esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hydraulic-Thermal Coupling Modeling and Analysis for HCS  
### Development of Hydraulic-Thermal Coupled Model  
AMESim serves as a comprehensive multi-disciplinary platform for the modeling and simulation of complex systems. Within the AMESim 2019 software environment, users are empowered to construct intricate multi-disciplinary system models and conduct simulations along with detailed analyses. Furthermore, this platform facilitates the examination of both steady-state and dynamic performance characteristics of any component or system. Following the HCS principle, a hydraulic-thermal coupled model displayed in Figure 3 was constructed using AMESim 2019 simulation software. The hydraulic-thermal coupled model is capable of resolving both the steady-state and dynamic parameters of the hydraulic system, in addition to thermodynamic parameters, by incorporating the influences of heat conduction, heat radiation, and other relevant factors. In the simulation, it is essential to establish the hydraulic and thermodynamic parameters for all components. The modeling procedure involved configuring the hydraulic circuit, selecting sub-models, defining parameters, and conducting simulations. The key parameter configurations in the model are detailed in Table 1. Some of the data in Table 1 are empirical parameters, such as mechanical efficiency, volumetric efficiency, coefficient of heat dissipation, etc. [21; 22].  
Figure 3: Hydraulic–thermal coupled model of HCS.  
The initial parameters of the HCS were established based on the data presented in Table 1. The temperature variations of the hydraulic oil in both the hydraulic oil tank (HOT) and the high-pressure pipe (HPP) are illustrated in Figure 4. The oil temperature reaches a stable state around 9000 s. The steady-state temperatures of the hydraulic oil in the HPP and the HOT are recorded as 83.9 \({}^{\circ}\)C and 49.1 \({}^{\circ}\)C, respectively.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences  
The investigated PRBSs in this article are m-sequences, Gold sequences, Kasami sequences, binary APASs, binary ZCZ sequences, and binary Golay sequences. Those are the most typically used PRBSs in PMCW radar systems, and their most relevant characteristics for the analysis in this article are discussed as follows.
# Recent Advances in Optical Detection of Aminoglycosodies
## 1 Introduction
### The History of Aminoglycos  
The history of AGs can be traced back to the discovery of streptomycin in the 1940s. [PERSON] and [PERSON] separated streptomycin from natural products successfully. Streptomycin is effective for the treatment of infections caused by most Gram-negative bacteria but is ineffective for _Pseudomonas_. The discovery of streptomycin inspired the world to screen antibiotics from natural products, and various AGs were discovered afterward. AGs that have a name with the suffix _-mycin_ refer to those that are derived from _Streptomyces_ genus, while those that have a name with the suffix _-micin_ refer to those that are derived from _Micromonospora_. Neomycins, kanamycincins, gentamicinis, tobramycin, and ribostamycins are typical aminoglycosodies containing 2-deoxystryptamine. They are produced from _Streptomyces fradiae_, _Streptomyces kanamycincticus_, _Micromonospora purprea_, _Streptomyces tenepharius_,and _Streptomyces ribosidifcus_, respectively [2]. Moreover, amikacin and netilmicin are presentive semi-synthetic aminoglycosoides. Figure 1 shows the structures of typical AGs.
## 2 Background and Related Work
### Serverless Computing
In IoT systems, the serverless approach can be particularly useful in the development of back-end components of services, running on edge or cloud nodes. In serverless computing, container startup latency to instantiate new functions is a well-known problem, which falls under the name _cold start_[7]. A cold start occurs when, in order to satisfy a new user request, it is necessary to start a new container, configure the runtime environment and deploy the requested function. As reported in Ref. [7], typical values of cold startup times ranges from hundreds of ms up to a few seconds. Therefore, cold starts can significantly affect the responsiveness of applications and, consequently, the user experience, making the serverless approach not compliant with the requirements of some types of services. A typical solution for this issue includes keeping a number of instances in the idle state (_warm start_), since reusing an existing function instance would take much less time than handling a new request from scratch. However, this clearly has a non-negligible cost, especially when the types of possible functions increase, as is increasingly common, since the modern way of implementing services consists of breaking them down into a series of micro-functions, each performing a simple task. In fact, in this way, the maintenance, upgradability, scalability and reusability of the software are greatly improved. Alternatively, there are a significant number of proposals exploring the use of artificial intelligence (AI) techniques [8], such as reinforcement learning [9], trying to promptly anticipate the occurrence of a new request and thus limiting the impact of keeping the instance in an inactive state, ready to serve new requests. However, these approaches rely on statistical properties; therefore, they may overestimate the number of idle instances that need to be generated to satisfy hypothetical future requests (over-provisioning and, therefore, constituting a waste of resources) or underestimate them, leading to cold start events.
