esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Limits of Large Language Models' Ability to Distinguish Between Objects
## 4 Experiments
### Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Q1**} & \multicolumn{4}{c}{**Q2**} \\ \cline{3-10} \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{2}{c}{**Step 1**} & \multicolumn{2}{c}{**Step 2**} & \multicolumn{2}{c}{**Step 1**} & \multicolumn{2}{c}{**Step 2**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multi-**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multi-**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multi-**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multi-**} \\  & & **Object** & **Object** & **Object** & **Object** & **Object** & **Object** & **Object** & **Object** \\ \hline closed model & GPT-4o [49] & **98.59** & **100.00** & **64.79** & **12.50** & **100.00** & **100.00** & 49.30 & 0.00 \\ \hline \multirow{4}{*}{small} & Qwen2-VL-7B [16] & 76.06 & **100.00** & 46.48 & 0.00 & **100.00** & **100.00** & 49.30 & 0.00 \\  & Phi-3-5-3.8B [5] & 50.70 & 75.00 & 25.35 & 0.00 & 90.14 & **100.00** & 39.44 & 0.00 \\ scale & llama3-lava-next-8B [3] & 76.06 & 87.50 & 45.07 & 0.00 & 81.69 & 87.50 & 38.03 & 0.00 \\ models & Osival-6-German-92B [50] & 84.51 & **100.00** & 52.11 & 0.00 & **100.00** & **100.00** & **54.93** & 0.00 \\  & MiniCPM-V-2-6-8B [51] & 85.92 & 87.50 & 52.11 & 0.00 & 94.37 & **100.00** & 53.52 & 0.00 \\ \hline \multirow{4}{*}{medium} & Llama-3-2.1B [31] & 80.28 & **100.00** & 47.89 & 0.00 & 95.77 & **100.00** & 47.89 & 0.00 \\  & lava-v1-6-13B [52] & 88.73 & 87.50 & 53.52 & 0.00 & 98.59 & **100.00** & 47.89 & 0.00 \\ scale & InterrVL2-5-26B [53] & 85.91 & **100.00** & 60.56 & 0.00 & 92.96 & **100.00** & 49.30 & 0.00 \\ models & lava-v1-6-34B [52] & 74.65 & 75.00 & 40.85 & 0.00 & **100.00** & **100.00** & 49.30 & 0.00 \\  & InterrVL2-5-38B [53] & 90.14 & 75.00 & 63.38 & **12.50** & 94.37 & **100.00** & 47.89 & 0.00 \\ \hline \multirow{4}{*}{large scale models} & Qwen2-VL-7B [16] & 59.15 & 75.00 & 43.66 & 0.00 & 94.37 & **100.00** & 43.66 & 0.00 \\  & Llama-3-2.90B [3] & 78.87 & 75.00 & 46.48 & 0.00 & 90.14 & **100.00** & 49.30 & 0.00 \\ \cline{1-1}  & InterrVL2-5-78B [53] & 73.24 & 50.00 & 53.52 & 0.00 & 92.96 & **100.00** & 47.89 & 0.00 \\ \cline{1-1}  & lava-next-72B [52] & 78.87 & 87.50 & 47.89 & 0.00 & 98.59 & **100.00** & 49.30 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Object classification results corresponding to Questions 1 and 2. Bold values indicate the best scores and underlined values indicate the second-best scores for each question. ities. By contrast, many smaller or mid-sized models, such as llama3-lava-next-8B and Ovisi1.6-Gamma2-9B, show marked drops in Step 2 accuracy even when they can correctly identify the objects in Step 1. This gap highlights the continued challenge for current vision-language models in going beyond basic classification to deliver coherent scene descriptions that accurately capture positional details.
