esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Clinical Profiles, Deformities, and Plantar Pressure Patterns in Diabetic Foot Syndrome
## 2 Materials and Methods  
### Participants  
From January to December 2016, patients with diabetes referring to the Diabetic Foot Clinical Center of the S. Orsola-Malpighi Hospital (Bologna, Italy) were invited to participate in the study. Those who gave informed consent were first screened at the Diabetic Foot Clinical Center and then examined at the Movement Analysis Laboratory of the Istituto Ortopedico Rizzoli (Bologna, Italy) within one week. The following information were collected via the clinical and pediatric screening: biological data (age, gender, body mass index (BMI), and obesity grade [13]), clinical data (type of diabetes, years of disease (YOD), glycated hemoglobin (HbA1c) level, peripheral neuropathy grade based on the Michigan Neuropathy Screening Instrument [14] and the Vibration Perception Threshold (VPT) measurement, the presence of peripheral artery disease or of other relevant comorbidities), and functional and orthopedic data (passive ankle joint mobility [15], hindfoot alignment, hallux mobility and alignment [15], pain via the visual analogue scale (VAS) and the Manchester Oxford Foot Questionnaire (MoX) [16]). On the same day of the examination at the Movement Analysis Laboratory, patients were functionally assessed via gait analysis according to the experimental protocol detailed in the following as experimental protocol 1 (Section 2.2). Those patients who also gave consent to the WBCT acquisitions were examined three-to-six months after the overall assessment according to the experimental protocol 2 detailed in the following (Section 2.3). Seventy-nine patients were enrolled in total, all without peripheral artery disease (Ankle-Brachial Index (ABI) \(>\) 0.9). Only one type 1 patient was in the obesity range and was examined but excluded from the analysis by group. The remaining 78 patients were grouped as follows: type 1 patients with neuropathy (DNT1, 12 patients); type 1 patients without neuropathy (DNNT1, 15 patients); type 2 patients, not obese and with neuropathy (DNT2-no ob, 11 patients); type 2 patients, with obesity and neuropathy (DNT2-ob, 20 patients); type 2 patients, without obesity or neuropathy (DNNT2-no ob, 7 patients); and type 2 patients, with obesity and without neuropathy (DNNT2-ob, 13 patients) (Table 1). The design of the statistical analysis was based on the identified number of groups (6), probability of type I error \(\alpha\) = 0.05, power = 0.80, and effect size = 0.5. The estimated total sample size was 60 (G*Power version 3.1.9.7, Heinrich Heine Universitat, Dusseldorf, Germany). Each patient executed an established gait analysis experimental protocol (experimental protocol 1, see below).
# Discovering Moving Flock Patterns in Movement Data: A Reeb Graph-Based Approach
## 4 Methodology
### Generating Reeb Graphs Based on Movement Data
#### 4.1.1 The Generation of Vertices  
The generation of vertices relies on the parameter \(r\). If multiple objects have spatial locations within a circle of radius \(r\) at the same timestamp, they are considered to belong to the same vertex. It should be noted that the selection of the base object (i.e., the object whose spatial location at a given timestamp serves as the center of the corresponding circle) is important. In this approach, we propose that, for each timestamp, the base object is  
Figure 3: Illustration of the methodology: (**a**) the sample dataset; (**b**) the groups of objects at each timestamp according to parameter \(r\); (**c**) the generated vertices; (**d**) the remaining vertices after removing certain vertices; (**e**) the generated Reeb graphs, and (**f**) the remaining Reeb graph.  
automatically determined as the one closest (in terms of Euclidean distance) to the centroid of all objects at that timestamp. This method enables consistent base object selection, thus can avoid variations of results caused by random selections of the base objects. The vertices of the Reeb graphs are generated according to the following four steps.  
Step 1: for each timestamp, calculate the centroid of the spatial locations of all objects;  
Step 2: find the object closest to the centroid in terms of spatial distance, and consider this object as a base object;  
Step 3: find all objects whose spatial distances to the base object are no larger than \(r\), and group these objects with the base object in the same vertex;  
Step 4: for the remaining objects, repeat steps 1-3 until each object at every timestamp is assigned to a vertex.  
