esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Freeze-Thaw and Wet-Dry Cycles on Tension Stiffening Behavior of Reinforced RAC Elements
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Raw Materials  
Coarse Recycled Concrete Aggregate (RCA) were produced from two different sources (more details in [PERSON] et al. [23]): one derives from a laboratory-produced concrete waste, labeled \"L-waste\", and the other comes from a concrete waste provided by a demolition waste recycling plant, labeled \"D-waste\". The residues were subjected to the same processing steps (crushing, mechanical sieving, homogenization and drying) for the production of the RCAs, labeled \"RCA_L_C1\" and \"RCA_D_C1\". As Natural Coarse Aggregate (NCA), granite-type rocks were used in two sizes, named \"NCA_C0\" and \"NCA_C1\". As Natural Fine Aggregate (NFA), natural quartz sand was used. Table 1 presents the characterization of the aggregates. Meanwhile, Figure 1 presents the grain size distribution of natural and recycled aggregates. The data summarized in Table 1 show that the recycled particles are characterized by a significant higher water absorption capacity and lower density in comparison with the companion (i.e., same class size) natural aggregates while the grain size distribution is quite similar (see Figure 1).  
As for cement, an \"high initial strength Portland cement\" (labeled CPV-ARI, according to the National Brazilian Standard [27]) with specific gravity of 3181 kg/m\({}^{3}\) and 28-day compressive strength of 40.6 MPa was used. As additive, a polycarbonate superplasticizer (labeled MC Powerflow 1180) with specific gravity of 1070 kg/m\({}^{3}\), solid concentration content of 35% and saturation dosage of 1.5% (solids of SP based on the cement weight) was used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Properties** & **NFA** & **NCA\_C0** & **NCA\_C1** & **RCA\_L\_C1** & **RCA\_D\_C1** \\ \hline D\({}_{\text{max}}\) (mm) & 4.75 & 9.5 & 19.0 & 19.0 & 19.0 \\ Specific gravity (kg/m\({}^{3}\)) [24; 25] & 2447 & 2662 & 2636 & 2105 & 2255 \\ Water absorption (\%) [24; 25] & 0.5 & 1.5 & 1.3 & 8.2 & 6.1 \\ Attached Mortar—V\({}_{\text{AM}}\) (\%) [23] & - & - & - & 64.8 & 35.1 \\ Abrasion wear (\%) [26] & - & 39.5 & 36.1 & 46.7 & 46.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the natural and recycled aggregates used ([PERSON] et al. [23]).
## 2 Discrete Element Parameter Model and Parameter Calibration of Soil-Soil Interaction
### Simulation Test
#### 2.5.2 The Steepest Climb Test Determined the Optimal Range of Significance Parameters  
Based on the experimental results from the previous Plackett-Burman Design, the steepest ascent experiment incrementally increased only the three significant parameters (G, A, E) with a selected step size, while the non-significant parameters (B, C, D, F) were tested at low levels. The relative error between the simulated soil pile angle and the actual pile angle was calculated. The experimental plan and results are presented in Table 6.  
Figure 4: Pareto chart of Plackett–Burman Design.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Degree of Freedom** & **Sum of Squares** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline A & 1 & 12.59 & 12.59 & 19.20 & 0.0119 \\ B & 1 & 0.0631 & 0.0631 & 0.0962 & 0.7719 \\ C & 1 & 0.0290 & 0.0290 & 0.0442 & 0.8437 \\ D & 1 & 0.0631 & 0.0631 & 0.0962 & 0.7719 \\ E & 1 & 11.51 & 11.51 & 17.55 & 0.0138 \\ F & 1 & 0.8164 & 0.8164 & 1.25 & 0.3270 \\ G & 1 & 197.56 & 197.56 & 301.34 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA of Plackett–Burman Design test result.  
From Table 6, it can be observed that, as the JKR surface energy (G) of the soil, the restitution coefficient among soil particles (A), and the static friction coefficient between soil and steel (E) increase, the simulated soil particle pile angle steadily increases. The relative error between the simulated pile angle and the actual pile angle initially decreases and then increases. The second set of simulation experiments has the smallest relative error, indicating that the optimal value range for the three significant parameters is near the levels selected in the second set of experiments. Therefore, the levels chosen in the first, second, and third sets of experiments were used for the Box-Behnken Design response surface analysis experiments to find the optimal parameter combination.
