esa-sceva/satcom-chunk-collection huggingface dataset:
# Uniform Circle Formation for Fully, Semi-, and Asynchronous Opaque Robots with Lights
## 5 The Algorithm for the Fully Synchronous Mode
### Summary of the Algorithm  
The strategy of the algorithm aims at finding and adopting regular tuples in \(C_{H}\) as follows:  
* Selects unambiguously some robots to be the pivots of the future regular tuples (see Definition 1).
* Makes some robots move to form regular tuples with the selected pivots. Indeed, these movements are not necessary if the regular tuples are already formed. Once the regular tuples are settled, the involved robots will not move anymore, thus fixing once and for all the base angle \(\frac{2\pi}{n}\) of the regular \(n\)-gon to be formed.
* Makes the other robots compute their destination points (on the basis of the regular tuples) and move to form the regular polygon. Moreover, the algorithm must deal with the following issues:* Robots have constant memory (color lights). So, some information, such as the cardinality of the system, cannot be stored.
* Obstructed visibility can cause deadlocks and collisions in certain system configurations. To avoid them, robots will lie and move safely onto specific segments, namely safe diameters and chords (see Definitions 4 and 5). No robot will cross these safe segments.
* In the fsynch mode, an algorithm is efficient if it can fully exploit parallelism (roughly speaking, if it makes the most of the robots move simultaneously during the same round, hence leading to quick executions). Thus, the algorithm has to avoid situations yielding strictly sequential dynamics.  
The next subsections show how the algorithm works, cycle by cycle.
## 2 Numerical Analysis Setup  
### Geometric Models  
In this study, a hypothetical saturated layered soil of 20 m thickness is considered, comprising a 10 m-thick non-liquefiable soil in the lower layer, a 2 m-thick non-liquefiable soil in the upper layer, and an 8 m-thick liquefiable layer located at a depth of 2 m-10 m between the two non-liquefiable layers, and the water table is assumed to be at the ground surface. The underground structure is a typical one-story and three-span utility tunnel, based on the actual design of the utility tunnel project at Beijing Daxing International Airport. The structure has a height of 3.5 m, a width of 9.5 m, a longitudinal dimension of 20 m (i.e., the longitudinal dimension of the standard section of the utility tunnel), side walls and slabs with a thickness of 0.4 m, and internal partition walls with a thickness of 0.25 m. The structure is located at different depths of liquefiable soil in different models. In this study, five burial depths of 2 m, 2.5 m, 3 m, 3.5 m, and 4 m are set to analyze the effect of burial depth on the seismic response of the structure, as shown in Figure 2. Figure 3a shows the numerical model of the utility tunnel at a 2 m burial depth. In the following analysis, the mid-section S in the longitudinal direction of each model is selected as the control surface, and the grid of the control surface, the location of the control points, and the structural details of the utility tunnel are shown in Figure 3b.  
Figure 2: Stratigraphic conditions and location of the utility tunnels in the numerical models.
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## 2 Materials and Methods
### Charge/Discharge Ratio--CDR [-]  
The charge/discharge ratio (CDR) is an important factor for load management. In the case of an EHF, because (heat production-storage-emissions) are in the same system, the CDR can be defined for the EHF by itself.  
Depending on what is desired, for the same amount of stored energy, some storage energy systems are able to release a large amount of energy in a short period of time, while others release it during a longer period of time. To characterize this particularity, a charge/discharge ratio is proposed. This means, a system with a high CDR allows the shifting of short peak periods with high peaks while a lower CDR allows the shifting of longer peak period with lower peaks. For a night-control strategy, a low CDR is required.  
\[\mathrm{CDR}\,=\,\mathrm{CT/DT} \tag{6}\]  
Figure 6: Components of a heating system with a storage system and their efficiency.  