## 3 Results
### Temporal Profile of Volatile Compounds  
The temporal profile of volatile compounds in fermented grains was investigated by GC-MS. In total, 81 volatile compounds were detected in samples, including 14 alcohols, 32 esters, 7 acids, 7 aldehydes and ketones, 8 phenols, and 13 other flavor compounds.  
Figure 3: Correlation network diagram of microorganisms in fermented grains. (**A**) Bacterial correlation network diagram; (**B**) fungi correlation network diagram. The red line represents a positive correlation and the green line represents a negative correlation.  
Figure 2: Temporal profile of microbial community structure in fermented grains during the third round of the fermentation process. (**A**) Bacterial; (**B**) fungal.  
During the heap fermentation process, the concentrations of most volatile compounds increased gradually with time. Among them, ethyl palmitate, phenethyl alcohol, and ethanol, as relatively abundant esters and alcohols, reached 17.34 \(\mathrm{\SIUnitSymbolMicro g}\)/g, 16.69 \(\mathrm{\SIUnitSymbolMicro g}\)/g, and 10.63 \(\mathrm{\SIUnitSymbolMicro g}\)/g at 5 days of heap fermentation, respectively (Table S3).  
Compared to the fermented grain collected from heap fermentation, the fermented grain from pit fermentation had more diverse and abundant volatile compounds (Table S3). The concentrations of alcohols showed a fluctuating upward trend during pit fermentation, reaching a maximum concentration of 44.65 \(\mathrm{\SIUnitSymbolMicro g}\)/g. Phenylethylanol and ethanol were the most abundant alcohols, reaching maximum concentrations of 22.22 \(\mathrm{\SIUnitSymbolMicro g}\)/g and 20.41 \(\mathrm{\SIUnitSymbolMicro g}\)/g at 14 days of pit fermentation, respectively. In particular, the concentrations of ethanol in fermented grains located in the lower layer were slightly greater than those in the fermented grains located in the upper and middle layers. Five new alcohols, namely, 1-nonanol, benzyl alcohol, 3-methyl-1-butanol, (R)-(-)-1,2-propanediol, and (S,S)-2,3-butanediol, were detected during the pit fermentation. The concentration of esters in the sample increased with fermentation time, but the concentrations of esters were higher in the upper and middle layers than in the lower layer. Six esters were newly detected in the fermented grains of pit fermentation, namely, ethyl acetate, formic acid, hexylester, isoamyl acetate, ethyl 3-phenylpropionate, ethyl L(-)-lactate, and phenyl 4-hydroxymphosphate. More abundant acids were detected in the fermented grains from the pit fermentation than fermented grains from the heap fermentation. Among them, acetic acid, as the precursor of ethyl acetate, among others, increased rapidly from 0.20 \(\mathrm{\SIUnitSymbolMicro g}\)/g to 0.90 \(\mathrm{\SIUnitSymbolMicro g}\)/g after 14 days of pit fermentation.
###### Abstract  
This paper aims to investigate the effect of glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) strengthening materials on the static and dynamic behavior of reinforced concrete (R/C) beams subjected to progressive damage. Four identical beams, each strengthened with either GFRP or CFRP, are tested under a cyclic quasi-static loading pattern. Impact hammer tests are performed for undamaged states and various damage levels of the beams. The dynamic test data are analyzed using the Enhanced Frequency Domain Decomposition (EFDD) method to estimate the dynamic characteristics of the beams. In this context, the first three vibration modes in both vertical and horizontal directions are considered. Strengthening is applied to both pre-damaged and undamaged beams, enabling a comparison of their performance before and after the strengthening procedure. Beams strengthened with CFRP exhibit a higher load-bearing capacity and stiffness but also fail at lower displacement levels compared to those strengthened with GFRP, which demonstrate more ductile behavior. Furthermore, the modal frequency ratios indicate that the first vibration mode is more sensitive to damage than the second and third modes. This study highlights the effectiveness of both strengthening materials in enhancing the structural performance of both undamaged and damaged beams.  
