) and (2), through the following expressions:  
\[S_{FIR}=\frac{1}{R}\frac{dR}{dT}=\frac{E_{32}}{K_{B}T^{2}} \tag{5}\]  
\[S_{WGM}=\frac{1}{\lambda}\frac{d\lambda}{dT}=\left(\frac{1}{n}\frac{\delta n} {\delta T}+\frac{1}{R}\frac{\delta R}{\delta T}\right)=\alpha+\beta \tag{6}\]  
Equation (5) must be carefully analyzed because it can lead to misinterpretations. It suggests that using pairs of energy levels with larger energy differences increases the FIR sensitivity. However, for large energy differences, the energy levels have no temperature correlation, and this technique is not valid as explained before. It also suggests that the sensitivity is enhanced at low temperatures, but this implies extremely low upper-level populations, corresponding to excessively low emission intensities, so the ratio of intensities is determined with undesirable uncertainty.  
In addition, the temperature resolution of the FIR technique and WGM can be estimated taking into account the resolution of the instrument and the temperature sensitivity of the technique using the following expressions [19]:  
\[\Delta T_{\text{min}FIR}=\frac{\Delta R}{R~{}S_{FIR}} \tag{7}\]  
\[\Delta T_{\text{min}WGM}=\frac{\Delta\lambda_{min}}{\lambda S_{WGM}} \tag{8}\]
## 3 Proposed Power Losses Model  
### Proposed Conduction Losses Model  
#### 3.1.1 The Conduction Losses Based on Phase-Current Period  
Tables 1 and 2 show the conduction losses using the traditional model and the calculation of the traditional losses model is based on the switching period, which means that the losses need to be calculated in every PWM period. Thus, the traditional losses model brings higher computational demands. In order to solve this problem, this proposed method considers the phase-current period  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Currents** & **T\({}_{1}\)** & **T\({}_{2}\)** & **T\({}_  
as a unit to solve the average losses. One current period consists of many switching periods; thus, the computational time of the proposed model is reduced compared with the traditional model. Supposing that there are N switching periods in one phase-current period, the conduction losses can be expressed as:  
\[P_{igbt\_cur\_cond}(t)=\frac{1}{NT_{sw}}{\sum}_{n=1}^{N}\int_{(n-1)T_{sw}}^{(n -1+D_{n})T_{sw}}(V_{cav}+R_{ce}i(t))i(t)dt \tag{6}\]  
where \(T_{sw}\) and \(D_{n}\) represent the switching period and the duty cycle in the \(n_{th}\) switching period, respectively. \(V_{cav}\) and \(R_{ce}\) are the IGBT temperature-dependent parameters and the specific relationship with the temperature will introduced in Equation (27).  
If the current ripple is ignored, the current can be considered as constant during the IGBT conduction process in one switching cycle. Then the conduction losses can be simplified as:  
\[P_{igbt\_cur\_cond}(t)=\frac{1}{NT_{sw}}{\sum}_{n=1}^{N}\left(V_{cav}+R_{ce}i( nT_{sw}))i(nT_{sw})D_{n}T_{sw}\right. \tag{7}\]  
where \(i(nT_{sw})\) represents the phase-current in the \(n_{th}\) switching period.  
Considering that the phase-current period is much larger than the switching cycle, N can be regarded as infinite. Taking \(T_{sw}\) as the time derivative, Equation (7) can be simplified as:  
\[P_{igbt\_cur\_cond}(t)=\frac{1}{T_{c}}\int_{o}^{T_{c}}(V_{cav}+R_{ce}i(t))i(t)D (t)dt \tag{8}\]  
where \(T_{c}\) indicates the phase-current period and \(D(t)\) is the duty cycle in one phase-current period.  
As shown in Equation (8), \(D(t)\) need to be solved and is a key parameter for the conduction loss calculation. However, the duty cycle is a changing parameter and related to the modulation mode. In the following section, the solution of duty cycle will be studied in detail.
## 2 Phased Array Radar (PAR)
### Rocket Tracking System (RTS)
#### 2.1.1 Features, Requirements and Functions
Generally, due to accuracy requirements to measure the target position, the beamwidth of tracking radars is often very narrow (for example, the order of 1.0 ~2.5 \({}^{\circ}\)[3, 28, 34]), while in surveillance radars, which seek to sweep a large area at one time, beams tend to be large (for example, range from 15 \({}^{\circ}\)-90 \({}^{\circ}\)[34, 35]). Despite these differences, it is possible to obtain a multifunctional system with hybrid radar sensors to perform simultaneously tasks of tracking and surveillance [29]. To do so, taking into account the design and implementation complexity, it is possible to use PARs, which are more flexible. Hence, these multifunctional systems can be obtained from tracking systems, by making the necessary changes in the scanning process of radar sensors to also perform surveillance tasks, such as air defense and missile warning. In Figure 2, an illustration is shown to compare the differences in beamwidth and radar-target distance between a tracking radar and a surveillance radar.