Considering the maximal electrical power of the heating system \((P_{max})_{Heating\ syst}\ (kW)\) and \(V\) the volume of the storage system, the charging time is considered as the duration (per day) needed to completely charge the system. It can be estimated as follows:  
\[CT=\frac{ESD\times V\times\eta_{1}\times\eta_{2}}{(P_{max})_{Heating\ syst}} \tag{7}\]  
It is more complicated to define the discharging time. In fact, as a capacitor, the discharge power of a sensible thermal storage system decreases exponentially with the decrease of the temperature difference. The electrical network analogy is used to calculate the discharge time (DT). In this study, 90% is selected to characterize the discharge time:  
\[DT=\ln{(10)}\times RC \tag{8}\]  
where \(R\) and \(C\) are defined as:  
\[R=1/hC=C_{p}\times\rho\times\eta_{3}\times\eta_{4}\times e \tag{9}\]  
where \(h\) is the total heat transfer coefficient. The value is defined as 11 W/m\({}^{2}\cdot\) K [34; 35; 36].
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Video Encoder Layer
#### 3.3.3 Multimodal Feature Fusion and Optimization  
**Temporal Modeling**: Absolute temporal encodings \(\mathbf{E}_{\text{time}}\) and cross-frame attention mechanisms jointly model long-range dependencies across video frames. For frame \(t\), the attention query \(\mathbf{Q}_{t}\) attends to keys \(\mathbf{K}_{t-\Delta}^{t+\Delta}\) within a temporal window \(\Delta\), capturing sub-second to minute-level events.  
**Spatial-Target Localization**: Explicit coordinate embeddings for bounding boxes \(\mathbf{B}\in\mathbb{R}^{4}\) and keypoints \(\mathbf{P}\in\mathbb{R}^{2}\) are concatenated with visual tokens:  
\[\mathbf{z}_{\text{aug}}=[\mathbf{z}_{L};\mathbf{BW}_{b};\mathbf{PW}_{p}], \tag{14}\]  
where \(\mathbf{W}_{b}\in\mathbb{R}^{4\times D}\) and \(\mathbf{W}_{p}\in\mathbb{R}^{2\times D}\) project spatial coordinates into the embedding space. This enhances pixel-level localization for objects in documents or videos [40].  
**Computational Optimization**: 1. Sparse Attention: Attention scores are sparsified using top-\(k\) selection or locality-sensitive hashing (LSH) [41] to reduce memory usage. 2. Dynamic Resolution Scaling: Patch sizes \(P_{t}\) are adjusted based on input complexity, prioritizing fine-grained patches (\(16\times 16\)) for text-rich regions and coarse patches (\(32\times 32\)) for homogeneous areas.
## 2 Materials and Methods
### Signal Processing and Algorithm Development  
The analysis of the collected data followed a systematic approach combining signal processing and algorithm implementation. Initial signal processing involved filtering and smoothing of the raw IMU data (10 Hz) through a moving average filter to reduce noise while preserving essential movement patterns, as shown in the comparison between raw and filtered signals in Figure 2A. The algorithm then processed this filtered data to identify the three critical performance phases through automated detection of transition points, illustrated in Figure 2B.  
Figure 1: Standardized placement of the WIMU PRO(tm) device on the canoe (red circle).  
Then, for the algorithm development, we focused on the detection and analysis of three critical phases in canoe performance: initial acceleration, steady-state cruising, and final sprint. The steps that we propose for our own algorithm are the following:  
Step 1: smoothing the signal considering means in subintervals.  
Step 2: define the cruising speed as the mean of the first speed, where the smoothed signal decreases, and the two consecutive means.  
Step 3: compute the local maximum and minimum.  
Step 4: compute the range as the interval between the absolute maximum and the absolute minimum.  
Step 5: Construct the best-fit polynomial (in a least-squares sense) for the data in the interval [0, t\({}_{\text{S}}\)], where t\({}_{\text{S}}\) is the time where the speed is stabilized (Step 2). Compute the average of the slope of the polynomial. We consider the first-order polynomial, since a line reproduces the behavior of the signal in the first phase, but the algorithm can use second-order polynomials.  
We refer to Appendix A at the end of this paper, for a pseudocode explanation of these steps.
## The System Model
### Network Model
All neighboring communication links \(m_{ij}\) construct an adjacency matrix:  
\[M\left(\mathcal{G}\right)=\left[ \begin{array}{ccccc}m_{00}&m_{01}&m_{02}& \cdots&m_{0n}\\ m_{10}&m_{11}&m_{12}&\cdots&m_{1n}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ m_{n0}&m_{n1}&m_{n2}&\cdots&m_{nn}\end{array} \right] \tag{3}\]  
where we define \(m_{ii}=0\). Moreover, each sensor node may belong to one or more neighboring nodes' sets, and can calculate we \(M\left(\mathcal{G}\right)\) independently.