## 2 Fluid-Structure Interaction Analysis of SFT under Waves  
### Governing Equation and Fluid Force of a Submerged Line Element  
The governing differential equation of a pipe and a tendon for SFT-acting static loads, such as self-weight, buoyancy, and tidal current, was determined, and the dynamic load of waves can be constructed through DNV-RP-C205 [13].  
The hydrodynamic force on a slender beam in a general fluid flow can be determined by adding up the sectional forces acting on each point of the beam structure as shown Figure 4. Generally, the force vector acting on a beam can be resolved into a normal force \(f_{N}\), a tangential force \(f_{T}\) and a loft force \(f_{L}\), which is the same as both \(f_{N}\) and \(f_{T}\). Additionally, a torsion moment \(m_{T}\) will occur on non-circular cross-sections.  
For slender structural beam members with a cross-sectional dimensions and insufficient planarity to allow the inclination of fluid velocities and the accelerations of particles in the normal flow direction to the beam member, to be neglected, the wave loads can be estimated using [PERSON]'s load formula, which is the sum of an inertia force, proportional to the acceleration, and a drag force, which is proportional to the square of the velocity.  
Normally, [PERSON]'s load formula is used when the following condition is satisfied by Equation (1):  
\[\lambda>5D \tag{1}\]  
where \(\lambda\) is the wavelength, and \(D\) is the diameter. When the beam member's length is larger than the cross dimensions, the end-effects can be neglected, and the total force can be taken as the sum of forces on each cross-section area, according to the length of the beam. As for the wave and flow conditions, they induced particle velocities, which should be supplemented with vector quantities.  
The sectional force \(f_{N}\) on a fixed slender beam in a normal two-dimensional flow to the member axis is given by:  
\[f_{N}(t)\ =\ \rho(1+C_{A})A\dot{v}+\frac{1}{2}\rho C_{D}Dv|v| \tag{2}\]  
\(v\) = the fluid particle (waves and/or current) velocity (m/s), \(\dot{v}\) = the fluid particle acceleration (m/s\({}^{2}\) ), \(A\) = the cross sectional area (m\({}^{2}\)), \(D\) = the diameter or typical cross-sectional dimension (m), \(\rho\) = the mass density of the fluid (kg/m\({}^{2}\) ), \(C_{A}\) = the added mass coefficient (with a cross-sectional area as a reference area), and \(C_{D}\) = the drag coefficient.  
The drag coefficient \(C_{D}\) is the non-dimensional drag force:  
\[C_{D}\ =\ \frac{f_{drag}}{\frac{1}{2}\rho Dv^{2}} \tag{3}\]  
where \(f_{drag}\) = the sectional drag force (N/m).  
In general, the fluid velocity vector will be in a direction, relative to the axis of the slender member. The drag force \(f_{drag}\) is decomposed with a normal force \(f_{N}\) and a tangential force \(f_{T}\).  
\[C_{A}\ =\ \frac{m_{a}}{\rho A} \tag{4}\]  
The mass coefficient is defined as:  
\[C_{m} =1+C_{A} \tag{5}\]
# Turbulent Drag Reduction Characteristics of Bionic Nonsmooth Surfaces with Jets
## 2 Physical Model Establishment and Hypothesis Verification  
### Model of A Bionic Nonsmooth Surface  
Due to the excellent aerodynamic characteristic, a sand dune was selected as the bionic object of the nonsmooth unit cell (Figure 1).  
[PERSON] et al. [40] studied the sand dune located on the western edge of the Erg Oriental near Merzouga in Eastern Morocco (31 deg03'N, 04 deg01'W), where the dune was an elongated dome with no avalanche slip face, showing that no flow separation occurred. According to the measured results, the cross-section of the dune can be expressed by the following Equation:  
\[Z(x)=Z_{ref}+A\cos(kx) \tag{1}\]  
Figure 1: Morphology of the sand dune.  
where \(Z_{ref}\) is the starting point of the dune, \(A\) is the half-height of the dune, and \(k\) is the number of waves (the wavelength \(\lambda=2\pi/k\)). According to Equation (1), the shape equation of the sand dune involves two parameters: The half-height (A) and the wavelength (\(\lambda\)) (Figure 2).  