Keywords:beam strengthening; GFRP; CFRP; pre-damaged; operational modal analysis; progressive damage; dynamic behavior; quasi-static loading +
Footnote †: journal: Applied Sciences
## 4 Proposed System
### Trajectory-based Message Delivery
#### 4.4.3 Message Carry-and-Forwarding  
If we consider that a target vehicle drives beyond contact point RSU's transmission coverage, another strategy to deliver a message is to rely on VANET routing by means of carry-and-forwarding among neighboring vehicles on the road. For instance, to deliver a message \(M_{jd}\) on VANET, \(RSU_{i}\) requests and finds a volunteer vehicle which willingly joins carrying and forwarding the message. Suppose that a vehicle \(V_{c}\) within \(RSU_{i}\)'s range is a volunteer vehicle. For \(RSU_{i}\)'s request, \(V_{c}\) responds acceptance message \(\{acc,\)\(pid_{ci},S_{c}\}\) to \(RSU_{i}\) to obtain \(M_{jd}\), where \(acc\) is a metadata and \(S_{c}\leftarrow\mathit{idSig(pid_{ci},\)\(acc|pid_{ci})\). \(RSU_{i}\) authenticates \(V_{c}\) by verifying the signature \(S_{c}\) under \(pid_{ci}\), if it holds, provides \(V_{c}\) with \(M_{jd}\).  
Once \(M_{jd}\) is stored in \(V_{c}\)'s storage, \(V_{c}\) carries \(M_{jd}\) by itself until \(V_{c}\) runs into the target vehicle \(V_{d}\) of \(pid_{di}\) on the road before \(ttl\) is expired, or \(V_{c}\) forwards \(M_{jd}\) to a next-hop vehicle in accordance with VANET routing protocol. For the former case, if \(V_{c}\) detects \(V_{d}\) on driving, \(V_{c}\) sends notification message to \(V_{d}\) to inform that \(V_{c}\) has a message \(M_{jd}\) for \(V_{d}\). Then, \(V_{d}\) requests the message to \(V_{c}\) in authenticated manner as follows:  
1. \(V_{d}\) requests the message to \(V_{c}\) by sending \(\{req,pid_{di},S^{\prime}_{d}\}\).
2. \(V_{c}\) verifies the signature \(S^{\prime}_{d}\) and forwards \(M_{jd}\) attached with its signature as \(\{res,pid_{ci},M_{jd},\)\(S_{c}\gets\mathit{idSig(vsk_{ci},res|pid_{ci}|M_{jd})\}\), where \(res\) is a metadata for the response.
3. \(V_{d}\) first verifies \(V_{c}\)'s signature \(S_{c}\) and extracts \(M_{jd}\). Then, \(V_{d}\) checks if \(M_{jd}\) actually originated from \(sRSU_{j}\) by checking \(sRSU_{j}\)'s signature \(S_{j}\) in \(M_{jd}\) as described in step 2) of immediate forwarding.  
