esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Physical Modelling and Numerical Methodology  
### Numerical Simulation  
The computational domain consisted of an inlet pipe of constant diameter and length L\({}_{1}\) followed by a 90\({}^{\circ}\) bend (elbow), and an outlet pipe of constant radius and length L\({}_{2}\) (see Figure 1). L\({}_{1}\) is obtained from Equation (1), which defines the development length according to the flow regime. L\({}_{2}\) is defined as 50 pipe diameters to ensure that the effects of the secondary flow disappear before the outlet [7]. The bending ratio is defined as Rc/D in the numerical validation and Rc/R in the study cases, where Rc is the radius of curvature and R is the radius of the pipe.  
\[\mathrm{L}=4.4\ \mathrm{Re}^{\frac{1}{6}}\mathrm{D} \tag{1}\]  
\(\mathrm{Z^{\prime}-Z}\), \(\mathrm{\theta}\) and R: Represent the axial, azimuthal and radial coordinates, respectively, \(\mathrm{\varphi}\): Angle of deflection of the bend (see Figure 1). The particle-laden flow at the inlet is vertical and aligned with the acceleration of gravity, g = \(-\)g\({}_{\mathrm{z^{\prime}}}\)\(\bar{k}\).  
The turbulent flow loaded with solid particles through the pipe bent at 90\({}^{\circ}\) was solved by CFD modeling and with the obtained values the ad hoc formulated Stokes numbers were calculated.  
It was studied: Two different particle sizes (d1 = 50 \(\mathrm{\upmu m}\) and d2 = 150 \(\mathrm{\upmu m}\)), for two dis-tinct flow conditions (Re1 = 61,508 y Re2 = 173,972), and three curvature ratios (Rc/R = 1, 4 and 8). The chosen particle sizes were determined for a dilute flow and according to the response time of the particle in a tracer flow for a Stk \(<\) 1 (not to be confused with the Stk formulated specifically for the flow in an elbow, but rather as classic definition of the number of stokes defined by average flow velocity and pipe diameter). In this work, nine different geometric configurations were simulated: Three cases correspond to this validation section (Table 1), and the other six were used to evaluate the relationship between Stokes numbers and particle accumulation (see Section 2.4).  
_Flow Properties._ The properties of the gas (air) and particles (seeds) are presented in Table 2.  
_Boundary Conditions._ Boundary conditions of velocity at the inlet, pressure (atmospheric) at the outlet and no-slip condition at the wall were imposed. The initial velocity is determined from a plane profile with hybrid initialization, solving the Laplace equation. A fully elastic shock condition and a coefficient of restitution for the particles was set at 1. We considered non-reactive, isothermal, and incompressible flow (low Mach number).
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.5 Error Analysis  
As shown in Table 8, in multi-granularity temporal knowledge graph question answering, the errors in the model's answer predictions reflect its shortcomings in understanding questions and reasoning abilities. For instance, when addressing the question \"When did [PERSON] first visit China?\", the model predicted multiple dates, including \"27 April 2009\" and \"8 April 2009\", while the actual answer was \"8 April 2009\". This situation indicates that the model failed to effectively identify the key temporal information in the question, leading it to choose among multiple candidate answers without accurately pinpointing the unique correct answer. Such a lack of understanding of temporal context may be a primary reason for the model's poor performance on time-related questions. Moreover, the model also exhibits significant limitations in handling entity recognition and temporal reasoning. For example, in predictions involving multiple countries and individuals, the model failed to accurately identify the specific entity related to \"Businessperson  
Figure 8: The weight coefficients of the tokenized question processed by ALBERT.  
Figure 9: The weight coefficients of the tokenized question processed by the CNN after [PERSON].  
of Uzbekistan\", reflecting its confusion in processing multiple entities. Similarly, the model struggled to grasp the granularity of time when dealing with month information, resulting in incorrect date predictions.  