Using the proposed approach, objects at each timestamp can be partitioned into one or more groups, with each group consisting of objects located within a circle of radius \(r\). For example, if we assume that \(r\) is set to a specific value in Figure 2(a), the resulting groups of objects are shown in Figure 2(b). The vertices of the Reeb graphs are illustrated in Figure 2(c), where each point represents a vertex, and the numbers next to each vertex indicate the IDs of the objects included in that vertex.
## 4 Numerical Simulation of Repeated Supportability of Roadway Roof
#### 4.1.3 Numerical Simulation Results When the Direct Top Is Mudstone  
The numerical simulation was conducted for different thicknesses of the direct roof, namely 0.8, 1.6, and 3.2 when the lithology of the direct roof was mudstone. The results of the crushing range and displacement distribution of the roof are presented in Figures 22-24. Figure 25 illustrates the variation in displacement and the relative displacement results on the displacement monitoring lines of the basic roof and direct roof.  
Based on the results presented in Figure 22, it is evident that when the direct roof consists of mudstone and has a thickness of 0.8 m under the combined influence of the original rock stress and repeated support, the maximum displacement on the direct roof surface is 27 mm. Despite the absence of immediate roof caving, the crushing range of the roof has already reached the interface between the direct roof and the underlying basic roof. Additionally, as depicted in Figure 25a, the maximum relative displacement of the two displacement monitoring lines at the interface between the direct roof and the basic roof reaches 9 mm, indicating the occurrence of roof detachment.  
According to Figures 23 and 24, the maximum crushing range of the direct roof reaches 1-1.3 m when the thickness is 1.6 m and 2.4 m, and the surface maximum displacements are 36 mm and 38 mm, respectively. When temporary and permanent supports are coordinated, the maximum relative displacement on the two displacement monitoring lines between the direct roof and the basic roof at the layering interface is less than 3 mm, indicating the stability of the direct roof without detachment.  
In conclusion, when the direct roof is composed of sandy mudstone and its thickness is less than approximately 1.3 m, the crushing range extends to the layering interface, resulting in a certain degree of detachment and posing a safety risk to the permanent support. Therefore, when the roof thickness is less than 1.3 m, the roadway cannot be repeatedly supported, and immediate permanent support is necessary after excavation. However, when the roof thickness exceeds 1.3 m, it can meet the criteria for repeatable support, fulfilling the conditions for phased temporary support.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 4 Preliminaries
### Problem Formulation

Table 5: Transaction Participants for DeFi X and DeFi Y.  
conditions and parameters are met before proceeding with the asset transfer. Select the optimal and suitable bridge \((Bri_{\text{suit}})\) for asset transfer using the following equation:  
\[Bri_{\text{suit}}=\arg\max_{Bri}\{\text{Suit}(Bri,\text{current state})\} \tag{1}\]  
Best miner \(BMi_{X}\) locks the asset \(\Delta A_{X}^{\text{Initial}}\) in the \(AP_{X}\) as follows. \[AP_{X}^{\text{locked}}=AP_{X}^{\text{Initial}}-\Delta A_{X}^{\text{Initial}} \tag{2}\]  
Consequently, \(Smr_{X}\) generates the proof for the locked asset at \(AP_{X}\). Select the bridge using Equation (1) by sending the proof \(X\) to DeFi \(Y\), and the miner \(BMi_{Y}\) verifies whether the received information is true or not. If it is true, the locked asset valued at \(X\) is credited in the \(AP_{Y}\) at DeFi \(Y\). \[Ass(T_{X\to Y})=\text{Bridge}(\text{DeFi }X,\text{DeFi }Y) \tag{3}\]  
This formulation indicates that the selection of miners and bridges is critical in determining the level of interoperability performance and finality guarantee. The asset transfer process is defined as:  
\[AP_{Y}^{\text{Final}}=AP_{Y}^{\text{Initial}}+\Delta A_{X}^{\text{Initial}} \tag{4}\]  
Consequently, \(Bri\) generates the transaction finality proof (\(TFproof\)) of the successful asset transfer and reversely sends it to DeFi \(X\) for confirmation. Interoperability and bridging solutions between any two chains typically support a diverse set of digital assets and charge differently based on their business models. Moreover, these bridges vary in liquidity depths and may appeal to different types and sizes of users and dApp transactions. All this information must be incorporated while modeling the cross-chain networks to ensure effective and efficient asset transfers from a source chain to a destination chain.
