esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Mechanical Properties of Stabilized Aeolian Sand  
The elastic modulus \(E\) and ductility coefficient _DI_, derived from stress-strain curves, were employed to quantify the specimen's deformation resistance during the elastic phase and post-failure ductility. Figure 6 illustrates the variations in E and _DI_ with _UCS_ for specimens in SLM experiments. The results reveal a positive correlation between the strength of stabilized aeolian sand and its elastic modulus. Specimens with higher _UCS_ values exhibited a rapid increase in deformation resistance following the compaction phase, typically accompanied by pronounced shear-induced cracks at failure. This enhancement is attributable to two synergistic mechanisms: (1) the abundance of cementitious compounds reinforcing interparticle bonds and (2) the presence of high-hardness crystalline phases (e.g., ettringite), which fill intergranular voids and inhibit particle displacement. These microstructural features collectively improve the cohesion and friction angle along shear planes.  
Conversely, ductility inversely correlated with strength. Specimens with lower _UCS_ displayed broader stress-strain curve peaks, characterized predominantly by axial splitting fractures parallel to the specimen axis. This behavior arises from two factors: (1) reduced cumulative strain energy under lower loads, resulting in gradual post-peak stress dissipation and (2) diminished hydration products, leading to weaker particle bonding and more micropores (a critical contributor to splitting failure modes). After specimen failure, the partial closure of these micropores generates residual strength, thereby decelerating the descending phase of stress-strain curves.  
The quantitative relationships between _UCS_ and the mechanical parameters can be mathematically expressed by linear and exponential functions, respectively, as shown in Figure 6.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 1 Introduction
When a nearby vehicle cuts into the lane, by considering the state change among the cut-in vehicle, the host vehicle and the preceding vehicle in front of it in the lane, a safety distance model of the cut-in vehicle was established, which is used to manage the transition of the FSM. In the end, considering the constraints of driving safety, comfort, and taking the possibility of cut-in of a nearby vehicle as a reference, an ACC control strategy based on MPC (MPC-ACC) was developed. Through the control strategy proposed in this paper, the host vehicle can judge and recognize a cut-in vehicle, and, at the same time, for a cut-in vehicle in different states, the host vehicle is controlled in coordination with the MPC-ACC to achieve safe and smooth processes in cut-in scenarios. The contributions of this paper are the following aspects:  
(1) A finite state machine is used to divide the cut-in scenarios, and a method of screening and quantifying potential cut-in vehicles is proposed, which can be used as a reference for vehicle control to cooperate with a cut-in vehicle. (2) The safety distance model of the cut-in vehicle is proposed. Comprehensively considering the movement state of the vehicle, the preceding vehicle ahead of the host in the lane as well as the cut-in vehicle, the safety distance model of the cut-in vehicle is established, which performs the conversion of the management finite state machine so that vehicle control can be taken appropriately when a cut-in vehicle changes lanes. (3) Facing the cut-in vehicles in different states, the MPC-ACC control strategy is developed to realize the follow-up control for cut-in vehicles. At the same time, driving safety and comfort are taken as the constraints of the MPC-ACC to make the host vehicle follow an optimized control trajectory. Figure 1: MPC–ACC control strategy architecture for cut-in scenarios. The rest of this paper is as organized follows. Cut-in vehicle identification and quantification are shown in Section 2. Section 3 describes the cut-in vehicle safety distance model. Section 4 introduces the MPC-ACC control strategy model. In Section 5, the simulation result research is given. The conclusions are presented in Section 6.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods
### Satellite Imagery Datasets  
#### 2.2.1 PlanetScope CubeSat Surface-Reflectance Ortho Tiles  
We used the OFRs' shapefile to search for and clip Level 3A surface-reflectance imagery available through Planet Orders API. The PlanetScope surface-reflectance ortho tiles use a fixed UTM grid system in 25 km by 25 km tiles with 1 km overlap [1]. We filtered out all images with more than 10% cloud using an image-based cloud-cover filter--this cloud-cover filter threshold allowed us to download mostly cloud-free images; however, because it is an image-based filter rather than an OFR or area-of-interest-based cloud filter, some useful observations (i.e., when the OFR is not covered with clouds but the image is filtered out) were not downloaded, decreasing the total number of observations per OFR. In addition, to deal with potential cloud-obscuration outliers, we used the PlanetScope  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Pixel Size (m)** & **Blue (\(\mu\)m)** & **Green (\(\mu\)m)** & **Red (\(\mu\)m)** & **NIR (\(\mu\)m)** \\ \hline PlanetScope & 3 & 0.455–0.515 & 0.500–0.590 & 0.590–0.670 & 0.780–0.860 \\ \hline Basemap & 3 & 0.450–0.510 & 0.530–0.590 & 0.640–0.670 & 0.850–0.860 \\ \hline Planet Fusion & 3 & 0.450–0.510 & 0.530–0.590 & 0.640–0.670 & 0.850–0.880 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PlanetScope, Basemap, and Planet Fusion image spatial resolutions and different wavelengths bands.  