## 4 Single-Objective Optimization  
In this work, the single-objective optimization problem is solved using the direct search approach. Additionally, two single-objective tasks are solved with a MATLAB R2024a (License No. 41224477) computer program: maximizing the gearbox efficiency and minimizing the gearbox volume. The figures and observations from the program include the following. The link between \(\mathrm{rgb}\) and \(\mathrm{u_{1}}\) is depicted in Figure 4. It is clear that for an ideal value of \(\mathrm{u_{1}}\), \(\mathrm{\eta_{gb}}\) reaches its maximum. Figure 5 shows the relationship between \(\mathrm{u_{1}}\) and \(\mathrm{V_{gb}}\). It was found from the figure that \(\mathrm{V_{gb}}\) reaches its minimum value when \(\mathrm{u_{1}}\) is at its ideal value. The relationship between \(\mathrm{\eta_{gb}}\) and \(\mathrm{V_{gb}}\), and \(\mathrm{\psi_{ba1}}\) and \(\mathrm{\psi_{ba2}}\), is depicted in Figures 6 and 7, respectively. Figure 6 illustrates that an increase in \(\mathrm{\psi_{ba1}}\) results in an increase in \(\mathrm{\eta_{gb}}\) and a decrease in \(\mathrm{V_{gb}}\). As \(\mathrm{\psi_{ba2}}\) increases, both \(\mathrm{\eta_{gb}}\) and \(\mathrm{V_{gb}}\) also drop (Figure 7). The relationship between the overall gearbox ratio, \(\mathrm{u_{r}}\), and the optimal gear ratio of the first stage \(\mathrm{u_{1}}\) is shown in Figure 8. Additionally, Table 2 presents the newly derived constraints for the variable \(\mathrm{u_{1}}\) that are calculated from the solutions of single-objective problems.  
Figure 4: Relationship between \(\mathrm{u_{1}}\) and \(\mathrm{\eta_{gb}}\).  
Figure 8: Optimal gear ratio of stage 1 versus the total gearbox ratio.  
Figure 6: Relationship between \(\psi_{ba1}\) and \(\eta_{gb}\) (**a**) and \(V_{gb}\) (**b**).  
Figure 7: Relationship between \(\psi_{ba2}\) and \(\eta_{gb}\) (**a**) and \(V_{gb}\) (**b**).
## 3 Results and Discussion
### Connected vs. Total Porosity  
Porosity \(\phi\) is one of the important properties of a porous medium, and is defined as the ratio of pore volume to the total sample volume. Connected porosity \(\phi_{con}\) is defined by the ratio of the volume of interconnected pores to the total sample volume, and, as a rule, \(\phi\geq\phi_{con}\). Some studies have shown that there is a relationship between \(\phi_{con}\) and \(\phi\)[16, 18, 37], which allows the percolation threshold porosity \(\phi_{c}\) to be found [37]. The \(\phi_{c}\) is the porosity at which the fluid begins to flow [21]. The porous medium becomes impermeable below \(\phi_{c}\). Percolation threshold porosity is usually determined based on the  
Figure 8: Connected pore space of randomly selected sub-volumes extracted from samples #1 (**a**), #2 (**b**), #3 (**c**) and #4 (**d**) before and after injection of HCl solutions. relationship \(k\sim(\phi-\phi_{c})^{q}\), where \(q\) is the power exponent [4; 6; 8; 19; 20; 21]. This relationship is often used as sufficient data on total porosity and absolute permeability are collected during the numerous laboratory studies carried out on cores. The percolation threshold porosity can also be determined if the relationship between connected and total porosity is available [16; 18; 37]. In this study, \(\phi_{c}\) is defined as the positive root of the equation \(a\phi^{2}+b\phi+c-\phi_{con}=0\), the value of which is given in Table 4.  
The study of the pore structure of young sea ice [18] and basaltic clasts [37] using micropot computed tomography showed that the dependence of the connected porosity on the total porosity obeys a power law according to the percolation theory \(\phi_{con}\sim(\phi-\phi_{c})^{p}\), where \(p\) is the power exponent. [PERSON] et al. approximated the relationship between the connected and total porosities using a cubic polynomial \(\phi_{con}=ax^{3}-(2a+\phi_{c})x^{2}+(a+1+\phi_{c})x\), where \(x=(\phi-\phi_{c})/(1-\phi_{c})\)[16]. Figure 9 shows the dependence of connected porosity on total porosity for sub-volumes from heterogeneous and naturally fractured samples. Blue circles and orange squares correspond to sub-volumes before and after their dissolution. The colored solid lines indicate the best-fitting curves of the relationship between the connected and total porosity, with a correlation coefficient \(R^{2}=0.96-0.99\). The fitting equation has the following form: \(\phi_{con}=ab^{2}+b\phi+c\), where \(a,\ b\), and \(c\) are the fitting coefficients, the values of which are given in Figure 9.  