Overall, these errors indicate that the model needs to enhance its contextual understanding and temporal reasoning capabilities in multi-granularity temporal knowledge graph question answering to improve overall answer accuracy. Future research could focus on improving the model's sensitivity to temporal information and reasoning abilities, thereby enhancing its performance in complex question-answering scenarios.
## 2 Experimental Setup
### Hydraulic Conditions  
Five velocities measured during a field campaign in the sea area of Juehua Island, where the M-shaped artificial reef prototypes will be released, were selected to be replicated in this study. These velocities are 0.6, 0.9, 1.2, 1.5, and 1.8 m/s, and they were obtained by releasing the flow Q as 20, 30, 40, 50, and 60 L/s, respectively (\(\pm\)2.5%). Based on the Froude similarity adopted, the correspondent experimental velocities tested within the experimental facility were then 0.085, 0.130, 0.170, 0.214, and 0.257 m/s, respectively; therefore, the values replicated were within \(\pm\)0.86% of the values measured in the field.  
The experimental water depth tested in the laboratory was 300 mm, and it was calculated using Froude similarity as well, according to the actual water depth recorded in situ, 15 m. To sum up, a total of 20 groups of experiments were conducted, including four geometrical conditions (1, 2, and 3 previously explained plus a controlled test with no artificial reefs, called 0), and five flow conditions with dissimilar velocities. All these experimental details are summarized and displayed in Table 1.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 6 Conclusions  
In this paper, we have dealt with LoRaWAN, a representative technology of LPWAN. In particular, we addressed a fundamental problem in LoRaWAN, the DOWNLINK constraint, by cooperative downlink listening, which is based on a shared downlink slot triggered periodically by the DL initiator.  
Figure 14: Battery life.  
Figure 13: Energy consumption.  
In addition, energy balancing has been achieved through the selection of a dynamic DL initiator to avoid intensive energy consumption of the DL initiator. In addition, it shows that various data-centric models, groupcasting and geocasting, are possible in association with CDL. The CDL was implemented with minimal modifications to the existing LoRa standard and maintained compatibility with the LoRa standard. Experiments have shown that the CDL significantly reduces traffic over the LoRa standard, especially when the number of nodes in a network increases significantly. In addition, CDL showed almost similar energy consumption compared to LoRa, and it was shown that it can guarantee battery life of more than 10 years considering one hour communication. Based on the results of this paper, it is expected that it can be applied to various application fields based on LoRaWAN.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2015R1D1A1A01059317).  
The authors listed all contributed to the development of the idea and experiments contained within this paper. More specifically, the first version of this paper was written by the corresponding author, [PERSON], and structure and revisions of this paper were led by the 1 st author.  
The authors declare no conflict of interest.
# Multimodal AutoML via Representation Evolution
## 5 Results
### Quantitative Results  
The overview of the results is shown in Table 3.  
Overall, the results can be summarized as follows. The proposed MuRE performs on average the best (3.25 average rank), followed by BERT (4.5) and late fusion via summation (4.5). The early fusion did not perform as well (ranked 5 th on average). The results indicate that multimodal approaches dominate, which indicates that the considered problems are indeed best solvable by considering both modalities. The proposed MuRE performs subpar on fakeddit dataset, however, is very competitive on other datasets. It outperforms all other approaches on caltech-birds dataset; this dataset requires that approaches learn from very few instances per label, indicating MuRE's data efficiency. Another observation is that multimodal models always outperform image-only models. There is no clear winner in terms of considering early or late fusion. Overall, AutoML approaches are on average ranked high. Detailed benchmark is specified as part of the Supplementary Material. Furthermore, results also indicate sub-optimal performance on the Fakeddit (I+T) dataset. The best-performing method for this dataset was a text-only BERT-based model. For this task, the follow-up results were obtained by considering early fusion(combining embeddings). This result indicates that a focused combination of sub-symbolic-only representations can offer competitive results. The worse performance of MuRE on this task can be attributed to potential noise introduced by considering high-dimensional sparse spaces alongside sub-symbolic ones; it is possible that the symbolic part of the space did not encode the relevant information present in embeddings, and, thus, impacted the learning in a negative manner. The task itself is defined as binary classification of whether a given text+image combination is _factual_. Given that MuRE considers also knowledge-graph-based representations, it is possible this part of information, due to imperfect mapping, offers incomplete information (note that only text titles are given, which might not have been enough in this case). Should the best-performing (for this task) BERT model capture these relations better, this is a possible explanation for better performance. The overall results indicate that automated representation learning serves as a strong baseline against strong competitors based on single modalities. Furthermore, it outperforms other commonly adopted fusion approaches, making it a strong baseline for multimodal tasks considered. To better understand whether the multimodal representation space already reflects the class structure, we further visualized the 2D projection [48] of the high-dimensional space, colored by the class assignments. The result is shown in Figure 2.  
