esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Test Analysis of Groundwater Level Fluctuations on
## 4 Analysis of Model Test Results
### Analysis of the Collapse Development Process  
The surface water, pore water, and groundwater in the lower part of the soil box are closely linked to form a complete hydrological cycle in the test model installation. The infiltration of surface water can change the water content of the cover soil. When rainfall or other recharge infiltrates, the water content of the cover soil increases, the cohesion and angle of internal friction decrease, the capacity and self-gravity increase, and the shear strength decreases, which ultimately leads to a reduction in collapse resistance. The effect of groundwater on karst ground collapse can be analyzed in many ways, but the magnitude and frequency of groundwater fluctuations are the most important. Based on the development of the soil cavity under the two test scenarios the whole process can be roughly divided into a softening phase of the soil above the cavity, a soil loss-spalling phase and a rapid-integral collapse phase (Figure 12).  
Figure 11: Settlement curves for each cover layer as the water level drops.  
Figure 10: Declining water table cover settlement.  
1. Soil softening stage: At the beginning of the test, rainfall and lateral seepage recharge the groundwater and a gradient difference is formed between pore water and groundwater, resulting in vertical seepage. As the support for the cover soil above the soil hole is weak, it is susceptible to submerged erosion making the soil particles soften.
2. Soil loss exfoliation stage: With the increase of rainfall and lateral seepage recharge time, the saturation of the soil increases and the permeability becomes poor. When the water level rises, the gas inside the cavern cannot be discharged and is squeezed to form a high-pressure air mass, which produces tiny fissures in the cover soil. When the water level falls, the negative pressure inside the cavern increases the additional force of downward seepage of pore water in the cover soil, which intensifies the loss and spalling of the soil.
3. Rapid integral collapse phase: long periods of continuous recharge and fluctuations in the water table resulted in a large loss and spalling of the cover soil above the cavity in the later stages of the test, and the arch structure no longer existed. The cavity was observed earlier in the form of an altar shown in Figure 13, at which point the self-weight of the soil increased with recharge, while the thickness of the soil was still being lost and spalled with fluctuations in the water table. When the collapse develops to the point where the collapse causing force is greater than the collapse resisting force, the soil undergoes shear deformation falling off as a whole, eventually leading to the overall collapse of the surface, as shown in Figure 14.  
Figure 12: Schematic diagram of the development of the earth cave collapse.
## 3 Bioactive Compounds in Wine By-Products
### Extraction Methods of Bioactive Compounds
#### 3.1.1 Conventional Extraction Methods  
Conventional extraction techniques can be used to extract bioactive compounds from by-products of the wine industry. The effectiveness of the extraction process depends on commonly used traditional extraction techniques of solid-liquid extractions, such as pressing, Soxhlet extraction, maceration, decoction or infusion, and steam-dragging extraction/hydro-distillation [77,81,82,83,84]. These methodologies represent an essential part of many industrial processes and involve a series of analogous and successive phases between the plant particle containing the solute and the solvent. The process of extracting compounds from solid material involves several steps, including the introduction of a solvent into the matrix, the dissolution of the compounds, the movement of the dissolved substance from inside to outside the matrix through diffusion, the transfer of the compounds from the particle's external surface to the solvent via convection, and, finally, the separation of the extract from the solvent through evaporation, if the solvent has a lower boiling point than the extracted compound [84; 85; 86].  
Although these conventional techniques have been used to extract bioactive compounds from wine by-products [81; 82; 83; 77], they have some associated problems, such as high energy consumption for the heating process, which can also result in the degradation of thermolabile compounds [34], or the use of hazardous solvents [87].
# Vertical Measurement of Equivalent Black Carbon Concentration at Low Altitude
## 2 Materials and Methods  
### Measurement of eBC Concentration Using a Drone-Based BC Sensor at KOREATECH Campus  
Figure 1 shows the drone used in this study and the snapshot of a program for drone control. A micro-aethalometer (microAeth AE51, AethLabs) weighing approximately 0.3 kg was used for the eBC measurements. The wavelength of the light source is 880 nm. BC was collected and analyzed on a filter strip consisting of a T60 Teflon coated borosilicate glass fiber media housed in a protective casing. To measure the vertical profile of eBC concentration, AE51 and a battery pack were loaded onto the hexacopter (DJI F550), which weighed approximately 2.0 kg in total and could aviate for about 13-15 min depending on weather condition with fully charged batteries. The hexacopter used in this study was modified and built for our experiments. Testo 174H was used to measure the temperature. Testo 174H uses a thermocouple as a probe. The range of measurement is \(-\)20 \({}^{\circ}\)C to 70 \({}^{\circ}\)C. The resolution of temperature measurement is 0.1 \({}^{\circ}\)C.  