Figure 2: Workflow used to estimate the OFRs’ surface area-time series from PlanetScope, Basemap, and Planet Fusion between July 2020 and July 2021.  
UDM2 to filter out all image clips that contained more than 5% unusable pixels (i.e., pixels covered by clouds, cloud shadow, with light and heavy haze).  
The PlanetScope ortho tiles were resampled to 3 m and projected using the WGS84 datum. The ortho tiles were radiometrically, sensor, and geometrically corrected and aligned to a cartographic map projection. These images were atmospherically corrected using the 6S radiative transfer model with ancillary data from MODIS [1, 38, 39], and the positional accuracy has been reported to be smaller than 10 m [1].
## 3 Results and Discussion
The comparison of the results obtained from the analysis of the three batches showed no intrabatch nor interbatch variability of the physicochemical properties or of the background microbiota, confirming that the production process of the studied salami is fully standardized, differing from what was reported by other studies of analogous products [30]. This outcome also confirms the importance of considering multiple batches and various replicates per batch in order to establish the inherent variability in physicochemical characteristics and growth potential linked to the product and its processing/storage conditions, as recommended by Annex II on challenge testing of EC Regulation 2073/2005 [28]. As mentioned above, in the present study, the contamination was performed on meat dough, and the evaluation on the pathogen evolution and the definition of the growth potential (\(\delta\)) was performed during both the production process and the shelf life. The behavior of _L. monocytogenes_ during spreadable salami ripening and storage time, considering the three batches of LM samples, is shown in Figure 1; the pathogen was undetectable in control samples. _L. monocytogenes_ artificially inoculated in salami was able to survive throughout the ripening time (2.43 \(\pm\) 0.19 Log CFU/g at 10 days) while a decrease in the pathogen concentration was observed during storage (1.07 \(\pm\) 0.11 Log CFU/g at 70 days; Figure 1). At the end of the storage period (day 70) for batch 1 the _L. monocytogenes_ was undetectable (\(<\)10 CFU/g) (data not shown). Concerning \(\delta\) definition, as reported in Table 2A, at day 0 (beginning of production process) the median concentrations of _L. monocytogenes_ were 2.15 Log CFU/g, 2.23 Log CFU/g and 2.10 Log  
Figure 1: Growth profile of _Listeria monocytogenes_ (Log CFU/g) during spreadable salami ripening and storage (data represent the average values \(\pm\) standard deviation of three replicates samples for three LM batches). Different superscript letters (a, b, c) indicate differences between _L. monocytogenes_ counts (_p_\(<\) 0.05). CFU/g for batch 1, 2 and 3, respectively. The calculated growth potential of _L. monocytogenes_ for the targeted spreadable salami was 0.13 Log CFU /g in batch 1 and 0.40 Log CFU /g in batch 2 and batch 3. The highest \(\delta\) value obtained is retained among all tested batches, therefore, for the production process the \(\delta\) is 0.40 Log CFU/g [31]. The calculated \(\delta\) is \(\leq\) 0.5 Log CFU/g, therefore, according to reference guidelines, it is assumed that during this time frame the salami is not able to support the growth of _L. monocytogenes_[31]. This evidence corroborates the results previously reported for the pathogen behavior (Figure 1); indeed, no increase in _L. monocytogenes_ loads was observed at any time during the process, whereas, a decreasing trend of the pathogen concentration was detected during the storage time (from day 10 to day 70).
