esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ML Methods  
Traditional ML methods for NPH detection can be categorized into three main functional areas: classification approaches for differential diagnosis, segmentation and feature  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
analysis techniques, and key feature identification. This organizational structure reflects the progressive stages of ML application in NPH diagnosis: first establishing accurate classification between NPH and other conditions, then developing methods to segment and analyze relevant brain structures and finally identifying the most discriminative features for diagnosis.  
The classification approaches (Section 3.1.1) address the clinical challenge of differentiating NPH from conditions with similar presentations, such as Alzheimer's disease and Parkinson's disease, as well as from healthy controls. These methods employ supervised learning algorithms trained on labeled datasets to establish decision boundaries between diagnostic categories. We examine these approaches based on their comparative diagnostic targets (healthy controls, AD, PSP, or multiple conditions), which represent different clinical scenarios with varying levels of diagnostic complexity.  
The segmentation and feature analysis techniques (Section 3.1.2) focus on isolating and quantifying relevant brain structures and CSF dynamics. These methods address the technical challenge of accurately delineating ventricles, brain parenchyma, and subarachnoid spaces--crucial regions for NPH diagnosis--and extracting meaningful measurements from these segmentations. We analyze these approaches based on their anatomical targets and technical methodologies. The key features section (Section 3.1.3) synthesizes findings across studies to identify the most diagnostically valuable measurements and biomarkers. This analysis helps establish which parameters should be prioritized in future ML model development for NPH diagnosis and provides insight into the underlying pathophysiological mechanisms of the condition. Together, these three areas provide a comprehensive view of traditional ML applications in NPH detection, from the clinical challenge of differential diagnosis to the technical challenges of image analysis and feature extraction.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 2 Materials and Experimental Methods
### Experimental Methods
#### 2.2.3 Environmental Impact Assessment Methods  
(1) Composition analysis of infiltration fluid  
The SL used for subgrade soil filling has low activity and fewer adverse reactions and emissions; however, the biggest hidden danger is the potential pollution of infiltration fluid in the subgrade. According to the Chinese specification (GB 5085.3-2007), the composition analysis of infiltration fluid was carried out to measure copper, zinc, cadmium, lead, chromium, hexavalent chromium, mercury, barium, nickel, arsenic and selenium [32]. In accordance with the Chinese specification (HJ/T 298), after the samples were treated, the contents of relevant elements were measured using a GGX-810 atomic absorption spectrophotometer, an AFS-8520 atomic fluorescence spectrophotometer and a T6 ultraviolet visible spectrophotometer, respectively. Table 1 lists the specific determination methods and standards.  
(2) Permeability test  
Permeability evaluation is an important indicator for the application of industrial solid waste in the subgrade. According to the Chinese specification, the permeability test was carried out by using T 0130-2007. The test water was degassed with the boiling method before the test, and the specimens were prepared using a circular knife with an inner diameter of 61.8 mm and a height of 40 mm. Considering the actual engineering situation, the variable head permeability test was conducted on the specimens with the compactness of 96% at the room temperature of 20 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Element** & **Standard** & **Instrument** & **Detection limit** \\ \hline
1 & Cadmium & HJ 787-2016 & GGX-810 & 0.9 \(\mu\)g/L \\
2 & Lead & HJ 786-2016 & GGX-810 & 0.6 \(\mu\)g/L \\
3 & Zinc & HJ 786-2016 & GGX-810 & 0.06 mg/L \\
4 & Arsenic & & 0.10 \(\mu\)g/L \\
5 & Mercury & HJ 702-2014 & AFS-8520 & 0.02 \(\mu\)g/L \\
6 & Selenium & & 0.10 \(\mu\)g/L \\
7 & Copper & HJ 751-2015 & GGX-810 & 0.02 mg/L \\
8 & Nickel & & 0.03 mg/L \\
9 & Total chromium & HJ 749-2015 & GGX-810 & 0.03 mg/L \\
10 & Hexavalent chromium & GB/T 15555.4-1995 & T6 & 0.004 mg/L \\
11 & Barium & GB 5085.3-2007 & GGX-810 & 0.1 mg/L \\ \hline \end{tabular}
\end{table}
Table 1: The specific determination standards and instruments in the composition analysis of infiltration fluid.
