esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Selecting Suitable Number of Clusters  
In WSNs, the quantity of data transmission hops from the nodes decreases when there are more clusters, \(L\), which conserves sensor node energy. However, this also means that the sink node, which needs to visit more helper nodes, consumes more energy. Consequently, the available energy \(P_{charge}\) for charging the helper nodes is diminished. Conversely, as \(L\) decreases, a considerable amount of energy is retained in the sink node, leading to an increase in \(P_{charge}\) but this results in more data transmission hops from the sensor nodes, potentially worsening energy imbalances. Hence, it is crucial to find the optimal value of \(L\) that ensures the optimal utilization of energy.  
Let \(n\) represent the total number of sensor nodes, with \(p\) denoting the ratio of advanced nodes and \(f\) indicating the ratio of helper nodes. Advanced nodes start with a greater initial power reserve compared to standard nodes. Helper nodes are strategically placedto minimize energy consumption within the network. Their primary function is to gather data from the closest CHs and relay it to the helper nodes and then to the mobile sink.  
Assuming that the advanced nodes possess \(\alpha\) times more energy than the standard nodes, the total initial power for the advanced nodes can be computed as:  
\[P_{tot.adv}=n.p.P_{av}(1+\alpha) \tag{5}\]  
The collective initial energy of all helper nodes is represented as in Equation (5). Each individual helper node possesses \(v\) times more energy than a standard node. The total count of helper nodes and their combined initial energy can be expressed as follows:  
\[m=n.f \tag{6}\]  
\[P_{tot.helper}=m.P_{av}(1+v) \tag{7}\]  
The computed overall initial power for the three-tiered heterogeneous network, considering the modified scheme, is determined as follows:  
\[P_{total}=n.P_{av}(1-p-f)+n.p.P_{av}(1+\alpha)+n.f.P_{av}(1+v)=n.P_{av}(1+\alpha.p+f.v) \tag{8}\]  
The process of selecting CHs from the advanced nodes can be explained as follows: a cluster is formed using the advanced nodes, and the CHs are responsible for aggregating data from their cluster members and subsequently forwarding it to the nearest helper node then this helper node resends the collected data to the mobile sink. On the other hand, the standard nodes use the direct transmission to send their data to the fixed BS and will be deployed in circle zone, as shown in Figure 2. The proposed scheme seems to mitigate the issue of hotspots by fine-tuning data collection quantities and ensuring data collection remains within the available power range.
###### Abstract  
Thermal shock is the physical process of rapid cooling of a hot object. The physical and mechanical properties of rocks that have undergone thermal shock will change. This variation can be applied to the development of geothermal in limestone reservoirs and serve as an effective means of enhancing heat exchange capacity. The essence of this is rock thermal shock rupture. The reason for the change in rock properties due to thermal shock is distinguished from other factors and is a process of instantaneous impact thermal stress caused by non-constant heat transfer, resulting in rock damage from microscopic damage to macroscopic damage. In order to study the variation of physical and mechanical properties of limestone with heating and cooling temperatures under the effect of thermal shock, a self-developed thermal shock test device was used to test the physical and mechanical properties of limestone heated to 100 \({}^{\circ}\)C-600 \({}^{\circ}\)C after thermal shock. The results strongly suggest that the heating temperature under the effect of thermal shock is the main factor affecting the evolution of basic physical properties of limestone; with the increase in heating temperature, the color of the specimen changes from off-white to white, the mass decreases and the volume nonlinearly increases, with a maximum reduction of 1.39% in mass and a maximum expansion of 2.79% in volume at 600 \({}^{\circ}\)C. Indeed, 500 \({}^{\circ}\)C is the temperature of abrupt mass loss. The heating temperature and the cooling medium temperature act together to deteriorate the mechanical properties of limestone. With increasing heating temperature (decreasing cooling temperature), the uniaxial compressive strength of limestone decreased by 39.5% (19.3%), modulus of elasticity by 59.5% (22.9%), tensile strength by 42.9% (7.6%), and cohesion by 43.2% (22.5%). The peak strain increases by 74.2% and the angle of internal friction increases by 27% (25.9%). The above data are average values. The empirical equations of the compressive strength, modulus of elasticity, tensile strength of limestone under the action of thermal shock versus heating temperature were obtained. The differences in the physical and mechanical properties of limestone after two heat treatments (thermal shock and high-temperature heating) were compared and analyzed, and the results showed that the physical and mechanical properties of limestone deteriorated more severely after thermal shock compared to high-temperature heating. The research results can provide technical support for the drilling of geothermal development and wellbore stability assessment in limestone reservoirs, and enrich the theory of high-temperature rock mechanics.  