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.8 Replay Attack  
The replay attack comprises the following: the adversary usually sends old messages to pass the verification of entities and re-computes the session key. However, in each session, \(U_{i}\), \(GWN\), and \(MS_{j}\) choose random numbers \(r\), \(r_{u}\), \(r_{g}\), and \(r_{s}\), respectively, to ensure the freshness and independence of exchanged messages. As a result, the adversary can neither calculate the correct session key based on the replayed message nor can they pass the authentication of \(U_{i}\).
## 3 System Model and Architecture Design
### Modeling Energy Consumption  
In 5G D2D networks, the energy consumption depends on several aspects, including the hardware, system architecture, tasks performed by the device, such as signal processing and coding, and radio frequency (RF) used. It also depends on the number of cells and access points that are in the geographical area. Due to their propagation aspects, the network architecture and location of the antenna also play key roles in the energy consumption. Let ET be the total energy efficiency. Therefore, it is computed as follows:  
\[\text{ET}=\text{TP}+FP+PC+SP+A \tag{5}\]  
where TP means the transmitter power with amplifier inefficiency, \(FP\) means the fixed circuit power, and \(PC\) indicates the power per transceiver chain. The last variable \(A\) symbolizes the coding/decoding/backhaul [32]. We have to note that the communications between the Base Station (BS) and beekeeper equipment are made up of regular communication, while the communication between the devices in SBD2D act as relay nodes of 5G networks.
## 5 Results and Analysis
### Case Analysis Using the Difference-Based Model  
Damage distribution maps generated by the difference-based model can support the prioritization of emergency road repairs in disaster-affected areas. Figure 4 shows an example of damage visualization for a specific urban area, enabling the identification of potentially isolated zones at risk of delayed access. While the model's quantified recall  
Figure 1: Road damage detection result of overlay approach.  
Figure 3: Road damage detection result of Siamese-based approach.  
Figure 2: Road damage detection result of difference-based approach.  
of 0.445 \(\pm\) 0.051 indicates that a significant portion of damage may be missed, these maps provide a critical \"first-pass\" assessment that is substantially more rapid and scalable than manual methods.  
Such outputs can be leveraged as quantitative and automated resources for disaster response planning, including traffic control, rescue route optimization, and infrastructure inspection prioritization. Compared to manual assessment methods, the proposed approach substantially improves both the speed and accuracy of field response.  
In conclusion, while acknowledging its quantified limitations, the difference-based road damage assessment model demonstrates the most practical and effective performance among the evaluated change detection strategies.