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 5 Performance Evaluation
### Performance Metric Modeling
#### 5.1.1 Response Time Definition and Measuring Method  
During the establishment of a security association, once a long-term TGT has been previously obtained by the subject, five messages are exchanged, as detailed in Section 3.3. Therefore, this response time includes the steps concerning when the subject requests and obtains the service ticket, the notification that the ticket is granted, the policy provisioning in the CDS by the ACS, and the security association request and response between the subject and the CDS.  
To measure this time, a few code lines are inserted into the subject-side software code, therein applying two timestamps: one at the beginning of the security association establishment and the other at the end of this establishment.
# A Regression-Based Technique for Capacity Estimation of Lithium-Ion Batteries
## 3 Capacity Estimation  
### Calculation of \(R_{0}\)  
The equivalent circuit model for the battery is shown in Figure 2; the relationship between circuit parameters are given by the following equation:  
\[v_{b,k}=OCV(SOC_{k})+Mh_{k}-\sum_{i}R_{i}i_{R_{i,k}}-i_{b,k}R_{e} \tag{5}\]  
In the above equation, \(v_{b,k}\) and \(i_{b,k}\) are the battery voltage and current, \(OCV(SOC_{k})\) is the open-circuit voltage that is a function of state of charge, \(Mh_{k}\) is hysteresis voltage, \(R_{i}i_{R_{i,k}}\) is the multiple of \(i^{th}\) resistance of capacitor-resistance nets in the corresponding current, and \(i_{b,k}R_{e}\) is the multiple of series resistance in the battery current.  
If we define \(S^{Re}_{vb}\) as the sensitivity of voltage to rate of change in resistance \(R_{e}\), we have:  
\[S^{Re}_{v_{b,k}}=\frac{R_{e}}{v_{b,k}}\ \frac{dv_{b,k}}{dR_{e}}=\frac{-R_{e}}{v_{ b,k}}\ i_{b,k} \tag{6}\]  
The equation shows that bigger \(i_{b,k}\) leads to higher sensitivity to changes in \(R_{e}\). In this part a method is described for estimating of \(R_{e}\) by subtracting \(v_{b,k}\) of two adjacent samples:  
Figure 1: Loss in battery total capacity versus time: (**a**) normal transition of ions between electrodes. (**b**) Effect of side reaction on transition. (**c**) Impact of structural decay on ion loss.  
Figure 2: Equivalent circuit model.  
\[\begin{array}{l}v_{b,k}=OCV(SOC_{k})+Mh_{k}-\sum\limits_{i}R_{i}i_{R_{k,i}}-i_{ b,k}R_{e}\\ v_{b,k-1}=OCV(SOC_{k-1})+Mh_{k-1}-\sum\limits_{i}R_{i}i_{R_{k,k-1}}-i_{b,k-1}R_{e} \\ \hline\overline{v_{b,k}-v_{b,k-1}=R_{e}(i_{b,k-1}-i_{b,k})}\end{array} \tag{7}\]  
As can be seen, the rate of change in \(R_{e}\) is impressively bigger than other variables in the equation:  
\[\hat{R}_{e,k}=\frac{v_{b,k}-v_{b,k-1}}{i_{b,k-1}-i_{b,k}} \tag{8}\]  
where \(\hat{R}_{e,k}\) is the estimated and not exact value of \(R_{e}\). It should be considered that \(\hat{R}_{e}\) could be estimated only when \(\Delta i_{b,k}\
eq 0\). In this regard, a minimum limit value has been considered in the simulation as the threshold in order to not consider minimal values of \(\Delta i_{b,k}\). Therefore, using a filter simply can reduce the error:  
\[\hat{R}_{e,k}^{\mathit{filt}}=\alpha\hat{R}_{e,k-1}^{\mathit{filt}}+(1-\alpha )\hat{R}_{e,k} \tag{9}\]  
where \(0\ll\alpha<1\). The equivalent series resistance is SOC-dependent and temperature-dependent. By defining a vector of \(R_{e}\) in different SOC levels, the first problem will be solved, and by adopting a matrix of resistance for various temperature and SOC levels, both will be solved.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 2 Digital Twin Framework for Real-Time Monitoring and Optimization of Underground Caverns  