Preliminary research has found that turbulent drag is most influenced by the near-wall turbulent region of the boundary layer, defined as \(y/\delta\leq 0.2\), where \(y\) is the normal distance between the fluid and the wall and \(\delta\) is the thickness of the boundary layer [41]. By disturbing the near-wall turbulent region with a nonsmooth unit cell, a new turbulent boundary layer will be generated. It is suggested that the depth of the nonsmooth unit cell (\(2A\)) should be smaller than the thickness of the near-wall turbulent region to obtain the best disturbance effect, which can be expressed as follows:  
\[2A/\delta\leq 0.2 \tag{2}\]  
As a result, to satisfy Equation (2), the depth of the nonsmooth unit cell (\(2A\)) was defined as 0.0015 m. The wave number was set to 2000; thus, the wavelength (\(\lambda\)) was 0.0031 m. To ensure the stability of the fluid, a flat surface was set behind the nonsmooth unit cell, and the length (d) was set to 0.005 m. The cross-section of the bionic nonsmooth surface is shown in Figure 3.
## Materials and Methods
### Instruments  
The experiment was conducted in a room where the CAVE type system is located. Three DLP Christie Mirage S+4K projectors with a resolution of 1400 \(\times\) 1050 pixels are placed side by side generate an 8-m-wide scene on a 9-m projection screen. They are capable of 3D stereoscopic projection, which was used with a frame rate of 60 fps. Participants, wearing 3D glasses were placed on the opposite side of the screen. The computational effort was distributed through 4 Dell Precision R7610 rack workstations, equipped with Nvidia Quadro K5000 graphics card and Intel Xeon E5-2600 processors. Three of them were used to render the projection itself, while the fourth was used for monitoring. BlenderVR  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Speed Pattern**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Constant**} & \multicolumn{2}{c}{**Slow Down**} & \multicolumn{2}{c}{**Stop**} \\ \cline{2-7}
**Indicator 1** & **Gasoline** & **Electric** & **Gasoline** & **Electric** & **Gasoline** & **Electric** \\  & **Engine** & **Engine** & **Engine** & **Engine** & **Engine** & **Engine** \\ \hline LAmax (dB(A)) & 77.49 & 74.87 & 66.15 & 60.6 & 64.28 & 61.45 \\ L5 (dB(A)) & 76.95 & 74.26 & 65.54 & 60.32 & 63.8 & 61.11 \\ L10 (dB(A)) & 76.55 & 73.57 & 64.81 & 60.11 & 63.57 & 60.99 \\ L50 (dB(A)) & 67.98 & 65.75 & 61.60 & 51.83 & 60.22 & 59.24 \\ L90 (dB(A)) & 63.23 & 60.61 & 57.94 & 47.44 & 56.26 & 51.31 \\ Measurement time (s) & 3.90 & 4.05 & 7.20 & 8.55 & 5.40 & 5.10 \\ Dynamic range (dB(A)) & 32.83 & 30.21 & 21.50 & 15.94 & 19.62 & 16.79 \\ \hline \hline \end{tabular}  
* Values of the acoustic indicators for the sound acquired by the HATS’s left channel (left ear).  
\end{table}
Table 3: Acoustic characteristics of the stimuli regarding the electric and gasoline combustion vehicles.  
Figure 2: Visual models of the vehicle used in the experiment: (**a**) Kia Ceed SW; and (**b**) Renault Zoe ZE.  
software [30] was used to control the simulation, paired with a VICON motion capture system. VICON reflective tracking points were placed on a set of headphones worn by participants. By knowing the position of the participants, it was possible to adjust the visual scene to their perspective, increasing the feeling of presence. The room was kept dark throughout the experiments, with the exception of the projection and the VICON's infrared lights. Participants were placed at 2 m from the screen, rotated so that their sagittal plane formed a 35\({}^{\circ}\) angle with the screen. The projection viewpoint was such that the participant was placed on the sidewalk of the virtual scenario, facing the road. CPB sounds were played synchronously with the corresponding visual stimuli on the headphones, using VLC media player. The sound was amplified through a Sony TA-AV570 Audio Video Amplifier. Acoustic levels were calibrated to ensure they were equal to the ones registered during the recording sessions.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 4 Discussion
This research validates the complementary roles of UL-SRS and DL-PRS, offering critical insights for optimizing positioning strategies in diverse 5G deployment scenarios.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Application  
Among the selected pivots (Figure 1), one pivot (highlighted in black) was used for the application of the trained models. This pivot was chosen due to having the largest number of Sentinel-2 images during the crop cycle. It had images from before sugarcane budding to harvest. The application consisted in quantifying the crop coefficient (K\({}_{\text{c}}\)) and the actual evapotranspiration (ET\({}_{\text{a}}\)) during the sugarcane crop cycle for the best METRIC model. In addition to K\({}_{\text{c}}\), water consumption during the sugar can crop cycle also was estimated using 29 images of the MSI sensor from 11/23/2019 (DAE-days after emergency 05) to 09/03/2020 (DAE 290) and 17 images of the OLI and TRIS sensor between 12/07/2019 (DAE 19) and 09/04/2020 (DAE 291).  