## 4 Test Scenarios
### Scenario II: Maritime Navigation under the Influence of Jamming on GNSS L1/E1/B1 Signal  
The L1 jammer was used to impair the L1/E1/B1 signals in this experiment. The jamming power was adjusted using a programmable attenuator. Intentional jamming started at around 90 th second from the beginning of the experiment. The jamming power was increased with a step of 10 dB after each 10 s interval until around 120 s. At 130 s, the process was reversed (i.e., the jamming power was decreased by 10 dB after each 10 s interval until around 160 s). For the jamming on L5/E5a/E5b in Scenario III, the data  
Figure 6: Average C/N\({}_{0}\) of tracked GNSS signals without the presence of intentional jamming.  
collection and jamming process was identical. As seen from Figure 7, the average C/N\({}_{0}\) of L1/E1/B1 signal experienced a drop each time J/S power increased. The degradation of C/N\({}_{0}\) is quite significant at 120 s where the J/S power is at its maximum. In this experimental scenario, the L5/E5a/E5b signals remain unaffected, as seen in Figure 7.
## Materials and Methods
### Sample Collection and Treatment  
A total of 63 topsoil samples (0-30 cm) were collected in the study area in October 2020 using the grid distribution method. Sampling sites were geolocated by GPS. Soil samples were crushed, sorted, and air-dried. Wearing polyethylene gloves, the air-dried soil samples were picked up, stone and plant at the same time, using mortar grinding, 200 mesh sieve, and all samples were placed into a polyethylene plastic bag. The ground soil samples were digested with HClO\({}_{4}\)-HNO\({}_{3}\)-HF, and the contents of Pb, As, Cu, Zn, Ni, and Cr were measured by inductively coupled plasma optical emission spectrometer (ICP-OES). The contents of NH\({}_{4}^{+}\)-N, NO\({}_{3}^{-}\)-N, AP, and AK were measured by high precision soil nutrient detector. The soil pH was measured at a water-soil ratio of 2.5:1 and the electrical conductivity (EC) was measured at a water-soil ratio of 1:5. The cation exchange capacity (CEC) was measured according to the Chinese National Environmental Protection Standard (HJ 889-2017). Soil water content (SWC) was determined by gravimetric method (HJ613-2011).
## 4 Discussion  
This study proposed and implemented a unified geodetic adjustment strategy for approximately 5560 multi-purpose control points (MPCs) established across South Korea between 2008 and 2021. Departing from previous regionally segmented approaches that relied on localized baseline processing and a limited number of fixed reference stations, the present framework employed 17 rigorously validated continuously operating reference stations (CORS), all referenced to the International Terrestrial Reference Frame 2014 (ITRF2014). This shift toward a nationally integrated adjustment paradigm markedly enhanced spatial uniformity, positional accuracy, and cross-temporal comparability within the national geodetic control network.  
The integrated adjustment demonstrated a high degree of internal consistency and coordinate reliability. Mean residuals remained below 1 cm in both east and north directions, and more than 99% of adjusted coordinates fell within \(\pm 3\sigma\) of the modeled normal distribution, affirming the statistical robustness of the results. Compared to previous region-based adjustments, the unified approach successfully reduced positional discontinuities that had been introduced by inconsistent software versions, adjustment strategies, and fixed station selections across administrative boundaries.  
The application of internationally recognized software packages--GAMIT/GLOBK and BERNESE--along with the use of multi-year GNSS observation datasets, enabled the precise modeling of baselines and the generation of repeatable solutions. Rigorous quality control procedures, including antenna and radome verification, receiver metadata validation, and standardization of RINEX file naming and formatting, ensured that systematic errors in input data were minimized throughout the processing pipeline.  
Despite these advancements, several limitations remain. A small number of outliers with coordinate residuals exceeding 20 cm were identified. These anomalies were primarily associated with low-quality observation data, inconsistent or missing metadata, or degraded environmental conditions such as severe multipath or signal obstruction. Furthermore, positional degradation was observed in long-baseline segments, particularly in regions with sparse CORS coverage or complex topographic features. These findings highlight the need for future enhancements in stochastic modeling, including the incorporation of baseline length-dependent weighting schemes, and the adoption of advanced preprocessing techniques to further improve data integrity.  