The visualization demonstrates that the joint representation space already segments the space of instances according to class assignments. Even though some of the classes are less well separated, relatively many denser clusters emerge.
## 2 Materials and Methods
### Olive Oil Characterization
#### 2.5.5 Extraction and Analysis of Volatile Compounds  
VOO samples (0.5 g of oil in 10 mL vials) were placed in a vial heater at 40 \({}^{\circ}\)C and after a 10 min equilibration, volatile compounds were adsorbed onto a SPME fiber DVB/Carboxen/PDMS 50/30 \(\upmu\)m (Supelco Co., Bellefonte, PA, USA) for 50 min. Desorption of volatile compounds was performed directly into the injector on a HP-6890 gas chromatography apparatus (Agilent Technologies, Santa Clara, CA, USA), equipped with a DB-Wax capillary column (60 m \(\times\) 0.25 mm i.d., film thickness, 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA). Three vials per sample were analyzed. Identification of volatile compounds was conducted with a 7820A/GC-5975/MSD system (Agilent Technologies, Santa Clara, CA, USA) equipped with the same column and using similar conditions. Full information on the operating conditions used for both systems 7820A/GC-5975/MSD and HP-6890, as well as the quantification method have been fully described in a previous paper [27]. The volatile compounds were individually quantitated and also clustered into different groups according to their origin in the lipoxygenase (LOX) pathway branch (C6 and C5 compounds) from linoleic (LA) and linoleic (LNA) acids, as well as terpenes and branched-chain (BC) volatile compounds from amino acid metabolism:  
C6/LnA aldehydes: (_E_)-hex-2-enal, (_Z_)-hex-3-enal, (_Z_)-hex-2-enal, (_E_)-hex-3-enal.  
C6/LnA alcohols: (_E_)-hex-2-enol, (_Z_)-hex-3-enol, (_E_)-hex-3-enol.  
C6/LA aldehyde: hexanal.  
C6/LA alcohol: hexan-1-ol.  
C5/LNA carbonyls: pent-1-en-3-one, (_E_)-pent-2-enal, (_Z_)-pent-2-enal.  
C5/LNA alcohols: pent-1-en-3-ol, (_E_)-pent-2-en-1-ol, (_Z_)-pent-2-en-1-ol.  
PD: pentene dimers (seven isomers).  
C5/LA carbonyls: pentan-3-one, pentanal.  
C5/LA alcohol: pentan-1-ol.  
LOX esters: hexyl acetate, (_Z_)-hex-3-en-1-yl acetate, (_E_)-hex-2-en-1-yl acetate.  
Non-LOX esters: methyl acetate, ethyl acetate, methyl hexanoate, ethyl hexanoate.  
Terrepenes: limonene, \(\beta\)-ocimene.  
BC aldehydes: 3-methyl-butanal, 2-methyl-butanal.  
BC alcohol: 2-methyl-butan-1-ol.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### ML/AI Accuracy  
Accurate prediction of solar power is a crucial component in the management of energy, dispatch of electricity, and maintenance of grid stability. The accurate prediction of solar power output enables utilities and grid operators to effectively control the variability in power generation, reduce interruptions, and strategize energy distribution.  