### Structure of Digital Twin  
Based on the number of data that are generated throughout the lifecycle of underground cavern construction projects, a five-dimensional digital twin framework is put forward for deformation analysis and rock support optimization. The five-dimensional components are physical objects, virtual objects, service system, DT data, and their connections, as shown in Figure 1. Among them, physical entities are the real-world underground caverns, borehole logs, and geology encountered, as well as sensors for recording real-time deformation data, geological mapping, and rock support elements that stabilize the underground caverns. The virtual part provides the virtual understanding of geology, project design, deformation simulation, and actual deformation from the design to the operation phase of the project. The behavior of physical entities is controlled or reduced within the allowable limit by carrying out optimization and deformation analysis in the virtual world. The virtual part should be a digital mirror and structured strictly similarly to physical entities to enhance the understanding of behavior in the real world. In this proposed DT, the developed DT virtual modules involve construction-related aspects, which are geology, rock support, and excavation, and structure monitoring-related aspects, which are behavior and stability. It integrates Industry Foundation Classes (IFC) to ensure standardized data exchange, real-time monitoring, and interoperability within BIM-based underground cavern stability optimization. IFC extensions support geotechnical monitoring, excavation sequencing, deformation tracking, and rock support assessment, enabling dynamic updates and data-driven decision making for enhanced structural stability.  
The DT service system documents the data and information of physical and virtual parts with reference to the construction process, deformation monitoring, design information, and its optimization. It also provides real-time information to all project stakeholders, enabling them to understand and make decisions on time. DT data include all the data and information from physical entities, virtual entities, and service systems. The type of data in the proposed DT consists of deformation sensor data, survey data, semantic information, geological data, and analysis data. These data are continuously updated depending upon the data update requirements and generated in real-time to provide feedback for the continuous efficient iterative optimization of the design and stability of underground infrastructure.
## 3 Results
### Point Cloud Accuracy Evaluation
#### 3.2.2 Comparison to Other Clouds  
To find point cloud deformations without dependence on checkpoints, point clouds were compared in CloudCompare software mostly with MLS_1, a point cloud tied on TS points with 50 m spacing. The smallest deviations were expected for this point cloud.  
The 3D deviations were then calculated from the coordinate differences and are given in Table 11. Figure 7 shows maps of clouds in comparison with MLS_1. These compared clouds have various spacing of control points. The maps show the deviations and visualise the cloud deformation. Figure 8 then shows a comparison of clouds with a different spiky strategy but with similar results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point Cloud Name** & **Control-Point Specs** & **StDev XYZ [mm]** & **RMS XYZ [mm]** \\ \hline MLS\_1 & Adjusted (TS) 50 m & 24 & 28 \\ MLS\_2 & Adjusted (TS) 1000 m & 29 & 31 \\ MLS\_3 & GNSS RTK 50 m & 32 & 32 \\ MLS\_4 & GNSS RTK 250 m & 30 & 33 \\ MLS\_5 & GNSS RTK 1000 m & 29 & 31 \\ MLS\_6 & Untied & 30 & 36 \\ MLS\_7 & Adjusted (TS) 50 m & 22 & 25 \\ MLS\_8 & GNSS RTK 1000 m & 25 & 26 \\ MLS\_9 & Untied & 28 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mean standard deviations and root mean square deviations of point clouds.  
Figure 7: Maps of point cloud comparison with different control points strategies.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission
### Post-Deployment Domain Adaptation
#### 3.3.2 Online Adaptation  
Online adaptation directly updates \(f^{d}\) on \(\tilde{\mathcal{D}}^{t}\) aboard the satellite. Therefore, it does not require downlinking \(\tilde{\mathcal{D}}^{t}\) to ground. The source dataset \(\mathcal{D}^{s}\) is also assumed to be unavailable on the satellite due to lack of storage. Hence, the problem is an instance of TTA (see Section TTA).  