## 4 Implementation System and Results
### Tested Background Noise of the P/R Device  
In this test step, we measured the background noise amplitude of the P/R device with different gains from the amplifier module. The goal of this test was to check the P/R device's interference amplitude under normal operating conditions. These results also evaluated the effect of environmental interference on the device when combined with other components of the SAM system. The results are depicted in Figure 16. As the gain increased, the noise amplitude increased, but the maximum amplitude of the noise was 45 mV. Usually, these values are quite small relative to the amplitude of the echo signal. This result was important for identifying and eliminating interference from other devices that could affect the echo signal.  
Figure 16: Amplitude of the background noise with differences in gain.  
Figure 15: (**a**) The pulser signal from the FPGA to the STHV; (**b**) The signal excitation transducer.  
We ran tests with different echo signals. The maximum amplitude of the echo signal was about 3.5 V. The echo signals, after going through the amplifier module, had amplitudes higher than 3.5 V, but then the output signals were distorted.
## 3 Mechanical Analysis of Inner Wall of Buried Non-Corroded Pipeline in Seasonal Frozen Soil Region  
### The Effect of Differential Frost Heave of Soils on the Deformation of Buried Pipelines  
The diameter, wall thickness and burial depth of the buried non-corroded pipe (X65) were known to be 0.762 m, 0.0175 m and 1.6 m, respectively. The specific boundary conditions of the model were described in Section 2.2. When the internal pressure of the pipe is 10.0 MPa and the temperature of the medium in the pipe is 3 \({}^{\circ}\)C. Combined with the two-dimensional plane diagram of the synergistic deformation of pipeline-soil in Figure 8. The change law of vertical deformation and Mises equivalent stress of buried non-corroded pipelines in the seasonally frozen-ground region with temperature change (within the 20 th year) is as follows.  
1. According to the Vertical Displacement Curve as shown in Figure 9, the average vertical displacement of buried pipelines increases with decreasing temperature when at 90 D (October) to 240 D (March). The maximum vertical displacement occurred in the frost heave section of 180 D (January), which was 0.033 m. At this time, the vertical displacement deformation of the top, bottom and sides (9 o'clock direction) of the inner wall of the buried pipeline is shown in Figure 10, which reflects the deformation form of the buried pipeline under the differential frost heaving of the soil when the temperature is the lowest.  
Figure 8: The simplified figure of differential frost heaving deformation of soil and buckling deformation of pipeline.  
Figure 9: Vertical displacement of the pipeline as a function of temperature.  
2. The effect of subzero temperature from 120 D (November) to 210 D (February) on Mises equivalent stress is shown in Figure 11. When the temperature is lowest at 180 D (January), Mises equivalent stress is maximum. At this time, the Mises equivalent stress peak points, 241.6 MPa and 323.8 MPa, occur at the top (12 o'clock) of the inner wall of the pipeline approximately 1.5 m to the left of the junction of the soil transition section and the frost heave section and at the side (9 o'clock) of the inner wall of the pipeline approximately 2.5 m to the right of this junction. 3. Due to the differential frost heaving of soil, the buried pipeline generates buckling deformation near the junction of the transition section and the frost heaving section. The offset along the pipeline length reaches the maximum in the buckling section, but it is much smaller than the vertical offset of the pipeline. In addition, due to the existence of circumferential extrusion force and axial friction force of soil on the pipeline, the offset along the length direction of the pipeline gradually decreases from the buckling section to both sides and finally tends to zero. As shown in Figure 8, the non-uniform tension between the cross-section M\({}_{1}\) and cross-section M\({}_{2}\) of the pipeline leads to the non-linear shear stress on the inner wall of the pipeline in the buckling section.
# PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting
## 2 Related Work
### Blockchain Scalability Solution  
Currently, the utilization of a single-chain structure in most consensus protocols results in a sequential block generation process, thereby constraining the throughput of blockchain systems. To address this scalability issue and alleviate the limitations imposed by serialized writing on a single chain, parallelization emerges as an effective approach.  
It is a widely accepted notion to enable nodes to concurrently extend parallel chains in order to enhance the throughput of the blockchain. In terms of proof-based consensus, OHIE [18] serves as an example for parallel-chain schemes. It adopts a \(k\)-chain structure comprising \(k\) parallel instances, each implementing a PoW consensus protocol. To establish the serialization of blocks across these \(k\) chains, an additional tuple \((rank,next\_rank)\) is included within each block. The blocks are then sorted based on ascending rank values. Monoxide [17] divides workload encompassing communication, computing, and storage into multiple independent and parallel consensus zones, with one blockchain dedicated to each zone. On the other hand, in voting-based consensus mechanisms like VaaP [15], a parallel chain operates on every node, aiming at improving scalability.  
The scalability solution for blockchain extends beyond parallel chains to include a DAG. Unlike parallel-chain protocols that have multiple independent genesis blocks during initialization, DAG-based protocols only have one genesis block. Here, a genesis block means the initial founding block of a chain. These consensus protocols transform the ledger from a single chain into a DAG structure, enabling high concurrency support [31]. An example of proof-based consensus is GHOST [32], which treats the candidate block set and its reference relationship as a tree and selects the maximum weight subtree as the final main chain. Building upon GHOST, Conflux [19] enhances throughput and transaction confirmation speed by incorporating block references and explicit sequencing rules in its design. In the context of voting-based consensus, DAG-Rider [21] is an asynchronous BFT protocol that comprises two layers: a round-based structured DAG for reliable message dissemination and a zero-overhead consensus protocol that enables nodes to independently determine a total order of messages to commit without incurring additional communication overhead. Tusk [22] represents an enhanced version of DAG-Rider, which reduces block generation round delays under normal circumstances. The synchronous variant of Bullshark [23], built on top of Tusk, further minimizes latency while being easier to implement. Meanwhile, the asynchronous edition of Bullshark incorporates fallback voting mechanisms that endow it with comparable low-latency performance as its synchronous counterpart in regular scenarios.  
However, regardless of the parallelization method employed in a consensus protocol, it often exhibits high latency due to the waiting time before a block is committed.
## 4 Experimental Results of Applying Robust-EKF  
Numerous investigations on robust-EKF for GNSS data exist in the literature [13; 14; 15]. In [13], robust-EKF is used for GEO/IGSO/GPS Raw-PPP/INS data fusion. In [14], it is applied for GPS/Galileo/GLONASS data combination. In [15], it is used for GPS/GLONASS data combination. All these studies assert the significant improvement of the position precision brought by the application of robust-EKF. In this paper, we combine the robust-EKF with the RAIM algorithm to ensure the position precision. The details of this robust-EKF and the experimental results for GNSS data are presented in the sequel in Sections 4.1 and 4.2 respectively.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method  
The framework of the algorithm in this paper is shown in Figure 1. First, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Second, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. Then, the obtained light components are divided into high-light areas and low-light areas and enhance them separately. Each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is then utilized to further enhance image details.