From the 29 images of the MSI sensor, the model with the best statistical results on approaches 1 and 2 was applied to determine the ET\({}_{\text{r}}F\). According to [PERSON] [7], the ET\({}_{\text{r}}F\) is equivalent to the K\({}_{\text{c}}\) when using the ET\({}_{\text{r}}\) of the alfalfa, which is the condition in which the ET\({}_{\text{r}}F\) was modeled. The 17 images of the OLI and TIRS sensor were used to calculate K\({}_{\text{c}}\) through the METRIC model. After quantifying the K\({}_{\text{c}}\), the crop's actual evapotranspiration was established. Therefore, the accumulated reference evapotranspiration for a 24 h (ET\({}_{\text{r-24}}\)) period was calculated on the same day the image was acquired.  
The ET\({}_{\text{r-24}}\) in each day was calculated using the Pemman-Monteith equation standardized by the ASCE (American Society of Civil Engineers) [32], and meteorological data were acquired in the A539 station, Mocambinho from the INMET (National Institute of Meteorology). With all the ET\({}_{\text{r-24}}\) and K\({}_{\text{c}}\), the ET\({}_{\text{a}}\) in each pixel was established by Equation (14).  
\[\text{ETa}=\text{Kc}\;\times\;\text{ET}_{\text{r-24}} \tag{14}\]  
Finally, the sugar can crop total evapotranspiration was determined during the cycle through METRIC and approaches 1 and 2 applying integral function through time, where x was the dates referring to images, and y was the ET\({}_{\text{a}}\) of each date. Thus, the area under the curve, which corresponds to the total evapotranspiration during the cycle, was calculated. This process was performed using function auc from the MESS package [33] for the R language.
###### Abstract  
The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshtastic's dynamic mesh networking capabilities and LoRa's low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices' measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.  
Internet of Things; security; reputation; LoRa; Meshtastic; LoWPAN; simulation Article
# Study of the Load-Bearing Characteristics of Bolts under Asymmetric Freezing Conditions
## 3 Results and Analyses
### Study on the Bearing Characteristics of Bolts in a Cold Environment
#### 3.2.3 Effects of Freezing and Swelling on the Extraction Force
The extremes of the pull-out force of the frozen bolt at different water content show an exponential growth pattern with the decrease of freezing temperature (as shown in Figure 16). The peak pull-out force grows faster in the early stage, then slows down and tends to a stable point in the later stage. This is due to the fact that as the freezing temperature decreases, the water migration and ice formation process is gradually completed, and the pull-out resistance has no more growth potential.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 4 Minimum Cross-Entropy by EO for MRIs
### Initialization  
In a population made up of a number of N particles, each particle consists of \(k\) dimensions that are initialized randomly within the boundaries of gray levels of the image as follows:  
\[th_{i}=L_{min}+rand_{i}(0,1)\times(L_{max}-L_{min}) \tag{21}\]  
where \(L_{min}\) and \(L_{min}\) are the minimum and maximum gray level values in the image histogram (0 and 255, respectively) and \(rand(0,1)\) is a random number in the range [0, 1].
# The Role of Surveillance Imaging in Patients with Advanced Laryngeal Cancer
## 3 Results
### Modality of Recurrence Identification  
The modality for the identification of the recurrence was patients' symptoms in 14 cases (40%), findings on physical examination in seven cases (20%), and findings on imaging for 14 cases (40%).  
The 14 cases identified by imaging included PET-CT in 11 cases (78.6%) and CT in three cases (21.4%).  