An important requirement of online adaptation is a suitable runtime environment on satellite-borne edge compute devices that can execute the TTA algorithm. A runtime environment that stores a full-fledged machine learning framework (e.g., PyTorch [74]) and its associated dependencies can require up to several gigabytes of disk space. Such resources are not available on edge devices. Furthermore, the runtime environment may need to be updated during the life of the mission due to bug patches. Uplinking these updates may also not be possible, especially for large runtime environments. The problem is summarised as follows:  
Figure 3: Post-deployment, target data are collected and downlinked to a ground station. Offline adaptation is then performed at the ground station, followed by uplinking of the model updates to the satellite.  
In Section 6.2, we describe our steps to execute state-of-the-art TTA algorithms on a testbed that simulates the compute payload of a EO satellite. This establishes the viability of TTA on space hardware.
## 2 Methodology
### General Principle of the Methodology  
This paper advocates for the utilization of an AD in regard to the optimization of dynamic systems governed by hybrid ODEs. Within the optimization loop, a time-domain simulation of a system with changing configurations is executed without retaining the values until a steady state is attained (the details of this process will be elaborated in this section). Subsequently, the simulation is conducted over a single operating period to compute various sizing criteria (such as average values, FFT, etc.) for certain variables before concluding the simulation, thereby avoiding unnecessary computations. From this, both the time-domain and frequency-domain characteristics of the simulated variables can be extracted for constraining purposes. The overarching sizing model as part of the optimization process is illustrated in Figure 3.  
The optimization process is anchored in Sequential Quadratic Programming (SQP) [17], intricately linked to the sizing model, which encompasses the following components:  
* An ODE system, along with tests designed to detect their variations, enabling the computation of time derivatives of state variables and beyond;
* A time-domain simulator that adeptly resolves problems in regard to ODE systems, yielding the values of state variables throughout the simulation, without necessitating post-processing of the sizing data;
* A criterion for halting the time-domain simulation, which involves the computation of sizing criteria subsequent to the detection of the steady-state conditions;
* An extractor of the sizing criteria that provides both time-domain and frequency-domain features, derived from an FFT computation, and that serve as constraints within the optimization problem;
* The gradient-based optimization algorithm SQP, which adeptly addresses the complexities of nonlinear constrained optimization.  
Figure 3: The global sizing model as part of the optimization process (generic algorithms are in red, application specific models and computer code are in blue).  
A robust interconnection between the computation of the sizing criteria and the dynamic process has been deliberately established, thereby obviating the need for state vector storage and facilitating the implementation of advanced control strategies, such as the stopping criteria delineated in this paper.  
In the subsequent subsections, we shall elaborate on the various facets of the methodology, including the selection of derivation techniques, the \"white-box\" hybrid ODE solver, the identification of events that necessitate alterations in the configurations of dynamic systems, the detection of steady-state conditions, and the extraction of the sizing criteria.
## 3 SDR Technology
### GNU Radio  
GNU Radio is a set of free and open-source tools aiming to implement SDR systems and provides ready-to-use signal processing blocks [20]. It can be used with COTS SDRs or in a simulation mode. It can be used to write applications, to receive data out of digital streams, or to push data into digital streams, which can be transmitted using specific hardware. GNU Radio contains a variety of blocks such as filters, channel coders, synchronisation elements, equalizers, demodulators, decoders, and many other elements that are typically found in software radio systems. These elements are readily available as software blocks. More importantly, it includes a method of connecting these blocks and then manages how data are passed from one block to another. Adding extra DSP functions can be achieved with the use of embedded Python blocks. These blocks allow the implementation of custom Python code.  