# Study on Band Gap and Sound Insulation Characteristics of an Adjustable Helmholtz Resonator
## 5 Conclusions  
In order to solve the problem of low-frequency noise in the environment and realize the active control of the noise of different frequencies, the periodic structure of Helmholtz resonator with adjustable cavity and double labyrinth tubes was proposed in the paper. This design optimized the length of tube and structure of cavity, which were two of the most important factors for band gap and sound insulation characteristics of the periodic structure. The results show that the structure generates two complete band gaps in the range of 0-500 Hz (39.27-86.18 Hz and 133.12-209.44 Hz) and two sound isolation peaks (50 dB and 100 dB) in the frequency range of band gaps. The phononic crystal shows excellent low-frequency sound insulation characteristics and can be adjusted according to the noise environment to achieve active sound insulation effect in specific frequency ranges. At the same time, the influence of the structural parameters of the periodic structure on the characteristics of the low-frequency band gap was discussed. It can be concluded that multiple low-frequency band gaps can be obtained from the two cavities if properly designed. Meanwhile, multiple resonant band gaps can be connected to form a wide band gap by adjusting the layout and size of cavity, so as to achieve the purpose of widening the band gap. In addition, in the periodic arrangement design of the structure, keeping the spacing of cells small and reasonable use of local resonance double coupling mechanism will greatly widen the band gap range and achieve the purpose of sound isolation in a larger frequency range. Results show that the design provides ideas and theoretical support to solve the problem of broadband vibration isolation. The purpose of theoretical research is to solve problems in real life, but the engineering application of acoustic metamaterials also faces great challenges. Exploring structure design in acoustic metamaterials can effectively guide the engineering practice of acoustic metamaterials, providing a new idea for low frequency noise control and active vibration isolation and noise reduction.  
Conceptualization, D.H. and G.Z.; methodology, H.Y.; software, D.H.; validation, D.H., J.Z., G.Z., H.Y. and H.L.; formal analysis, J.Z.; investigation, H.L.; resources, G.Z.; data curation, J.Z.; writing--original draft preparation, D.H.; writing--review and editing, J.Z.; visualization, H.L.; supervision, H.L.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11504429.  
The study did not involve humans or animals.  
The study did not involve humans.  
The data used to support the findings of this study are available from the corresponding authors upon request.  
The authors declare no conflict of interest.
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
## 1 Introduction
In this contribution, we will discuss the advantages and drawbacks of dual-frequency and fine-tuning operations using switched resonators implemented by RF MEMS technology. Electromagnetic high-frequency simulations are performed using the Cadence AWR Microwave Office package V22.1, using the Axien(r) solver (Cadence Design Systems, Dublin, Ireland). The paper is structured by briefly describing the SPDT switch in Section 2, which allows for signal routing through the paths where resonators are hosted, and the signal filtering is performed. Sections 3 and 4 describe, in parallel, the operation of the triangular- (Sierpinski) and U-shaped resonators, respectively. A conclusion about the presented work is then elaborated in Section 5.
## 3 Application in Age-Development fMRI Data
### \(f\) and Estimated \(\mathcal{E}\)  
To extract region-of-interest (RoI) signals from the voxel-wise fMRI data, we utilized the MSDL brain atlas [25], which provides an intermediate parcellation scheme consisting of 39 nodes. This atlas allowed us to define distinct brain regions of interest. By mapping the fMRI data onto this parcellation, we obtained subject-level graph signal matrices (\(f\)) for all 155 participants. Each matrix had a size of [39; 149], where the first dimension represented the spatial resolution (39 RoIs) and the second dimension represented the temporal resolution (149 time intervals).  
To estimate the functional connectome, we computed the Pearson's correlation coefficient between each pair of time series \(\mathcal{V}_{t}\) for the 155 subjects. This resulted in 155 subject-level connectivity matrices (\(\mathcal{E}\)) with dimensions of [39; 39], where each element represents the correlation strength between two corresponding brain regions (nodes) in the MSDL brain atlas. These estimated connectivity matrices can be interpreted as static functional connectomes (sFCs) and serve as a representation of the functional connectivity patterns for each subject. These sFCs are used for comparative analyses in our study, allowing us to evaluate the performance of the proposed smoothness harmonics against conventional static functional connectivity measures.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.23. Stream  
Stream [77] reduces the downloaded program image size, compared to _Deluge_, by separating the image into two parts: the reprogramming protocol and the application. The sensor node must support booting from multiple images. The reprogramming image is preloaded as a separate image from the application image--the application image is therefore significantly smaller. For an update to occur, the application detects that an update is requested, and reboot into the reprogramming image. This only requires a small amount of extra code in the application image, rather than the full reprogramming protocol. In addition, image metadata is only broadcast by the reprogramming image during the reprogramming phase. The dissemination is based on the _Deluge_ protocol, with changes to ensure a node keeps running the reprogramming image until all its neighbours have received the update. This is done by broadcasting an ACK once the new image has been received--the neighbours can now remove this node from their _requesting set_. Once a node has downloaded all the update pages, and its requesting set is empty, it reboots into the updated application image.  