rock mechanics; limestone; thermal shock; physical and mechanical properties +
Footnote †: journal: Applied Sciences
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Data  
#### 2.2.1 Kbdli-Based GWT  
The _GWT_ was an essential factor for estimating \(R_{e}\) in this study. To estimate the spatially distributed _GWT_, we used satellite-based data in the computation of the KBDI. As the input data, we used LST from a multi-functional transport satellite (MTSAT) (4 km spatial resolution) and precipitation (P) from global satellite mapping of precipitation (GSMaP) (10 km spatial resolution). Since no MTSAT-based LST data in 2002-2006 were available due to the satellite's pre-observation period, we instead used MODIS LST (MOD11A1, 1 km spatial resolution) for these years. MTSAT is a geostationary satellite. MODIS sensors are onboard a polar-orbiting Terra satellite. Therefore, their observation frequencies are different. The maximum LST (\(LST_{max}\)) was calculated by using the hourly LST by MTSAT data and the daytime product by Terra MODIS data. The merit of the use of MTSAT LST is that an accurate \(LST_{max}\) could be obtained due to its high temporal observation. However, for analyzing the relationship between climatic factors and \(R_{e}\), the long-term interannual variations were also crucial. Therefore, the MODIS LST altered the MTSAT one. As an ancillary dataset, the land surface water coverage (LSWC) of the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) was used  
Figure 1: Spatial distribution of tropical peatlands in this study (colored by brown). The global forest watch database (GFW) [36] was used as a peatland map. The locations of the flux towers are marked as red circles. _UF_, _DF_, and _BF_ denote undrained forest, drained forest, and burned forest, respectively.  
with a 10 km spatial resolution for confirming the existence of surface water [21]. The LSWC was calculated by combining MODIS normalized difference water index (NDWI) and the AMSR-E daily normalized difference polarization index (NDPI) [37].
## 2 Current Capabilities of Modern FPGAs  
### FPGAs vs. ASICs  
FPGAs are made up of an interconnection of logic blocks in the form of a bidimensional array. The logical blocks consist of look-up tables (LUTs) constructed over simple memories that store boolean functions. Each LUT has a fixed number of inputs and is coupled to a multiplexor and a Flip-Flop in order to build sequential circuits. Likewise, several LUTs can be combined in order to implement complex functions.  
The FPGA architecture makes it possible to implement any combinational and sequential circuit, which can range from a simple logic function to a high-end soft-processor [12]. In order to reduce the complexity of designing FPGA systems, Hardware Description Languages (HDL) such as VHDL and Verilog HDL are used. Likewise, the vendor's synthesizer seeks for an optimized arrangement of the FPGA's resources based on the hardware description (particularly the content of the LUTs and interconnection) during the process of mapping and routing in order to generate a bitstream, which is afterwards loaded in the platform.  
In the past, FPGAs have been mainly used in signal processing and network packet analysis [13]. However, thanks to the high-speed embedded resources included in the FPGA such as DSP slices and fast memories, they are now also utilized for algorithm acceleration either as coprocessors or standalone systems, _i.e_., System-On-Chips (SoCs). Moreover, several vendors reserve space in the FPGAs to custom soft hard-IP (HIPs) processors.  
Besides the reprogramming capabilities, the usage of an FPGA as platform has several advantages in comparison to ASIC. Firstly, implementations can be updated in order to introduce a new feature, even partially and during run-time. Secondly, current FPGAs include high-speed multipliers and adders that work at the highest supported frequency. Furthermore, FPGAs track the Moore's law, _i.e_., such platforms based on SRAM provide the advantages of the latest fabrication processes of ICs in terms of area reduction and energy consumption. Finally, their costs are much lower in comparison to ASIC fabrication of custom ICs. However, ASICs have much better performance and require much less area and energy. Thus, energy consumption is nowadays the main challenge of FPGAs vendors [14]. Likewise, vendors as Xilinx are substituting platform components by optimized ASIC HIPs in order to provide a better power balance [12].