As can be seen from Table 4, the values of \(\phi_{c}\) for heterogeneous and fractured carbonate samples before and after their dissolution are close to 5.9%, which is determined during an experimental study of the dissolution of carbonate samples [17]. There is a decrease in \(\phi_{c}\) after dissolution, except for a slight increase for the fractured sample, indicating that pore connectivity is increased due to the formation of new channels during rock dissolution. The largest change, \(\sim\)60%, was observed for a heterogeneous sample.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework  
In this section, the context of the framework is outlined and its elements are defined. The framework's conceptual view is outlined in Figure 1 and explained briefly in Section 1. To complement, Figure 2 shows the real implementation of the framework. The framework consists of Echelon electricity meter with ZigBee interface card called Multipurpose Expansion Port (MEP) card, developed by Develco Products company and a Raspberry Pi that pairs with the MEP card via ZigBee gateway that is connected to it. The Raspberry Pi works as a gateway and data handler. It forwards the measurements to the developed DataBase Cloud Service (DBCS) [10].  
The context is that a _user_ is living in his/her home and there are some electrical _apilances_ are installed in the home. When a user makes use of an appliance, it is referred to as a _usage_. The framework gathers the user's consumption data from the _smart main meter_ and then tries to check and recover the data and then recognize which appliances were used with their usage time. The framework interprets the results and builds a natural language report of the user's behavior. The framework's _beneficiaries_ will receive natural language reports (e.g., via emails, SMS, social networks, etc.) about the observed users' behavior and thus they do not have to be physically present to the users.  
The data check, data recovery, LDA, outcome refinement, and Natural Language Processing (NLP) are deployed as services in the intelligent services part, and they use RESTful interface to exchange data. Due to this interface, the framework enables an easy upgrading to its services without affecting other components in the framework. The following sections explains how each service is working.
## Materials and Methods
### MODIS GPP Product  
The MODIS instruments on the satellites Terra and Aqua provide periodic observations of global air, vegetation, and soil-related information [45]. MOD17A2 is a global-scale, long-term series of terrestrial GPP with different time scales from weekly, monthly to yearly [46]. Version 6 is the latest generation of the MOD17A2 GPP product, which is based on the radiation-use efficiency model. It can be potentially used as input to ecosystem models for calculating terrestrial energy and carbon and water cycles [17]. The newly-released data are produced using the 8-day LAI/FPAR composite (500 m), and the native resolution is increased to 500 m in comparison with previous versions. In order to match the spatial resolution of the BESS GPP data, the spatial resolution of MODIS GPP is resampled to 1 km. This study used MOD17A2H (C6) from 2001 to 2015, which is available from the website [https://e4 fdl01.cr.usgs.gov/MOD17/MOD17A2H.006/](https://e4 fdl01.cr.usgs.gov/MOD17/MOD17A2H.006/). A total of 13,110 scenes of MODIS GPP images were obtained for analysis in this study. The detailed algorithm of MOD17A2 was proposed by [PERSON] [47] in 1972. The equation is as follows:  
\[\text{GPP}=\varepsilon\times\text{APAR} \tag{1}\]  
\[\varepsilon=\varepsilon_{max}\times\text{TMIN}_{scalar}\times\text{VPD}_{ scalar} \tag{2}\]  
\[\text{APAR}=(\text{SWRad}\times 0.45)\times\text{FPAR} \tag{3}\]  
where \(\varepsilon\) is the estimated LUE and \(\varepsilon_{max}\) is the assumed maximum LUE, mainly affected by the vegetation type; \(\text{TMIN}_{scalar}\) is the temperature stress factor, \(\text{VPD}_{scalar}\) is the water stress factor, APAR is the photosynthetically active radiation absorbed by the vegetation, and SWRad is the short-wave solar radiation received at the surface; FPAR is the fraction of photosynthetically active radiation absorbed by green vegetation, which uses the MODIS FPAR product (MCD15A2H.006).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.3 FROG Node  
The Fog-computing platform \"FROG\" [22] was developed for smart city and precision agriculture applications. The FROG node is based on the Arduino Fio V3 that relies on the low-power ATmega32U4 microcontroller having 32 kB flash program memory, 2.5 kB SRAM, 1 kB EEPROM. A throughput of 16 million instructions per second (MIPS) at 16 MHz is attainable to allow for power consumption optimization against processing speed. An XBee-PRO XSC radio system that operates in the 902-928 MHz band was chosen for communication. A software-selectable transmit power output of 24 dBm is achievable by the radio system. The receiver sensitivity is \(-\)109 dBm at 9600 band. The ideal line-of-sight communication range can reach up to 45 km.  