Overall, the findings of this study support the adoption of a unified, nationally coordinated adjustment strategy for future control point densification, maintenance, and multi-epoch geodetic applications. The proposed framework offers a scalable, accurate, and repeatable methodology that aligns with international geodetic practices and strengthens the long-term stability of Korea's national spatial reference system.
## 3 Results and Discussion
### In Vitro Disintegration Test
#### 3.8.2 Dissolution Time  
Gradual measurements of the release profile of the API incorporated in the polymeric matrix were made from dissolution. The release was evaluated by determining the concentration of the compound over time. To simulate the salivary mucosa, the analysis was conducted in an aqueous medium at pH 6.8. The results indicated that the polymeric film released approximately 73% of the API within 10 min and achieved complete release within 15 min, maintaining a constant proportion after 30 min (See, Figure 9). Given these results, the proposed ODF can be classified as a very fast dissolving dosage form, since, according to the WHO, a drug is considered very fast dissolving when no less than 85% of the labeled amount of the drug has dissolved in 15 min, at pH 6.0 and 7.4 under the established conditions [58]. The fast release time obtained is directly attributed to the polymers used in the preparation of the ODF, since the release rate can be influenced by hydrophilic or hydrophobic characteristics of all the components of the matrix; in addition, the interaction between them can significantly influence the dissolution rate of the active ingredient. Generally, the presence of hydrophilic polymers such as chitosan or starch facilitates rapid dissolution upon contact with saliva, thereby efficiently releasing the active compound [59]. Under these conditions, the film demonstrated a low disintegration time (<60 s), meeting the European Pharmacopoeia (Ph. Eur.) standard [60]. The acceptance criterion for ODF disintegration time is classified as \"fast\", ranging between 30 and 180 s [21]. According to the physicochemical characterization of the TPS-CH-M and TPS-CH-M-A films, the incorporation of API at 0.5% \(w/w\) into the TPS-CH-M filimogenic solution resulted in a film with suitable properties for ODF applications. This film exhibited a density of 1.554 g/mL, and API quantification confirmed that the model drug accounted for approximately 11.75% of the total polymer film mass. Given that a typical ODF measures 3 cm \(\times\) 2 cm, the total mass of the TPS-CH-M film is approximately 37.2 mg, with a total drug load of around 4.37 mg. Therefore, this film could serve as a potential platform for delivering drugs with a similar dosage requirement, such as naloxone, which is used for rapid opioid overdose reversal at a dose of approximately 0.4 mg. Additionally, this system could be adapted for drugs soluble in water, ethanol, or ethanol/water mixtures, such as loreamide, which is used for the treatment of acute diarrhea.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **0** & **5** & **8** & **10** & **15** & **30** \\ \hline TPS–CH–M & & & & & \\ TPS–CH–M & & & & & \\ TPS–CH–M–A & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Picture of films in contact with water at 37 \({}^{\circ}\)C.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
###### Abstract  
Describing the hydraulic conductivity of unsaturated soil is very important in predicting water transport. Most current models have complex forms and generally need to be calibrated by the measured unsaturated hydraulic conductivity curve. A simple model, by which it is possible to conveniently predict the unsaturated hydraulic conductivity, is proposed in this study. The soil-water characteristic curve and hydraulic conductivity curve are separated into three parts. The soil-water characteristic curve is represented by Fredlund and [PERSON]'s equation. A simple model composed of three lines is proposed for estimating the hydraulic conductivity of unsaturated soil. The model parameters can be conveniently calibrated from the measured soil-water characteristic curve and saturated hydraulic conductivity. Finally, the proposed model is validated by the experimental data from different soils. The proposed model provides a simple approach to estimating the hydraulic conductivity of unsaturated soil, which is more convenient for practical application.  