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 3 Simulated Annealing Based Resource Allocation
### SA for Proportional Power Allocation \(\delta_{n}\)
The SA computational time is 93.05 s for the system with 10 users, where the NOMA-DC is 113.5 s. Hence, SA is faster by 20.45 s than NOMA-DC because the SA reaches the optimum solution faster at the beginning and iteratively slows down to end the algorithm with limited steps. Figure 4 shows the SA configurations compared to NOMA-DC [10], FTPA [4], and OFDMA when the number of users increased from 10 to 60 users. The presented algorithms and both SA configurations achieved a lower throughput when the system tested with 10 users, and the throughput gradually increased when the number of users increased to 60 users. The cold configuration gives higher Throughput than the hot configuration when the number of users increased. The EQ-cold configuration outperforms the other schemes because only two parameters are tuned in the system model that enhanced and expedited the search for more users. The SA computational time is 116.5 s for the system with 60 users, where the NOMA-DC is 394.05 s. Thus, the SA is faster than NOMA-DC, which is one advantage of SA for a large number of users. The equal power allocations in  
Figure 3: System’s Throughput Versus Maximum Transmit Power 12 Watt for 10 Users. the cold and the hot configurations slightly outperform the other power allocations in the cold and hot configurations, hence the equal power allocation can be an optimum solution for the power allocation scheme in the NOMA system. Figure 5 shows the cold-EQ configuration converges earlier than the hot configuration when observed on the system with 60 users. Both SA configurations converge higher than NOMA-DC, but the cold configuration shows a higher convergence than the hot configuration. For a large number of users, more iterations are required for the hot configuration which needs over 2000 iterations to converge, but these amounts of iterations are enough for cold configuration. Moreover, as the number of users increase, the SA with a cold configuration performs better because it starts from a better solution. On the other hand, the NOMA-DC is very slow for a large number of users, while the SA algorithm is relatively faster. Figure 4: Throughput Versus Number of Users (10–60) with Maximum Transmission Power of 12 Watts. Figure 5: Convergence and Iterations.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Noise Models under Test
#### 4.3.2 Synchronous Impulsive Noise  
References like [1; 2; 3] describe the synchronous impulsive noise as being common in the PLC propagation channel. Since the frequency distribution of the synchronous impulsive noise is very variable, white synchronous impulsive noise has been selected for a direct comparison with respect to the background AWGN noise. Regarding the time distribution of the noise, the impulsive noise generation is carried out by modulating the amplitude of the AWGN noise with periodic square pulses, obtaining white periodic impulsive noise synchronous to the electric network, as traditionally classified in [1; 2].  
The following parameters are configurable:  
* Pulse to Base Ratio (\(PBR\)). The ratio between the pulse power (\(P_{pulse}\)) and the base power (\(P_{base}\)) of the impulsive noise, configured in dB.
* Period (\(T\)). The period between pulses can be configured in \(\mu\)s.
* Duration (\(d\)). The duration of each pulse can be configured in \(\mu\)s.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(n\) & _h[n]_ & \(n\) & _h[n]_ & \(n\) & _h[n]_ & \(n\) & _h[n]_ \\ \hline
0 & \(-\)0.04244 & 4 & \(-\)0.05787 & 8 & \(-\)0.09095 & 12 & \(-\)0.21221 \\
1 & 0.00000 & 5 & 0.00000 & 9 & 0.00000 & 13 & 0.00000 \\
2 & 0.04897 & 6 & 0.07074 & 10 & 0.12732 & 14 & 0.63662 \\
3 & \(-\)0.00000 & 7 & \(-\)0.00000 & 11 & \(-\)0.00000 & 15 & 0.00000 \\ \hline \hline \end{tabular}  
* Remaining values of \(h[n]\) defined through symmetry: \(h[n]=h[30-n]\)\(\forall n\in\mathds{N}/16\leq n\leq 30\)  
\end{table}
Table 3: Impulse response definition for abrupt phase shift channel.  
Figure 7: Impulse response of the abrupt phase shift channel.  
Figure 8 describes every configurable parameter in a graph that represents noise power in time.  