While, a hexacopter can cover an altitude range from the ground to 600 m agl, a FW-UAV aircraft can cover an altitude range from 150 m above the ground to around 2000 m above sea level. The blade diameter of our hexacopter was 0.22 m. The empty weight and the takeoff weight were 0.5 kg, and 2.0 kg, respectively. The maximum operation altitude was about 1000 m. The maximum horizontal and vertical velocities were both 14 m/s. A Graupner MZ-24 controller was used for stable aviation. The control of ascent, descent and hovering is quite straightforward. Naza-M Assistant Software (v2.40) controlled our drone to make an automatic vertical ascent, hovering for a while and descent to the ground. When hovering at a certain position, our drone discharged the thrust equivalent to the weight of the drone at the measurement altitude. Instantaneous changes in wind direction and speed made the position of our drone shift momentarily, but it became stable shortly in a new posture with a help of a compensation scheme by the controller.  
At the measurement altitude, our drone was hovering about 4-5 min to measure the eBC and the temperature, which were recorded every minute. The measurement was done in three ways depending on the battery status. First, the measurement height has been selected at random order. For example, the measurement was done at 10 m, 50 m, 20 m, 70 m and then 30 m, etc. This is defined as the random mode. Second, the measurement altitude increases from the ground to 130 m agl in sequence as the drone goes up and this is defined as the ascending mode. Third, the measurement altitude decreases from 130 m agl to the ground in sequence as the drone comes down, and this is defined as the descending mode.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 3 Implementation  
The NIST's PQC standardisation efforts are widely regarded as the most significant globally [31], progressing through a series of competitive evaluation rounds [24]. Submissions undergo rigorous peer review and are evaluated on their technical merits before the selected algorithms progress to be finalised as standards. These are the standards incorporated in this study.  
As indicated by the literature review, the field of post-quantum computing (PQC) is extensive. This paper focuses on a specific overlapping area defined by four key aspects:  
1. **Key Exchange:** A crucial component of secure communication whose traditional techniques were protected by large-number factorisation and that will be rendered vulnerable to [PERSON]'s algorithm in quantum computing. New quantum-resistant techniques are emerging.
2. **Energy Consumption:** A comparative evaluation of the energy used during key generation methods for both traditional and post-quantum cryptography.
3. **Computing Platform:** The Raspberry Pi 5 was selected as the test platform, ensuring the homogeneity and repeatability of the results. Its uniform and common integration into operational technology solutions and use by hobbyists make it a relevant choice.
4. **Software Library:** Standardised libraries were employed in the solutions, recognising the implementation complexity and verification challenges of cryptographic code. Following the established best practice in cryptography, this paper utilised the tested, peer-reviewed, and performant OpenSSL library rather than developing custom implementations.  
An illustration of these four constituent parts is presented in Figure 1. This paper focused on a practical comparison of the energy consumed during key generation for newly standardised PQC algorithms against traditional methods. Experimentation was conducted on a Raspberry Pi platform, employing standardised cryptographic libraries to ensure a consistent basis for comparison. The aim was to establish a baseline for this platform, to ascertain the impact of transitioning to PQC for this specific aspect of the cryptographic suite.
## 2 Materials and Methods
### Machine Learning  
Machine learning models were trained and tested in MATLAB using models available in the Classification Learner toolbox. Three different models were included: k-Nearest Neighbours, Bagged Decision Trees, and Neural Network. Default'medium' settings for the parameters, as defined in MATLAB (version r2023a), were used and are provided in Table 2.  
Two methods for class-balancing the training data were tested: the random undersampling (RUS) and the synthetic minority oversampling technique (SMOTE). RUS involves randomly removing data points from the majority class and SMOTE involves synthesising new data points of the minority class(es) using a weight combination of neighbouring data points. In each case, the classes were fully balanced, e.g., 50:50 porous:non-porous data points.  
The complete dataset consisted of three separate builds of 17 cylindrical rods as described in Section 2.1. A test dataset was withheld by selecting 3 out of 17 of the rods in each build, equating to approximately 18%. Only the uniform discretisation approach  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**k-Nearest Neighbours** & **Bagged Decision Trees** & **Neural Network** \\ \hline \hline \multirow{3}{*}{\begin{tabular}{c} k = 10 \\ Distance metric: Euclidian \\ Distance weight: equal \\ Standardise data: yes \\ \end{tabular} } & \
\begin{tabular}{c} Ensemble method: bag \\ Learner type: decision tree \\ Max number of splits: 2 \(\times\) 10\({}^{5}\) \\ Number of learners: 30 \\ Number of predictors: all \\ \end{tabular} &
\begin{tabular}{c} Fully connected layers: 1 \\ Layer size: 25 \\ Activation: ReLU \\ Iteration limit 1000 \\ Regularisation strength: 0 \\ Standardise data: yes \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for machine learning models.  