An underestimated source of error in PV-DTs is the combined effect of climate change and machine learning model uncertainties [124; 125; 126]. ML models are generally considered a black box and significant efforts have been made in recent times to improve their trustworthiness [127; 128; 129]. As previously mentioned in sub-Section 3.3.5, ML models are applied for several modeling and analyses in PV-DT to provide varying levels of accuracy. In recent times, the combined effects of climate change have been shown to significantly vary expected input and output values from the ML models [130; 131]. Considering that PV systems rely on a chain reaction, from accurate weather and environmental conditions forecasting to stable temperature and operating condition of inverter/electronic components to accurate solar array yield, brings to the fore the possibility of high levels of uncertainty that must be overcome in PV-DT development. This scenario may become worse in ML/AI algorithms that require trial and error tuning [65].  
High MI/AI accuracy in PV-DT is not only necessary for energy prediction but also for the economic evaluation of solar energy generation in smart microgrids [7], the strategic planning of energy allocation, prompt interventions, and decreased operational expenses in line with the objectives of sustainable energy management. This emphasizes the need for high accuracy and reliability.
## 2 Materials and Methods
### Problem-Solving Methodology  
The methodology for solving the movement of freely moving AVGs is illustrated in Figure 3. Based on the problem definition in Section 2.2, the methodology consists of four sequential steps. Firstly, it is necessary to analyze the problem of intra-company transport and choose a suitable type of transport system. The choice of transport system is based on the attributes of the production system. In the next step, selecting a suitable simulation program and creating a simulation model of the production system is necessary. In our case study, the simulation program Tecnomatix Plant Simulation version 2302 is applied, and the fictitious production system described in detail in Section 2.1 is modeled. The third step is defining the movement of AGVs, both from the point of view of the AGV's movement path determination and then from the methods defining this movement. The last step is the optimization of the transport system. The optimization goal is to maximize production volume while minimizing the number of AGVs.  
The flowchart detailing the sequence of steps for controlling AGV movement based on equipment requirements is presented in Figure 4. It graphically highlights the activities of individual equipment groups MG1-MG4. Subsequent sections elaborate on this methodology, presenting algorithms and methods that govern the operation of the intelligent AGV transport system.  
It must be noted that the primary issue with the free movement of AGVs is the occurrence of collisions. Despite the capabilities of the Tecnomatix Plant Simulation program, it does not provide a means to set conflict zones for mutual AGV avoidance effectively. Consequently, collisions are an inevitable part of the simulation. Although the AGV can be configured with two collision zones, it is only possible to define deceleration for each zone. To address this, the problem of AGV collisions and the subsequent need for mutual avoidance is managed by slowing down the AGV movement. This deceleration simulates  
Figure 3: Methodology for problem solving.  
Figure 2: Ways of moving AGVs: track (**a**); markers (**b**); freely moving (**c**).  
the time required for AGVs to maneuver around one another. If an AGV detects another AGV within a distant collision zone, it will decelerate slightly. If it enters a near-collision zone, it will decelerate significantly.
## 4 Simulation of Transient Flow Wave Signals in Tubular Strings
### Frequency Response of the Tubular String  
For Equation (30), only frequency \(f\) is kept variable (varying from 0.1 Hz to 20 Hz) under the conditions of the tube diameter 0.1 m, wave velocity 1260 m/s, fluid density 1200 kg/m\({}^{3}\), dynamic viscosity 3.81 \(\times\) 10\({}^{-3}\) Pa-s and terminal impedance \(Z_{l}=4Z_{c}\). Then the amplitude-frequency characteristics of the whole tubular string within 20 Hz can be calculated.  