On the other hand, if a message is delivered by hop-by-hop forwarding, each intermediary vehicle involved in VANET routing protocol must append its signature to the forwarded message. For instance, suppose that \(hop=\{V_{1}\to V_{2}\rightarrow...\to V_{l}\}\) is a set of vehicles involved in message forwarding to the destination vehicle \(V_{d}\) (Note, how to establish a route and forward a message to the destination node is beyond our scope. We can assume the existing VANET routing protocols such as [6; 29]). The message arriving to \(V_{d}\) consists of \(\{fwd,M_{jd},\{pid_{hi}|S_{h}\}_{V_{h}\in hop}\}\) where \(fwd\) is a metadata for message forwarding and \(S_{h}\) is a signature of each \(V_{h}\). The last hop vehicle can forward \(M_{jd}\) to \(V_{d}\) as described in the above. By verifying each signature \(S_{h}\) under \(pid_{hi}\), \(V_{d}\) can be convinced that the received message \(M_{jd}\) was forwarded via authenticated vehicles.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 4 Numerical Results
### Blocked and Non-Blocked Intervals  
We proceed by analyzing the impact of the system parameters on the mean blocked and non-blocked durations, see Figures 5 and 6. Observing Figure 5a, one may notice that when the distance between the BS and the UE increases, the mean duration of the non-blocked interval decreases for both of the considered values of \(\lambda_{B}\) and \(v_{B}\). The reason is that longer distances between the BS and the UE lead to larger LoS blockage zone areas and thus higher temporal intensity of blocker entries. However, the associated increase in the mean blocked interval duration is less drastic. It could be explained by the fact that the effect of higher temporal intensity on the mean blocked interval duration is non-linear as seen from (11). For moderate values of temporal intensity, both intervals are rather short. As it increases further, individual blockers start to form longer blocked intervals. The mean duration of both intervals is always greater than 1 s for the considered distances and values of \(\lambda_{B}\) and \(v_{B}\). It is significantly higher than the average RTT in the modern Internet, which is often in the range of 30-200 ms [17]. Particularly, it implies one should not expect dramatic TCP throughput degradation, i.e., TCP should achieve its theoretical throughput in both states, for the selected parameters.  
Analyzing the dependence of the mean blocked and non-blocked durations on the speed of blockers as shown in Figure 5b, we note that the trend associated with increased speed of blockers is similar to that observed in Figure 5a for the BS to UE distance. The reason is that the growth in \(v_{B}\) also affects the temporal intensity of blockers similarly, i.e., increased \(v_{B}\) leads to shorter non-blocked intervals and longer blocked intervals. Quantitatively, the effect of \(v_{B}\) is more pronounced as compared to the impact of distance. Note that the duration of the blocked interval is short for the small values of \(v_{B}\), which implies that TCP may not achieve its theoretical throughput in this state. However, the fraction of time that the UE spends in this state is close to zero, meaning that the TCP throughput degradation is minimal.  
The effect of blocker density is demonstrated in Figure 6. Again, the trend caused by \(\lambda_{B}\) is similar to that induced by \(v_{B}\) and \(d\). Comparing the curves corresponding to \(v_{B}=1\) m/s and \(v_{B}=2\) m/s, we observe that the speed of blockers produces the main impact on the mean blocked and non-blocked intervals.  
Figure 4: Comparison of model and simulation results.
## 2 Materials and Methods
### Verification of the Proposed Multi-Purpose Testing System  
The constructed measurement rig (Figure 4) had to be optimized while simultaneously verifying the functionality of the software for the testing equipment.  
Figure 3: Structural design of the load test rig.  
During the experimental pilot testing, various types of functional tests were conducted, including measurements of key characteristics of gear modules in robotic drives, such as angular transmission error, hysteresis, lost motion, starting torque, and others. The process also included integration tests, system testing, and acceptance testing.  
As part of the task, the connectivity verification between hardware components and the central server was also performed to ensure reliable and uninterrupted data transmission during testing. The processing of measured data occurs after each testing sequence, enabling the comparison of current results with previous measurements. The entire testing process was conducted under conditions simulating a real production environment to ensure the highest relevance of the obtained data.  
In addition to the main structural frame, which supports four individually mounted units--each consisting of a support bracket, a servo motor, a gear module, sensors, and a counterweight--the break-in test stand is also equipped with control elements. These control components form part of the operator interface and are used to configure the necessary parameters required for the correct initiation of the test procedure. Furthermore, the system enables fault reset functionality in the event of errors occurring during operation and includes an emergency stop mechanism to ensure immediate shutdown of the entire test stand in hazardous or abnormal conditions. The tested gear modules are labeled G1, G2, G3, and G4. These gearboxes are identical and new, supplied by the same manufacturer.
# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
## 3 Maximum Marginal Approach
### Detail Components Set Construction  
Wavelet transform is a decomposition of EEG based on the frequency. It utilizes a scalable and shiftable wavelet to decompose the EEG for calculating the frequency components. The narrow scale indicates that the frequency is high, while the wide scale indicates low frequency. The wavelet transform is formulated as follows.  
\[WT(a,\tau)=\frac{1}{\sqrt{a}}\int f(t)\varphi(\frac{t-\tau}{a})dt \tag{1}\]  
where \(a\) indicates the scale, \(\tau\) indicates translation, \(t\) is time. In addition, \(f(t)\) is EEG and \(\varphi(t)\) is the wavelet signal. Wavelet transform utilizes a wavelet to infinitely approximate the EEG by changing the scale and translation for calculating the detail and approximate components. The frequency of EEG is the reciprocal of scalar \(a\), and the corresponding time is represented as the translation \(\tau\). Emotions can cause small changes in EEG, and these changes are observed in the detail component. We construct a set to include each EEG signal's detail component. The detail component is defined as follows:  
**Definition 2** (Detail component).: _The detail component of EEG signal is computed by using wavelet transform, which is formulated as \(d\). The set consists of detail component of \(i\in[0,I]\) EEG signal, which is formulated as \(S=\{d_{i}|i\in[0,I]\}\)._  
As shown in Figure 2, there are two detail components \(d_{i}\) and \(d_{j}\) of EEG signals with different emotions, which are extracted by using wavelet transform. The set consisting of these two components is formulated as \(S=\{d_{1},d_{2}\}\).  
Figure 2: Example of the detail component set.  
A discrete wavelet transform utilizes a wavelet to decompose the EEG signal for \(N\) times. Figure 3 shows an example of a two-level wavelet transform for decomposing EEG signal, where \(a^{n}\) and \(d^{m}\) indicate the approximate and detail component at the level \(n\), respectively. The approximate component and detail component at level 1 can be calculated after the first decomposition of EEG. The detail component at level 2 can be calculated by applying wavelet transform on the approximate component at level 1.
# Optimised Twin Fluid Atomiser Design for High-Viscosity, Shear-Thinning Fluids
## 2 Materials and Methods
### Video Analysis  
The nozzle configurations listed in Table 3 were validated using high-speed imaging based on the shadowgraph method. The high-speed CMOS camera, Photron Fastcam SA 1.1, is capable of recording images at up to \(128\times 32\) pixels at a frame rate of 150,000 frames per second (FPS). For this study, a frame rate of 75,000 FPS was chosen to increase the image resolution to \(256\times 128\) pixels, providing an optimal balance between temporal and spatial resolution for analysing atomisation behaviour. The validation was conducted to evaluate the atomisation quality \(100\times 10^{-3}\) m below the nozzle exit for the tested nozzle configurations as well as for the validation of the optimal volumetric ALR for the ONC.  
The focus of the Photron Fastcam was adjusted so that the downward pouring liquid jet was captured at its centre axis. Four video recordings were made at distinct measurement points (MP) downstream of the nozzle (e.g., \(100\times 10^{-3}\) m, \(500\times 10^{-3}\) m, \(700\times 10^{-3}\) m and \(1050\times 10^{-3}\) m) for the determination of droplet velocity and atomisation quality.  
The camera's focus distance was set to \(50\times 10^{-3}\) m throughout all experiments. The camera lens had a focal length of \(30\times 10^{-3}\) m. The pixel size of the camera sensor was \(20\times 10^{-6}\) m, ensuring good light capture, though this also resulted in a minimum resolvable droplet size of \(250\times 10^{-6}\) m. This resolution made the camera suitable for recording the high-speed liquid droplets that have not fully disintegrated near the nozzle, but it limited the ability to capture smaller droplets below \(250\times 10^{-6}\) m in diameter. The Sony RX10M3 was therefore utilised for determining droplet size distributions.  
The open-source image processing software ImageJ J (Version 1.54f)was utilised to obtain an averaged droplet velocity across 100 tracked droplets. The \"TrackMate\" plugin was employed for this purpose. A thresholding detector was selected, and a filter was applied to track only droplets with a pixel area size smaller than 200. This filter was introduced to exclude false positives arising from overexposed or brightly illuminated regions in the video of the high speed camera. These overexposed regions were mistakenly detected as droplets, even though they were not part of the actual spray. A Kalman tracker was used with the following settings: an initial search radius of 15 pixels, a search radius of 20 pixels, and a maximum frame gap of 5 frames.