Figure 6: Uniform discretisation approach (UDA).  
was used for test data; since the defect-centric approach depends on prior knowledge of pore locations that could not be available in practice, it was considered unsuitable for test data and likely to overestimate model accuracy. The remainder of the rods were used as the training dataset. Five-fold cross-validation was used to estimate validation accuracy.  
Two feature sets were tested as inputs to machine learning models: one using only the data from the three machine sensors and one using both the three sensors and the four process settings. The classifications of porosity and porosity type were carried out independently. The overall classification accuracy was used as the measure to compare performance. While this measure has limitations, especially in biased datasets like we find here, class balancing was used to mitigate this.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
###### Abstract  
Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the \(F_{1}\) score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.  
biometrics; sEMG; pattern unlock; time domain feature; one-class classification +
Footnote †: journal: Applied Sciences
## 4 Event-Triggered Invocation  
Feedback schedulers are usually time triggered. An obvious advantage of this mode is that it makes it convenient to design and analyse the feedback schedulers using feedback control theory and techniques. In wireless environments where the environmental changes including noise interference and node movement are irregular and bursty, however, it could be very difficult to choose an appropriate invocation interval for time-triggered feedback schedulers.  
On one hand, the invocation interval cannot be set too small because accurate DMRs would be impossible to obtain. Therefore, the feedback scheduler with a large invocation interval will not be capable of coping with, in a timely fashion, interference and node movement that occur between two consecutive invocation instants. On the other hand, when a relatively small invocation interval is used, it is possible that the system stays in steady state for quite a long time, when there is actually no need for sampling period adjustment. In this situation, time-triggered feedback schedulers could potentially waste resources in periodic execution of feedback scheduling algorithms and unnecessary update of system parameters.  
To address this problem, an event-triggered invocation mechanism is proposed to improve the efficiency of feedback schedulers. Discussed below is how to implement this mechanism.
# The Effectiveness of Pump Techniques and Compages: A Systematic Review
## 3 Results
### Qualitative Synthesis
#### 3.3.1. Pump Techniques Versus Light Touch  
Nine studies [44; 45; 46; 47; 48; 49; 50; 56] were identified comparing osteopathic pump protocol versus light touch at short-term follow-up. Outcomes assessed were pulmonary function, blood collection, and length of hospitalization.  
* _Pulmonary function._ Only one study [47] assessed this outcome on patients complaining of Chronic Obstructive Pulmonary Disease (COPD). The OMT group showed a statistically significant decrease in Forced Expiratory Volume in the 1 st second (FEV1), the Expiratory Reserve Volume and airway resistance. The OMT group also had a statistically significant increase in the residual volume, total lung capacity, and the ratio of those values compared with the control group.
* _Blood collection._ Two studies [49; 56] assessed this outcome. No statistically significant changes emerged between the treatment groups and the control groups with regard to white and red blood cell count. In the first study [49] on subjects aged \(\geq\) 60 years, an increase of platelets in the control group and a decrease in the treatment group was observed. In the second study [57] on adult males, a decreased level of monocytes was noted in both groups.
* _Length of Hospitalization._ Four studies [44; 45; 48; 50] assessed this outcome on patients affected by pneumonia and patients operated on for heart surgery. All four studies showed no significant difference between the treatment groups and the control groups. A third group that received standard care was also compared in two studies [48; 50]. In both studies, the length of hospitalization was shorter for the experimental groups than for the standard treatment groups.