As well as the flexibility benefits introduced by energy reduction, the idea of using a separate image to support the update operation introduces significant benefits into the activity from the viewpoint of autonomous behaviour. It would allow an autonomous update to have significant functionality, without impacting the size of the application image. Also, the time period during which an autonomous update was active, could allow for much more efficient energy use (not subject, for example, to any power-cycling schemes determined by the application).
## 3 Results
### Energy Analysis  
The Gasifier-Generator equipment was running for 2 h (120 min), even though, for the first twenty minutes, it was running in torch mode without connecting the 3-kW motor, connected to the alternator (see Supplementary Materials). With the values of the electrical energy produced by the alternator, we can determine the kWh/kg, that is, the energy produced per kilogram of agricultural biomass and plastics mixture. We can calculate the efficiency of the gasifier from the average volumetric flow of air, with an anemometer. For the calculation of syngas production during gasification tests, an anemometer is used that is connected to the air inlet. Moreover, with the quality of the syngas produced, it is possible to obtain the amount of N\({}_{2}\) present. Another important factor is related to the amount of N\({}_{2}\) present in the air. The data are applied in Formula (2), and the is volume calculated.  
\[\mathrm{V_{syngas}=V_{air}\times(0.781/N_{2})} \tag{6}\]where: V\({}_{\rm syngas}\) volume of syn-gas produced Nm\({}^{3}\)/h; V\({}_{\rm air}\) is the volume of air introduced in Nm\({}^{3}\)/h; 0.781 is the percentage of nitrogen in the air; N\({}_{2}\) is the percentage of nitrogen in the syngas.  
Taking advantage of the fact of calculating the volume of synthesis gas, the equivalence ratio was calculated. The equivalence ratio is commonly used to quantitatively indicate whether a fuel oxidant mixture is rich, lean, or stoichiometric. The equivalence relationship is defined as:  
\[\rm{ER}=(A/F)_{\rm{stoic}}/(A/F) \tag{7}\]  
where: ER is the equivalence relation; (A/F)\({}_{\rm{stoic}}\) is the ratio of air/fuel mass under stoichiometric conditions; (A/F) is the same mass ratio but under the experimental conditions that were adopted.  
Therefore, for fuel-rich mixtures, ER \(>\) 1, and for lean fuel, ER \(<\) 1, and for stoichiometric mixture, ER = 1.  
Gasifier efficiency, total efficiency and engine efficiency are calculated as follows [89]:  
\[\rm{n_{gas}}=(V_{\rm{syngas}}\times LHV_{\rm{syngas}})/(m_{fuel}\times LHV_{ fuel}) \tag{8}\]  
\[\rm{n_{tot}}=E_{el}\times 3.6/(LHV_{fuel}\times m_{fuel}) \tag{9}\]  
\[\rm{n_{eng}}=((\eta_{tot}\times\eta_{gen})/\eta_{gas}) \tag{10}\]  
where: \(\rm{n_{gas}}\) is the efficiency of the reactor-gasifier; LHV\({}_{\rm{syngas}}\) is the lowest heating value of the syngas, in MJ/m\({}^{3}\); m\({}_{\rm{fuel}}\) is the fuel mass used during the test, in kg; \(\rm{n_{tot}}\) is the total efficiency; LHV\({}_{\rm{fuel}}\) is the lower heating value of the fuel or inlet mixture, in MJ/kg. \(\rm{E_{el}}\) [kWh] is the electrical energy generated; \(\rm{n_{eng}}\) is the motor efficiency (0.3); \(\rm{n_{gen}}\) is the efficiency of the generator (0.8).