## 4 Theoretical Analysis of Rigid Rotor Vibrations Amplitude in Rolling Bearings
### Impact of Load and Contact Deformations on Level of \(pp\)-Amplitude  
Due to the load and the occurrence of contact deformations, additional displacement and approach of the inner ring of the bearing relative to the external will occur and the new rolling elements will come into contact with the bearing rings. The support of bearing will continue to happen between the two boundaries positions: on the even (BSE) and the odd (BSO) number of the rolling elements. Only, depending on the size of contact deformations, the support system will move from _the support system 1-2_ to _the support system 2-3_, 3-4, 4-5_, etc., as shown in Figure 3[19; 21].  
The number of rolling elements that will come into contact with the bearing rings is directly dependent on two sizes, the inner radial clearance, and the external radial load, and can be easily determined by the procedure given in the literature [21; 27].  
The additional displacement of bearing rings will cause the further change of the \(pp\)-amplitude of rotor vibrations.  
The total displacements of the rotor center will be equal to the sum of the displacements for unloaded bearing (Equations (5) and (6)) plus displacement due to the contact deformations of bearing elements (Figure 4). Hence, for the boundary positions of the supports on an even and an odd number of the rolling elements, the total displacement of the rotor center will be equal:  
\[y_{e}=\frac{e}{2\cdot\cos\gamma/2}+w_{e}, \tag{8}\]  
\[y_{o}=\frac{e}{2}+w_{o}, \tag{9}\]  
where \(w_{e}\) and \(w_{o}\) are rotor center displacements due to the contact deformations (bearing deflection) in the BSE and BSO cases of support system.  
Figure 3: Support systems of rolling bearing.  
According to the Equation (4), the amplitude of VC vibrations of the loaded rolling bearing can be calculated as a difference of the total center displacement for the boundary cases of the support, according to:  
\[\Delta=y_{e}-y_{o}=\left(\frac{e}{2\cos(\gamma/2)}+w_{e}\right)-(e/2+w_{o}), \tag{10}\]  
\[\Delta=y_{e}-y_{o}=\frac{e}{2}\bigg{(}\frac{1}{\cos(\gamma/2)}-1\bigg{)}-(w_{o }-w_{e})=\Delta_{1}-\Delta_{2}, \tag{11}\]  
where \(\Delta_{2}\)= \(w_{o}\)-\(w_{e}\) - \(pp\)- amplitude of the rotor vibrations due to the contact deformations of bearing elements. This amplitude is equal to a difference of the bearing deflection in two boundary support position: BSO (\(w_{o}\)) and BSE (\(w_{e}\)).
## 2 Study Area and Data
### Conditioning Factors
#### 2.2.3 Topographical Factors  
Within the scope of this study, topographic factors that indirectly or directly affect the formation of sinkholes were determined as follows: proximity to drainage (PDR), slope (SLP), aspect (ASP), curvature (CRV), plan (PLC) and profile curvature (PRC), elevation (ELV), stream power index (SPI), and topographic wetness index (TWI). Proximity to drainage, slope, plan, profile curvature, and elevation are considered as an influencing factor in sinkhole formation due to their relationship with the flow rate and direction of surface and groundwater and the rate of surface water that is not absorbed completely by the soil on the relevant area [53]. While the topographic wetness index refers to hollows where water can generally accumulate and the soil may become more saturated, the stream power index is a measure of the erosive power of flowing water [54,55]. Aspect is a directly effective factor in the soil moisture and evaporation process and indirectly reflects the water accumulation capacity of the slopes, as well as affecting the rainfall, wind, and vegetation of the relevant area [56,57,58]. The digital elevation model (DEM) of the study area was produced from contour lines of 1/25,000 scale standard topographic maps, and the data for other topographic parameters given above were derived from these data (Figure 4).  
Figure 4: Topographical factors: (**a**) ELV; (**b**) SLP; (**c**) ASP; (**d**) CRV; (**e**) PLC; (**f**) PRC; (**g**) PDR; (**h**) SPI; (**i**) TWI.