# Pharmaceuticals in Coastal Waters: An UHPLC-TOF-MS Multi-Residue Approach
###### Abstract  
Anthropogenic chemical contamination represents a key stressor of natural environments with pharmaceuticals comprising a particular group of emerging pollutants with the potential to induce biological responses in non-target organisms. Therefore, an analytical method based on ultra-high-performance liquid chromatography coupled to time-of-flight tandem mass spectrometry (UHPLC-TOF-MS) was developed for estuarine and seawaters, targeting 63 globally used pharmaceuticals (including amoxicillin, ciprofloxacin, sulfamethoxazole, trimethoprim and venlafaxine included in the Surface Water Watch List) from 8 therapeutic groups: antibiotics, analgesic, NSAIDs, antide-pressants, \(\beta\)-blockers, lipid regulators, anticonvulsants and antihypertensive drugs. The method presents high selectivity and sensitivity, with the limits of detection ranging from 0.01 to 8.92 ng/L and the limits of quantification from 0.02 to 29.73 ng/L. Considering precision, the highest value was achieved for amoxicillin (20.9%) and the lower for ofloxacin (2.6%), while recoveries ranged from 80.6 to 112.6%. Overall, the quantification method was highly efficient for multi-residues quantification in such complex environmental samples.  
Environmental contamination; emerging contaminants; drugs; seawater; UHPLC-TOF-MS
## 4 Analysis
### Markov Processes (Uncertainty Modeling)
#### 4.1.3 Transition Matrix P  
The transition matrix P encapsulates the probabilities of moving from one state to another. It is a square matrix where each element represents the probability of moving from state i to state j.  
\[P(S_{1}\to S_{1})\ldots P(S_{1}\to S_{2})\ldots P(S_{1}\to S_{3})\ldots P(S_{1}\to S_{4})\]  
\[P(S_{2}\to S_{1})\ldots P(S_{2}\to S_{2})\ldots P(S_{2}\to S_{3})\ldots P(S_{2} \to S_{4})\]  
\[P(S_{3}\to S_{1})\ldots P(S_{3}\to S_{2})\ldots P(S_{3}\to S_{3})\ldots P(S_{3} \to S_{3})\]  
\[P(S_{4}\to S_{1})\ldots P(S_{4}\to S_{2})\ldots P(S_{4}\to S_{3})\ldots P(S_{1} \to S_{2})\]  
\[P(Si\to Sj)\ =\ \frac{Number\ of\ transitions\ from\ S_{i}\ to\ S_{j}}{Total\ number\ of\ transitions\ from\ S_{i}}\]  
\[\sum_{j=1}^{n}P(S_{i}\to S_{j})\ =1,\forall i\in\{1,2,\ldots,n\}\]  
where  
\[i,j\in\{1,2,\ldots,n\}\]  
The transition matrix P is then formed by these transition probabilities \(P_{ij}\), with each entry representing the probability of transitioning from state \(S_{i}\ \to S_{j}\).  
Each row sums to 1, reflecting the total probability of all transitions from a given state.  
Filling in the values from the transition probabilities, the matrix becomes:  
\[p\begin{pmatrix}0.10&0.70&0.20&0.00\\ 0.00&0.10&0.10&0.80\\ 0.10&0.00&0.40&0.50\\ 0.00&0.00&0.00&1.00\end{pmatrix}\]
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
## 3 Gas Concentration Calibration
As the CH\({}_{4}\) mass concentration increased from 400 ppm to 5000 ppm, the absorption spectrum amplitude increased linearly from 0.0005 to 0.0046. This relationship  
Figure 5: Calibration of laser wavelength and current under different temperatures. Figure 6: Absorption spectrum of CH\({}_{4}\) with different mass concentrations. was fitted as \(y\) = 9.05 \(\times\) 10\({}^{-7}x\) + 1.36 \(\times\) 10\({}^{-4}\) (R\({}^{2}\) = 0.995), where \(y\) represents the absorption spectrum amplitude and \(x\) represents the CH\({}_{4}\) mass concentration. This equation was used to determine the CH\({}_{4}\) mass concentration during the in situ testing. Figure 8 shows the absorption spectrum of the gas sample with a methane concentration of 5000 ppm at a distance of 2 m. The signal amplitude and noise amplitudes were 0.0045 and 5.4 \(\times\) 10\({}^{-5}\), respectively. The signal-to-noise ratio (SNR) was calculated to be 83.33. The signal is taken as the detection limit of the apparatus when SNR = 1. Thus, the detection limit at the distance of 2 m was 5000/83.33 = 60 ppm. For the testing distance of 10 m, the detection limit was 60 \(\times\) 2/10 = 12 ppm.