hydraulic conductivity; unsaturated soil; estimation; capillary; adsorption Article
## 2 Materials and Methods
### Clinical, Imaging, and Surgical Parameters  
For preoperative parameters, clinical data consisting of age, initial PSA, biopsy Gleason score (GS), the greatest percentage of biopsy cores, and the number of positive re-cores were collected using an electronic medical record (EMR) search, and MRI images were interpreted by a radiologist with greater than seven years of experience who was unaware of clinical and surgical information. Based on the scoring method of PI-RADSv2, the score was recorded per patient using the five-point scale. The probability of clinically significant prostate cancer in a particular patient according to PI-RADS score was defined as follows: 1, very low (clinically significant cancer is highly unlikely to be present); 2, low (clinically significant cancer is unlikely to be present); 3, intermediate (the presence of clinically significant cancer is equivocal); 4, high (clinically significant cancer is likely to be present); and 5, very high (clinically significant cancer is highly likely to be present). We also evaluated extracapsular extension (ECE) and seminal vesicle invasion (SVI) on MRIs using the following 3-point scale scoring system: 1, definitely negative; 2, probably positive; 3, definitely positive.  
Postoperative pathologic data, consisting of surgical GS, extracapsular extension (ECE), seminal vesicle invasion (SVI), tumor volume, positive surgical margin (PSM), perineural invasion (PNI), and lymphovascular invasion (LVI), were also recorded. Whole-mount step-section findings from the resected prostate gland were utilized for assessing ECE, SVI, and tumor volume.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion
### Implications for Future Research
#### 4.6.1 Development of Hybrid Detection Techniques  
One of the most promising areas for future research is the development of hybrid techniques that combine multiple malware detection approaches, leveraging the strengths of each. Currently, research tends to focus on individual techniques (machine learning, deep learning, static, or dynamic analysis), but a more holistic combination of these techniques could provide greater adaptability to new threats. The integration of static and dynamic analysis with machine learning techniques would allow capturing both the static characteristics of malware code and its dynamic behavior in real-time, offering a more robust and comprehensive approach to detecting evasive malware.  
For instance, static analysis techniques are fast and efficient at detecting known malware signatures but lack the ability to detect new variants or malware that employs obfuscation techniques. In this sense, dynamic analysis can complement static analysis by executing the malware in controlled environments to observe its real behavior, capturing malicious changes during runtime that might otherwise go unnoticed. The combination of these methods with machine learning and deep learning models, which learn more complex and hidden patterns, could dramatically improve the ability to detect advanced threats like zero-day malware or advanced persistent threats (APT).  
Hybrid techniques could also include the incorporation of deep neural network models alongside more traditional approaches like computer vision and behavioral analysis to create systems capable of identifying and preventing malware in a wide range of environ ments. These approaches enable security systems not only to detect known attacks but also to anticipate malicious behavior patterns not yet seen in the field.
# Linear Plasma Device for the Study of Plasma-Surface Interactions
## 3 Experimental Procedure  
Before irradiation, tungsten samples of 99.95% purity in the form of cylinders with a diameter of 6.3 mm and a height of 5 mm were ground and polished. During irradiation of tungsten with helium plasma, the pressure in the interaction chamber was 5\(\cdot\)10\({}^{-4}\) Torr. The tungsten samples were exposed to helium plasma at T = 500 \({}^{\circ}\)C, T = 900 \({}^{\circ}\)C, and T = 1300 \({}^{\circ}\)C. The duration of irradiation was 1 h.  
The characteristics of the microstructure were determined using a scanning electron microscope (SEM) JSM-6390 (eol, Tokyo, Japan) combined with an energy-dispersive spectrometer (EDS) Oxford INCAEnergy (Oxford Instrument, Abingdon, UK). X-ray structural analysis was performed using the X'PertPRO X-ray diffractometer (Almelo, Netherlands) with Cu K\(\alpha\) radiation. The diffractometer was calibrated using the Si standard. Surface roughness was measured using the Precise Roughness Tester HY 230 profilometer. Surface erosion after irradiation was assessed by weighing the samples before and after irradiation using analytical scales designated VL-224V. To determine hardness and modulus of elasticity during indentation, a microhardness tester (Fischerscope HM2000 by Helmut Fischer GmbH, Sindelfingen, Germany) was used. This instrument performs instrumental indentation testing following DIN EN ISO 14577-1. The loading rate was 2 \(\mu\)m/s, and the applied load was 1.96 N. The results of the tests were initially processed using the WIN-HCU 7.1 instrument software. A Vickers diamond pyramid indenter with an angle of 136\({}^{\circ}\) was used.