Since GNU Radio 3.10 is software, it can only handle discrete data. Usually, I/Q baseband samples are the input data type for receivers and the output data type for transmitters. SDRs are then used to shift the signal to the desired center frequency. However, within GNU Radio, any data type can be passed from one block to another, e.g., bits, bytes, vectors, integers, and real or complex numbers. When creating new applications in GNU Radio, they are primarily based on Python. Nonetheless, the performance-critical signal  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SDR** & **Frequency** & **ADC/DAC** & **Max. RF** & **RF Channels** & **Price** \\ \hline RTL-SDR Blog V3 & 24–1766 & 8-bit & 3.2 MHz & 1 RX & low \\ \hline Great Scott Gadgets & 1–6000 & 8-bit & 20 MHz & 1 TX/RX & medium \\ HackRF one & & & & & \\ \hline Analog Devices & & & & & \\ ADALM-PLUTO & 325–3800 & 12-bit & 20 MHz & 1 TX–1 RX & medium \\ \hline Nuand & & & & & \\ bladeRF 2.0 xA4 & 70–6000 & 12-bit & 56 MHz & 2 TX–2 RX & medium \\ \hline Ettus Research & & & & & \\ USRP B200 mini & 70–6000 & 12-bit & 56 MHz & 1 TX–1 RX & medium \\ \hline Ettus Research & & & & & \\ USRP B200 & 70–6000 & 12-bit & 56 MHz & 1 TX–1 RX & medium \\ \hline Ettus Research & & & & & \\ USRP B210 & 70–6000 & 12-bit & 56 MHz & 2 TX–2 RX & high \\ \hline Deepwave Digital & & & & & \\ AIR7201-B & 300–6000 & 14–16 bit & 100 MHz & 2 TX–2 RX & very high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Non-exhaustive list of common SDRs that can be used for the reception of LPWAN protocols.  
processing path is implemented in C++ using processor floating-point extensions. Thus, the developers can implement real-time, high-throughput radio systems in an easy-to-use environment.
## 3 Results
### Chemical Characterization of CP Extracts  
The concentration of the main phenolic compounds in the water extracts of CP was determined by High Performance Liquid Chromatography (HPLC). The research was focused on those phenolic compounds, present in the pericarp of chestnut, which were reported to have cytoprotective effects towards normal cells and/or antiproliferative effects on cancer cells. Molecules like gallic acid and ellagic acid, two phenolics of low molecular weight, the ellagitannin isomers castalagin and vescalagin, and flavanols represented by catechin, epicatechin and epigallocatechin [4; 38; 46] were identified and quantified, and the results are reported in Table 4; the chromatogram of ILDP can be found, as an example, in Figure S2 in Supplementary Materials.  
Gallic acid, ellagic acid, vescalagin, castalagin, catechin, epicatechin and epigallocatechin were the seven main phenolic compounds identified in the four accessions and in the marron. Water at 20 \({}^{\circ}\)C was more efficient than hot water at extracting gallic and ellagic acid from the epicarp of all the accessions, but less efficient in the marron. The ellagic acid was the most represented compound in all the samples and its content was 5-fold higher when the extraction was done with cold water. Ellagic acid aside, each accession is distinguished by the prevalence of certain compounds: vescalagin, castalagin and epicatechin characterized the MURG extracts, regardless of the temperature used for extraction.  
LOCG cold extracts were characterized by gallic acid, vescalagin and castalagin, while the hot ones have a high content only of vescalagin, castalagin and, particularly, of epicatechin. ILDP cold extracts could be distinguished by their content of gallic acid, catechin and epicatechin, whereas the hot extracts by vescalagin, castalagin and epicatechin. COEV was distinguished by a low content of all the compounds (thus confirming the results of the spectrophotometric assay) if the extraction took place in cold water, and by a prevalence of castalagin and vescalagin when the extractant was hot water.  
Figure 1: Cyclic voltammetry, with a scanned potential range (E\({}_{\text{app}}\)) comprised between \(-\)0.2 V and +0.8 V vs. carbon pseudoreference, in the absence (PBS black line) and in the presence of 2 mg mL\({}^{-1}\) of MURG (green line), LOCG (purple line), ILDP (red line), COEV (blue line) and MARRV (yellow line) CP cold water extracts (**A**) and hot water extracts (**B**).  
The marron MARRV showed a different phenolic composition from the Sardinian accessions: in cold water extracts, low quantities of all seven molecules were detected, except for ellagic acid, whose value was the lowest among the samples tested. Differently, the hot extracts were characterized by moderate quantities of vescalagin and epicatechin and by a high quantity of catechin, higher than that found in the four accessions.