## 3 Results and Discussion
### IPS Parameters Effect  
#### 3.2.1 Heating Range and Powers  
Case 8 entailed the activation of only sheet A with a considerable increase in power of 150 W to simulate Type (2) previously defined. Results (Figure 14) showed that this design could not prevent ice formation even near the stagnation point. The high power applied to sheet A led to a leading edge surface temperature approximately equal to that of Type (1) in case 1, as shown in Figure 15, but with a temperature fall rapidly after the heating area. It could be inferred that the temperature of the superhydrophobic surface here should have fallen below the freezing point. As a result, it is not advisable to reduce the heating area range since this could adversely impact efficient heat distribution in addition to the superhydrophobic coating's poor heat transfer. The surface temperature on the superhydrophobic surface must be kept above the freezing point.  
Figure 14: Leading edge ice formation when only sheet A was turned on.  
Figure 13: Comparison of surface temperature with different environment temperatures and heating power densities.
## 3 Design of the Wirelessly Activated Node
### Sensing, Communication, and Control  
When the RTC connects the load switch at the input of the LTC3106, the node gets fully powered and starts its normal operation. At wake up, the SMT32L476 configures the SX1280 transceiver in sleep mode to minimize its power consumption while idle (1 uA typical current consumption). It then configures the IIS2 DLPC inertial measurement unit (IMU) for temperature and acceleration data measurement through the I2C interface. The IMU features a 32-level first in first out (FIFO) buffer and a programmable output pin that awakes the MCU to fetch data in bursts when the FIFO is nearly full. MCU's current consumption is minimized by appropriate usage of direct memory access (DMA) transfers, careful selection of the core operating frequency (reduced to 2 MHz), and use of sleep modes while waiting for the next interrupt from the IMU. The MCU also performs the statistical analysis on the signal during acquisition.  
When data is collected, the MCU wakes up the transceiver and sends a 20-byte payload containing the 2-byte node ID, minimum, maximum, and average acceleration on the three axis, and temperature data. The gateway replies with a 3-byte acknowledgment (2-byte node ID and 1-byte acknowledgment).  
The MCU then programs the RTC for the subsequent wakeup through the I2C interface, and the RTC itself sends the node to sleep mode till the next wake up.
## 2 Force Modeling for Machining Stability Analysis
### Data Preparation and Feature Engineering  
To determine the optimal feed rate that minimizes machining instability, this study employs XGBoost regression, a powerful machine learning algorithm designed for handling nonlinear relationships. The model predicts the optimal feed rate \(v\) based on specific machining conditions, enabling efficient and automated feed rate selection.  
Following frequency analysis, only data corresponding to stable machining conditions--where chatter vibrations are minimized--are selected for model training. This ensures that the regression model is trained exclusively on machining conditions that yield optimal feed rates for chatter suppression.  
The dataset used for training is derived from theoretical cutting force model simulations, systematically varying machining parameters across a range of conditions. The dataset consists of a total of 232 data points, each containing key machining parameters and their corresponding optimal feed rate values. The input features include axial depth of cut (\(ap\)) and radial depth of cut (\(b\)).  
To enhance the model's ability to capture nonlinear relationships between machining parameters and feed rate, feature engineering is performed: Quadratic terms (\(ap^{2},\ b^{2}\)) are introduced to account for second-order interactions that influence machining stability and chatter behavior.  
To assess the influence of each input feature on prediction performance, we employed gain-based feature importance from the trained XGBoost regression model. In gradient boosting trees, the gain is defined as the reduction in the loss function achieved by splitting a node on a particular feature. The gain is computed as:  
\[Gain=\frac{1}{2}\Bigg{[}\frac{G_{L}^{2}}{H_{L}+\lambda}+\frac{G_{R}^{2}}{H_{R} +\lambda}-\frac{\left(G_{L}+G_{R}\right)^{2}}{H_{L}+H_{R}+\lambda}\Bigg{]}-\gamma \tag{13}\]  
where \(G\) and \(H\) represent the accumulated first- and second-order gradients of the loss function at the left (L) and right (R) branches, respectively, and \(\lambda\), \(\gamma\) are regularization terms. The cumulative gain over all splits involving a given feature provides a supervised and interpretable importance score that reflects the feature's contribution to the model's predictive performance.  
As shown in Figure 7, the squared radial depth term (\(b^{2}\)) contributes the most to the model, indicating a strong nonlinear relationship between radial engagement and chatter response. This is followed by the original axial depth (\(ap\)), radial depth (\(b\)), and finally the squared axial depth (\(ap^{2}\)). These results validate the inclusion of both linear and nonlinear terms in the feature set and demonstrate the XGBoost model's ability to leverage these patterns for improved prediction accuracy.  
Once feature engineering is complete, the dataset is split into training (80%) and test (20%) subsets, ensuring the model's ability to generalize across different machining conditions. This structured dataset enables the XGBoost regression model to accurately predict optimal feed rates under varying cutting conditions.