The mean time interval for the diagnosis of recurrence was significantly shorter among patients whose recurrence was identified by imaging compared with those diagnosed by other means (23.8 vs. 45.9 months, \(p\)-value = 0.016). This difference, however, did not translate into a significant difference in 5-year overall and disease-specific survival rate (20.6% vs. 26.6%, log-rank \(p\)-value = 0.53, 62.1% vs. 59.7%, log-rank \(p\)-value = 0.87) [Figure 3A,B].  
We performed sub-group analysis of patients who were treated with radiotherapy prior to recurrence (N = 31). Diagnosis of recurrence with imaging among these patients was associated with significantly shorter mean time to diagnosis compared with other modalities (23.8 vs. 38.8 months, \(p\) = 0.037). This diagnostic benefit did not translate into better DSS of OS among these patients (log-rank \(p\)-value = 0.8, log-rank \(p\)-value = 0.84, respectively).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **Number of Patients** \\  & **(\%)** \\ \hline TNM stage of primary tumor & \\  3 & 16 (45.7) \\  4 & 19 (54.3) \\ \hline Recurrence location & \\   Local & 22 (62.9) \\   Regional & 13 (37.1) \\   Distant & 9 (25.7) \\ \hline Diagnostic modality of recurrence & \\   Medical history & 14 (40) \\   Physical examination & 7 (20) \\   Imaging & 14 (40) \\ \hline Treatment modality & \\   Surgery & 18 (51.4) \\   Radiotherapy + chemotherapy & 6 (17.1) \\   Palliative treatment & 6 (17.1) \\   Unknown & 5 (14.3) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._We aimed to assess whether imaging was associated with earlier diagnosis and better prognosis among patients with distant recurrence. The mean time to recurrence diagnosis  
Figure 3: Overall (**A**) and disease-specific (**B**) survival from diagnosis of recurrence by diagnostic modality.  
was 45.2 months among the patients whose distant recurrence was diagnosed by imaging (N = 4) compared with 67.8 months among those diagnosed by other modalities (N = 5). This difference did not reach statistical significance (\(p\) = 0.56). When comparing one-year OS, no difference was demonstrated between the imaging-diagnosed patients and other modalities (33.3% vs. 0%, log-rank \(p\)-value = 0.27) [Figure 4].  
Regarding treatment for recurrence, we found no difference in the treatment between the different diagnostic modalities, as the rate of patients treated by either surgery, definitive radiotherapy with or without chemotherapy, and with palliative intent was similar between the different diagnostic modalities sub-groups (\(p\)-value = 0.83).
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Tree
#### 5.12.3 AND-OR Graph  
An _AND-OR Graph_ is a directed tree which utilizes some basic logic, including conjunction (and) and disjunction (or). AND/OR graph can provide a simple and visual understanding of a system, while it helps us to avoid unnecessary enumerating every alternative route of a given job in a scheduling problem [148; 149].  
AND/OR trees are typically made for each job, so nodes represent operations and directed edges present the predetermined route of a given job [150]. Logic is implemented, dictating whether the subsequent paths are all required (conjunction, represented by AND node) or either path is required (disjunction, represented by OR node) [148]. There are other methods for implementing logic, such as manipulating edges, which are also satisfactory.  
One of the particular applications of AND-OR tree is to those problems where the shop scheduling problem is in a dynamic environment with alternative routes, such as flexible job shop [151], or with some kind of joining final assembly operation, such as flexible assembly job shop scheduling problem [148]. In such systems, each final product may be assembled from several subassemblies, and each subassembly may be assembled from several types of parts. Each part might go through linear or nonlinear process plans with operations on machines, as shown in an example in Figure 19.
## 4 Results
### System Performance (Latency Overhead)  
The performance overhead introduced by the AVI PoC layer was measured across 50 representative queries. The end-to-end latency of requests routed through AVI was compared to direct calls to the Grok-2-1212 API. The average additional latency (L_AVI) introduced by the AVI processing pipeline (excluding LLM inference time) was found to be approximately 85 ms when RAG was disabled. When the RAG module was activated for context retrieval from the ChromaDB vector store, the average overhead increased to approximately 450 ms (Table 9).  
This indicates that the core validation logic imposes a relatively low latency penalty, while the RAG process, as expected, adds a more significant delay dependent on database search time. These latency figures are considered acceptable for many interactive applications, although further optimization would be necessary for high-throughput scenarios [56].