Integrating the described noise in time, the average power of the described noise (\(P_{noise}\)) is calculated in Equation (1).  
\[P_{noise}=\frac{P_{base}\left(T-d\right)+P_{pulsed}}{T}=P_{base}\left(1-\frac{ d}{T}\right)+P_{pulse}\frac{d}{T} \tag{1}\]  
Equation (1) can be extended to obtain the relation of \(P_{pulse}\) and \(P_{base}\) with \(P_{noise}\) after the configurable parameters \(PBR\), \(d\) and \(T\). These results are represented in Equations (2) and (3).  
\[\frac{P_{pulse}}{P_{noise}}=1+PBR\left(1-\frac{d}{T}\right) \tag{2}\]  
\[\frac{P_{base}}{P_{noise}}=\frac{1}{PBR}+1-\frac{d}{T} \tag{3}\]  
The synchronous impulsive noise \(PBR\) is fixed to 20 dB, whereas the period between pulses \(T\) is fixed to \(10^{4}\)\(\mu\)s. These parameters correspond to a 50 Hz network with powerful white periodic impulsive noise in both zero crossings, based on the experience of field deployments of ZIV devices. The duration of the pulse \(d\) remains variable.
###### Abstract  
Artificial neural networks (ANNs) have been considered for assessing the potential of low GWP refrigerants in experimental setups. In this study, the capability of using R449A as a lower GWP replacement of R404A in different temperature levels of a supermarket refrigeration system is investigated through an ANN model trained using field measurements as input. The supermarket refrigeration was composed of two indirect expansion circuits operated at low and medium temperatures and external subcooling. The results predicted that R449A provides, on average, a higher 10% and 5% COP than R404A at low and medium temperatures, respectively. Moreover, the cooling capacity was almost similar with both refrigerants in both circuits. This study also revealed that the ANN model could be employed to accurately predict the energy performance of a commercial refrigeration system and provide a reasonable judgment about the capability of the alternative refrigerant to be retrofitted in the system. This is very important, especially when the measurement data comes from field measurements, in which values are obtained under variable operating conditions. Finally, the ANN results were used to compare the carbon footprint for both refrigerants. It was confirmed that this refrigerant replacement could reduce the emissions of supermarket refrigeration systems.  
HFC phase-down; energetic performance; ANN; COP; TEWI
# MFEM: Multi-View Feature Enhanced Attention Model for Image Captioning
## 4 Experiments
### Discussion  
The results presented in Table 1 demonstrate that MFEAM achieves state-of-the-art performance across all major evaluation metrics, including BLEU, METEOR, ROUGE, CIDEr, and SPICE. In particular, MFEAM attains a CIDEr score of 140.1, outperforming strong baselines such as BENet (137.6), RSTNet (135.6), and DLCT (133.8). This improvement can be attributed to the dual-channel attention refinement strategy introduced by the multi-view feature enhanced attention, as well as the decoupled decoding structure in the student model.  
In addition to the quantitative gains, qualitative results in Figure 5 further illustrate the effectiveness of MFEAM in generating accurate, context-aware, and semantically rich captions. Compared to CaMEL, MFEAM produces descriptions with smaller semantic deviations and better alignment with spatial and relational cues in the image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **MFEAM** & **B-1** & **B-4** & **M** & **R** & **C** & **S** \\ \hline CLIP-VIT-B/16 & - & 77.8 & 38.0 & 29.1 & 58.0 & 122.5 & 21.8 \\ CLIP-VIT-B/16 & \(\surd\) & **78.8** & **38.9** & **29.2** & **58.6** & **125.7** & **22.0** \\ \hline CLIP-VIT-B/32 & - & 76.5 & 36.7 & 28.1 & 56.9 & 118.1 & 21.0 \\ CLIP-VIT-B/32 & \(\surd\) & 76.5 & **36.9** & 27.9 & 56.9 & **119.7** & **21.1** \\ \hline CLIP-RN50 & - & 75.7 & 36.2 & 28.1 & 56.7 & 115.7 & 20.8 \\ CLIP-RN50 & \(\surd\) & **76.3** & **36.6** & **28.2** & **56.8** & **116.2** & 20.7 \\ \hline CLIP-RN101 & - & 76.5 & 37.2 & 28.4 & 57.2 & 118.6 & 21.2 \\ CLIP-RN101 & \(\surd\) & **77.1** & **37.4** & **28.5** & **57.5** & **119.0** & 21.1 \\ \hline CLIP-RN50\(\times\)4 & - & 77.2 & 37.6 & 28.7 & 57.6 & 121.9 & 21.6 \\ CLIP-RN50\(\times\)4 & \(\surd\) & **77.9** & **38.3** & 28.6 & **58.0** & **122.4** & **21.7** \\ \hline CLIP-RN50\(\times\)16 & - & 78.0 & 38.8 & 29.4 & 58.6 & 125.0 & 22.2 \\ CLIP-RN50\(\times\)16 & \(\surd\) & **79.2** & **39.7** & 29.3 & **59.1** & **127.3** & **22.4** \\ \hline \hline \end{tabular} Note: “\(\surd\)” indicates that the proposed MFEAM architecture is used, while “-” indicates that a single language model serves as the language decoder.  