The node is equipped with several on-board gas sensors including CO\({}_{2}\) sensors. A 12 V Li-Po battery with a capacity of 6400 mAh is utilized for powering the sensor node. Regarding power consumption, the FROG node's processor and the sensors altogether draw 741.98 mA of current. The current consumption of the XBee-PRO XSC radio module is around 266.5 mA.
## Appendix B PSO-Based Optimization Program
### Iteration Module  
During the iterative process of the particle swarm optimization algorithm, it is essential to confine all particles within the feasible region. This means that the constraints of each particle must be checked. However, verifying the constraints with the finite element analysis is time-consuming. To improve this, constraint verification is only performed for particles whose cost is lower than the previously lowest, given that the objective function (theoretical material cost) can be explicitly and directly calculated by design variables. The improvement leads to a significant reduction in verification times.  
Detailed steps of the iteration module are:  
1. Initialize random velocity for each particle in the swarm.
2. Initialize Pfitness, Gfitness, Pbest, and Gbest.
3. Update velocity and position for each particle with Formula (A1) and (A2).
4. Calculate fitness for each particle. If the fitness is worse than Pfitness, undo the update of position and wait for the next evolution. If the fitness is better than Pfitness and verification is checked, then update Pbest. If the fitness is also better than Gfitness with verification checked, then update Gbest.
5. Repeat step 3 and step 4 until each particle in the swarm has been updated.
## 1D and Map-Based Modeling Approaches for Railway Compression Ignition Engine in NRTC Application
Devices like turbochargers and injectors can be integrated into the flow network through modules that replicate their interactions with the flow during simulation [19]. A detailed 1D engine model offers information over hundreds of sub-volumes in an engine performance simulation, but the computational time can take up to several orders of magnitude more than in real-time. When the simulation speed is of priority, a detailed model can be converted into a simplified 1D model in which volumes are lumped together, where possible, to enable a larger timestep and solve fewer sub-volumes. In this case, a reasonable trade-off between computational effort and accuracy is chosen as a function of the application. These kinds of models can also be designed to run in real-time for Hardware in the Loop (HiL) applications at the expense of certain details, e.g., wave dynamics, that might not reflect the final results [20]. The simplest approach to describe the entire engine in a data-driven manner is through a map-based engine model which comprises the entire engine behavior into maps. With the proper maps, vehicle performances and fuel consumption can be reasonably assessed for various legislation cycles and drivetrain configurations [21]. In ref. [22], the authors applied a map-based approach to model an innovative thermo-electric propulsion system for marine applications, in order to perform optimal management strategies for emissions reduction and battery pack durability. The map-based approach is also suitable to model stationary applications such as innovative micro-combined heat and power [23]. In the map-based modelling approach, engine components are not modelled, and the computational requirements are low. To the authors' knowledge, although studies on engine simulation are available in the specific literature, the novelty introduced in this work is firstly related to the specific field of application. Further to this point, there is a poor number of works in the literature on compression ignition engines for railway applications. In particular, the implementation of the Non-Road Transient Cycle emission standard for these types of engines has not been thoroughly investigated. Furthermore, the aim of this work is the evaluation of a reasonable trade-off between model accuracy and computational time among three different modeling approaches for the same engine performing the homologation cycle mentioned above. The purpose of this analysis is to find the right compromise in terms of the computational effort and predictive accuracy of the proposed models in view of more complex numerical applications for future authors' activities involving railway powertrains. The remainder of this paper is organized as follows. In the first part, an outline of the emission test cycle is presented. A specific section will be reserved to illustrate engine models and the application of the transient cycle to them. In the last part of the paper, the main outcomes, with suggestions for further advances, are discussed.
## 5 Simulation Results and Analysis
### The Impact of Network Density  
In the testing phase, for the purpose of evaluating and analyzing the network performance in different network densities for the GPSR, QNGPSR, TQNGPSR, and DLGR-2 DQ protocols, we tested different numbers of nodes in the UANET that varied in the range of 40\(\sim\)140. Figures 7 and 8 present the average E2E delays and PDRs under different network densities, respectively. There were 100 nodes in the network. The minimum speed of the nodes was 3 m/s, and the maximum speed of the nodes was 10 m/s. The packet-sending rate was 5 Hz.  
As shown in Figure 7, the E2E delay of DLGR-2 DQ outperformed the other three routing protocols. Since DLGR-2 DQ considered the total waiting time in the reward function, it required that each node learn to avoid selecting a congested neighboring node. As the number of the nodes increased, the delays in DLGR-2 DQ gradually decreased. That was because the network density increased as the number of the nodes increased, so the probability of reaching the destination node with the minimum number of hops increased.  