* _Cognitive status._ Only one study [46] assessed this outcome on elderly subjects. In this study, comparing the subjects who responded to Mini-Mental State Examination (MMSE) in the \"normal\" range, no differences occurred between the treatment group and the control group.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Evaluation Metrics  
To quantitatively evaluate the performance of different SE models, we employ several commonly used objective metrics. In the evaluation on the VoiceBank+DEMAND dataset, we adopt four evaluation metrics: wide-band perceptual evaluation of speech quality (WB-PESQ) [43] and three mean opinion score (MOS) metrics [44] (i.e., CSIG, CBAK, and COVL). WB-PESQ is used to evaluate perceptual speech quality with a score range from \(-0.5\) to \(4.5\). CSIG, CBAK, and COVL are MOS predictions of signal distortion, background noise suppression, and overall audio quality, respectively. All three MOS scores range from \(1\) to \(5\). For the DNS-Challenge dataset, we take WB-PESQ, narrow-band perceptual evaluation of speech quality (NB-PESQ) [45], short-time objective intelligibility (STOI) [46], and scale-invariant signal-to-noise ratio (SI-SNR) [47] as performance evaluation metrics. STOI measures speech intelligibility, whose score ranges from \(0\) to \(1\). SI-SNR measures the ratio of the power of the clean signal to the power of the noise in a way that is invariant to the scale of the signal. The selection of the aforementioned SE performance evaluation metrics is guided by established practices, community consensus, and the focuses and characteristics of the datasets. Higher values for all above metrics indicate better speech quality.  
In addition, we employ multiply-accumulate operations (MACs) to quantify the computational complexity of the model. This value reflects the total computational costs for a single forward propagation calculation of the DNN model. We adopt the Python (versionnumber: Python 3.7.12, creator: [PERSON]) tool package named _thop_ ([[https://pypi.org/project/thop/](https://pypi.org/project/thop/)]([https://pypi.org/project/thop/](https://pypi.org/project/thop/)) (accessed on 26 September 2024)) to calculate this metric. Additionally, the _thop_ package also allows for the simultaneous calculation of the model size.
## 4 Results and Discussion
### RCCF Performance Evaluation  
#### 4.2.1 Retrieval Effectiveness  
Table 3 presents the retrieval performance of RCCF compared to traditional fusion methods across multiple BEIR datasets. Our RCCF method demonstrates consistent improvements across all evaluation metrics, with particularly notable gains in NDCG@10 and MAP scores.  
The superior performance of RCCF can be attributed to its adaptive fusion strategy that leverages retriever consensus. When retrievers agree on document relevance, RCCF amplifies their collective confidence, leading to a more accurate ranking. Conversely,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Component** & **GPU Hours** & **Peak Memory (GB)** & **Avg. Utilization (\%)** \\ \hline RCCF Pre-training & 78 & 18 & 22 \\ AM-HDGAT (Warmup) & 42 & 31 & 68 \\ AM-HDGAT (Full training) & 512 & 38 & 83 \\ Adversarial Fine-tuning & 184 & 41 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational resource utilization breakdown by training component.  
when consensus is low, the method adopts a more conservative approach, preventing the propagation of uncertain results.
## 6 Conclusions  
We described the deformation characteristics, triggering factors, and possible disaster risks of large landslides in the Jinsha River. Based on 96 scenes of SAR images in three directions, the deformation characteristics of the landslide were obtained using SBAS and three-dimensional decomposition technologies. In addition, we analyzed the relationship between landslide deformation and water level. Furthermore, a numerical simulation of the landslide body was conducted to predict possible disaster risks. The main conclusions are as follows:  
1. The maximum vertical annual displacement rate of the Xiaomojiu landslide reached 12 cm/a; the rear edge and middle part had rapid deformation areas with an increasing deformation trend.
2. The high water level accelerated the deformation of the Xiaomojiu landslide. After the Baige landslide blocked the Jinsha River, the infiltration and erosion of river water accelerated the damage of the landslide.
3. The increasing deformation rate implies that the Xiaomojiu landslide is on the verge of failure. Numerical simulation results showed that the whole sliding process of the rock mass might last about 80 s, forming the deposited area with an area of about 450,000 m\({}^{2}\) and a thickness of 150 m.  
[PERSON] conceived the manuscript; [PERSON] provided funding support and helped improve the manuscript. J.Y. provided the research data. All authors have read and agreed to the published version of the manuscript.  
The work was funded by China Three Gorges Corporation (YMJ(XLD)/(19)110); National Key R&D Program of China (2018 YFC1505002); China Geology Survey Project (DD20221738-2); National Science Foundation of China (41672359).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Characterization of MIL-88A Carriers
#### 2.2.5 Fourier-Transform Infrared Spectroscopy (FT-IR)  
The functional groups on the MIL-88A samples' surface before and after diclofenac sodium adsorption were analyzed by FT-IR spectroscopy. The measurements were carried out utilizing an FT-IR Bruker IFS66/S spectrometer (Bruker, Billerica, MA, USA) with the GLOBAR light source and Michelson interferometer. MIL-88A and MIL-88A--dcllofenac sodium systems--were blended with anhydrous potassium bromide at a mass ratio of 1:200 mg and compressed to form a tablet. The analysis was conducted within a wavenumber range of 4000-500 cm\({}^{-1}\).