\end{table}
Table 4: Results of different image feature extraction modules when training with cross-entropy loss.  
Furthermore, compared with recently proposed methods, MFEAM offers a competitive advantage by achieving high caption quality without relying on external data or excessive model complexity. These results confirm the effectiveness of our framework in bridging visual and textual modalities while maintaining computational efficiency.
## 1 Introduction  
Technology can help people maintain lasting and sturdy connections with others. Technology has also made connections across distances more immediate and similar to those in real life, by including more mediums (e.g., audio, visual). In doing so, it can support more natural social connections. Older adults (age 65 and above) have seen socially connective technology evolve from pen-and-paper letters to phone calls to video calls, and most recently, mobile telepresence robots.  
Mobile telepresence robots are movable robots that make video conferencing versatile [1]. People operating the robots at a distance, or \"pilots\" [2, 3, 4], can call into people housing the robots, or \"hosts\". Through the robot, they can experience a two-way video and audio connection as they drive the robot in open spaces that are clear of barriers. Because these robots allow pilots to physically move around their environments, they increase perceived presence in social interactions, and people can gain more benefits (e.g.,perceived social support, reduced loneliness) from interacting with loved ones through this medium [5; 6; 7; 8]. However, to do so, users (pilots and hosts) need to be able to accept and use them over the long term.  
In this paper, we examined host participants' acceptance of telepresence robots through seven acceptance phases in the phased framework of technology acceptance (PFTA; [9]). We ran a seven-month study of older adults using mobile telepresence robots for social connection with loved ones, and our prior papers describe participants' use, frustrations, and recommendations for the robots overall [10] and how participants on average progress through the acceptance phases [11]. However, we found that progression was more nuanced than prior studies reported. Therefore, in this paper, we more precisely examine how each of the four older adults moved through the acceptance phases and how researchers can more accurately determine when a given participant has entered a specific acceptance phase. We study what characteristics of participants, and what contexts of use, aid and hinder older adults in accepting these mobile telepresence robots in the long term. Results will help researchers and practitioners help users move towards a more complete acceptance of technology that has great potential to benefit them.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 4 Receding Horizon Control with Temporal Logic Planning
### Complexity  
From Algorithm 1, one can see that the state number of the production automaton has an impact on the algorithm complexity. Let \(|\phi|\) be the length of the formula \(\phi\) where all the symbols and operators are included. Based on the LTL formula, the Buchi automaton includes, at most, \(|\phi|\times 2^{|\phi|}\) states [11]. Hence, the size of the production automaton is bounded by \(|Q_{P}|\times|\phi|\times 2^{|\phi|}\). Moreover, \(F_{P}\) is the number of acceptable states, and the overall complexity of the offline portion of Algorithm 1 is \(O(|F_{P}|^{3}+|F_{P}|^{2}+(|\phi|\times 2^{|\phi|})^{2}\times|F_{P}|)\) according to [11]. It should be pointed out that, due to constructing the product automaton offline, the computational complexity does not influence the real-time computation of Algorithm 1.  
On the other hand, the in-line complexity of Algorithm 1 is highly dependent on the horizon \(N\), the number of obstacles \(M\) and the maximal changing number of positions of signal sources \(\delta P\). Let the maximal number of transitions at each state in the production automaton be \(\delta Q\). Hence, the complexity of the in-line portion of Algorithm 1 is bounded by \(\delta P\times(\delta Q)^{MN}\) and is determined by the used searching algorithm. Moreover, the computational latency can be relaxed by using a more efficient graph search algorithm such that the optimal trajectory can be found from exponential time to polynomial time. Further, by dividing the whole region, each region can consist of the lesser states so as to reduce the search time.