Figure 5: Convergence performance comparison of PDR among TQNGPSR, QNGPSR, and DLGR-2 DQ during the training phase.  
Figure 6: Convergence performance comparison of different exploration strategies during the training phase.  
As shown in Figure 8, as the network density increased, more nodes assisted in forwarding packets to the destination node, so the PDR of all the routing protocols increased. The PDR of DLGR-2 DQ was also significantly better than the other routing protocols, because DLGR-2 DQ considered the link quality as a factor of the reward function and thus avoided forwarding packets on a poor-quality link between the current forwarding node and a candidate forwarding node.
## 3 Results and Discussion
### Determination of Optimal Processing Conditions and Validation of Regression Model  
Optimal processing conditions for NFC goji juice were determined using the TSS/TA ratio and sensory evaluation scores as response indicators. The ideal parameters were: homogenisation time of 2.05 min, ultrasonication time of 2.94 min, sterilisation temperature of 102.38 \({}^{\circ}\)C, and sterilisation time of 85 s. For practical production, these parameters were adjusted to: homogenisation time of 2 min, ultrasonication time of 3 min, sterilisation temperature of 102 \({}^{\circ}\)C, and sterilisation time of 85 s. Validation experiments yielded a  
Figure 6: Surface plot and contour plot of interaction of various factors from NFC wolfberry juice (**a**,**b**). Effects of Response surface plots and contour lines on the interaction of homogenization time and sterilization temperature, (**c**,**d**). Effects of response surface plots and contour lines on the interaction of homogenization time and sterilization time, (**e**,**f**). Effects of response surface plots and contour lines on the interaction of sterilization temperature and sterilization time, (**g**,**h**). Effects of response surface plots and contour lines on the interaction of homogenization time and ultrasonication time, (**i**,**j**). Effects of response surface plots and contour lines on the interaction of ultrasonication time and sterilization temperature).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measuring Item** & **Fresh-Pressed Goji Juice** & **NFC Goji Juice** \\ \hline TSS/TA ratio & 11.91 \(\pm\) 0.2 b & 12.80 \(\pm\) 0.1 a \\ TSS (\%) & 19.9 \(\pm\) 0.15 b & 20.10 \(\pm\) 0.17 a \\ TA (g/L) & 1.67 \(\pm\) 0.2 a & 1.57 \(\pm\) 0.15 a \\ Turbidity/NTU & 1231 \(\pm\) 3.5 b & 1420 \(\pm\) 2.8 a \\ L & 11.58 \(\pm\) 0.8 a & 10.22 \(\pm\) 0.2 b \\ a & 34.68 \(\pm\) 1.8 a & 30.82 \(\pm\) 1.3 b \\ b & 19.82 \(\pm\) 0.3 a & 17.46 \(\pm\) 0.2 b \\ \(\Delta\)E & 41.59 \(\pm\) 0.5 a & 36.87 \(\pm\) 0.3 b \\ Total phenols (mg/mL) & 3.40 \(\pm\) 0.01 a & 2.60 \(\pm\) 0.02 b \\ \(\beta\)-carotene (mg/mL) & 3.02 \(\pm\) 0.1 b & 4.75 \(\pm\) 0.2 a \\ Polysaccharides (mg/mL) & 7.50 \(\pm\) 0.2 b & 9.50 \(\pm\) 0.2 a \\ Betaine (g/100 g) & 0.276 \(\pm\) 0.01 b & 0.403 \(\pm\) 0.01 a \\ _E. coli_ (MPN/mL) & Not detected & Not detected \\ Total colony count (CFU/mL) & \textless{}10 & \textless{}10 \\ \hline \hline \end{tabular} Note: Different lowercase letters in the same line indicate significant differences (\(p<0.05\)).  
\end{table}
Table 8: Quality indices of fresh-pressed goji juice and NFC goji juice.  
final TSS/TA ratio of 14.03 and a sensory evaluation score of 85.5, differing from predicted values by only 1.9% and 0.5 points, respectively. The close match between validation results and model predictions indicates the applicability of the model for optimizing NFC goji juice processing. Under these conditions, the NFC goji juice exhibited a rich red colour, glossy appearance, distinctive goji aroma, stable and uniform juice consistency, and a smooth, balanced taste of sweetness and acidity.