As seen in Figure 15, when the frequency of the signal gradually increases, multiple peaks can be observed, indicating that the signal is fluctuating. When the fluctuation frequency is very low, the ratio of the end amplitude to the beginning amplitude for the pressure head fluctuation is close to 1. When the length of the tubular string increases to 1000 m, the number of wave peaks increases and their amplitude becomes larger. For a short tubular string length of 100 m, the end amplitude showed multiple peaks over the entire frequency range, peaking at 3 Hz, 9 Hz and 16 Hz, and the end amplitude is greater than the beginning amplitude near all three frequency points. In deep wells, the ratio of the end amplitude to beginning amplitude fluctuates sharply as the signal frequency increases, and the signal frequency becomes more influential on the ratio of the end amplitude to beginning amplitude. If the interference signal is strong, the signal for communication is easily drowned in the interference signal, making it difficult to process the signal. Therefore, it is necessary to select the frequency corresponding to the peak of the ratio of the end amplitude to the beginning amplitude for signal transmission.  
Figure 14: Effect of tube terminal impedance on fluctuations.  
Figure 15: Amplitude-frequency characteristics of different lengths of tubular strings.  
During the signaling of transient flow fluctuations, it is desirable that the signal has a relatively large amplitude after it has been transmitted to the surface. Since the well depth is always changing, the optimal communication frequency \(f\) is also always changing. For Equation (31), if the parameters other than tube length \(l\) and fluctuation frequency \(f\) are fixed, it is also possible to plot the amplitude-frequency space for different lengths of tubular strings, as shown in Figure 16. Once the length of the tubular string and the fluctuation frequency are determined, the corresponding amplitude ratio can be determined. If the signal processing capability of the ground equipment is limited and the signal is required to reach the ground with a higher amplitude, calculations can determine the appropriate frequency range for different lengths of tubular strings used for signal carriers.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems
### Adopting MIMO Architecture Combining Polarization Multiplexing and MRC
The key performance improvement lies in the following: compared to single-channel transmission, this 2 \(\times\) 2 MIMO-PDM-with-MRC solution achieves up to 1.2 dB SNR gain while successfully overcoming coherence disruption phenomena prevalent in long-distance MIMO transmission. This represents the first breakthrough solution simultaneously delivering performance gains and effective coherence disruption suppression in photonics-assisted terahertz MIMO systems. \[\mathbf{Y}=\mathbf{H}\mathbf{X}+\mathbf{n}. \tag{6}\]0 Diversity reception gain vs. number of receiver antennas diagram.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things
### Security in Crowd-IoT
Figure 18: System model for secure fog-based crowdsourcing [33]. In machine learning, federated learning, also known as collaborative learning, can train an algorithm across multiple decentralized edge devices or servers. This is different from traditional centralized machine learning techniques, which either require local datasets are uploaded to one server or often assume that local data samples are identically distributed in traditional decentralized approaches. [PERSON] [58] proposed a blockchain-based crowdsourcing federated learning system for IoT devices. Several technologies such as edge crowdsourcing and computing, mobile technologies, blockchain, distributed system, and storage, and federated learning, are used to build up the system. Figure 20 shows an overview of their system. There are three major components, manufacturers, customers, and blockchain in the system. For a crowdsourcing federated learning task, manufacturers send a request. Then customers who are interested sign on their trained models to the blockchain, which serves as a centralized server to collect these models. In the crowdsourcing tasks, authors also designed an incentive mechanism to attract participation. The blockchain will monitor updates during the federated training. Their system can guarantee the model accountability to prevent malicious attacks. The study in this section reveals that approaches employing public-key encryption methods are unsuitable for crowdsourcing since they need the persons issuing the keys for decryption to do so online. This will make the implementation of crowdsourcing with a large number of users and tasks very complex. Conventional symmetric encryption  
Figure 19: Distributed fog-blockchains for mobile crowdsourcing [57]. Figure 20: Privacy-preserving blockchain-based federated learning for IoT [58]. methods on the other hand are inapplicable to crowdsourcing since the data can be compromised once the keys are revealed. Using blockchain techniques and advanced secure encryption mechanisms hold a great promise for crowdsourcing IoT systems.