esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Smart metering IoT applications are among the most energy-critical in the current panorama. Metering sensors are battery-powered and are expected to have a lifetime exceeding ten years. In order to achieve such long operation duration, a generic header compression mechanism named Static Context Header Compression (SCHC) has been introduced and accepted as a standard by the Internet Engineering Task Force (IETF). This paper aims to demonstrate the energy savings enabled by the use of SCHC on a cellular IoT network by the means of real-life implementation and measurements. Experiments are conducted in a controlled environment for different scenarios and considering multiple parameters such as message size and radio conditions. Measurements show the high impact of this header compression mechanism, particularly when the radio conditions are bad and repetitions are used to improve the reliability of the transmission: a reduction of up to 40% in energy consumption is observed. Using SCHC over the non-IP transport mode (NIDD) of NB-IoT compared to the legacy IP mode also enables significant energy savings and allows the latency to be reduced while maintaining the interoperability provided by the IP layer.  
smart metering; LPWAN; NB-IoT; SCHC; energy efficiency
## 6 Conclusions  
1. The equivalent force on the back of the concrete retaining wall was calculated based on the Rankine earth pressure theory. The concrete retaining wall was regarded as a four-sided fixed support plate, and the ultimate load was solved by establishing the virtual work equation. A novel method for determining the safe thickness of the concrete retaining wall was proposed, and quantitative criteria for the safe thickness of concrete retaining wall were given.
2. The theoretical formula derived in this paper was compared with the thickness of the concrete retaining wall obtained by the traditional empirical formula and the Kalmykov formula. The traditional empirical formula ignores the formation stress and groundwater pressure behind the concrete retaining wall. The Kalmykov formula has poor applicability when the final grouting pressure is small or large, and the influence of groundwater pressure is not considered. By considering the impacts of different working conditions on the concrete retaining wall, the rationale of the safe thickness of the concrete retaining wall obtained in this paper was further clarified.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Thickness /m** & **Condition 1 (\(P_{j}\) = 0.6 MPa)** & **Condition 2 (\(P_{j}\) = 1 MPa)** & **Condition 1 (\(P_{j}\) = 1.4 MPa)** \\ \hline Empirical formula solution & 0.52 & 1.04 & 0.67 & 1.34 & 0.80 & 1.60 \\ Theoretical formula solution & 0.93 & & 1.15 & & 1.33 & \\ Experimental design & Left tunnel & 0.50 & Left tunnel & 0.70 & Left tunnel & 0.80 \\  & Right tunnel & 0.90 & Right tunnel & 1.20 & Right tunnel & 1.30 \\ \hline \hline \end{tabular} (1)  
* For the right tunnel, in the three working conditions, except for slight local deformation of the concrete retaining wall during a certain period of grouting, no water seepage, cracking, or other accidents occurred, which verifies the safety of the thickness of the concrete retaining wall obtained using the theoretical formula.
* The concrete retaining wall was damaged to varying degrees under the three working conditions for the left tunnel. To avoid accidents, grouting must be stopped to further strengthen the concrete retaining wall. Thus, the concrete retaining wall did not fulfill its purpose or meet its requirements. Yet, the rationality of the safe thickness of the concrete retaining wall obtained by the theoretical formula was indirectly verified.  
In summary, the method for determining the safe thickness of the concrete retaining walls established in this paper is reasonable and has reference significance for similar projects.
## 3 Coupled Modal Analysis of Ring-Stiffened Conical Shell with Bases
### Coupled Modal Analysis  
#### 3.2.1 Acoustic-Structure Coupled Modal Analysis  
Prior to the acoustic radiation analysis, the acoustic finite element mesh is introduced and coupled with structural mesh. The acoustic-structure coupling modal analysis of the ring-stiffened conical shell system is carried out. The first eight-order coupled modes of the ring-stiffened conical shell are presented in Figure 3. As shown in Figure 3, compared with modes of the shell in vacuum, an obvious influence of the fluid medium can be seen. The first two-order vibrations still occur on the shell. However, compared with the vibration in  
Figure 1: Finite element model of the ring-stiffened conical shell with bases: (**a**) axonometric drawing; (**b**) cutaway view.  
Figure 2: Fluid–structure coupled finite element model.  
vacuum, the vibration amplitudes of the shell and bases decreases greatly. The third-order and fourth-order vibrations are presented as the combined vibration of the bases and shell, and the amplitudes of them are basically the same. The fifth-order and sixth-order vibrations are mainly the base vibration, while the seventh-order is mainly the vibration on the shell. This is quite different from the modal shape in vacuum. It can be seen that the vibration of the whole structure is greatly affected by the added mass of water medium. Both the natural frequency and the modal shapes are greatly changed and the amplitudes of vibration are significantly decreased.  
Figure 3: _Cont._  
The natural frequency in a different medium is shown in Table 1. Significant differences are found between the natural frequency in vacuum and in water. Compared with the natural frequency in vacuum, the natural frequency in water decreases significantly, and each order natural frequency decreases by around 100 Hz.
###### Abstract  
The underwater acoustic sensor network (UWASN) is a system that exchanges data between numerous sensor nodes deployed in the sea. The UWASN uses an underwater acoustic communication technique to exchange data. Therefore, it is important to design a robust system that will function even in severely fluctuating underwater communication conditions, along with variations in the ocean environment. In this paper, a new algorithm to find the optimal deployment positions of underwater sensor nodes is proposed. The algorithm uses the communication performance surface, which is a map showing the underwater acoustic communication performance of a targeted area. A virtual force-particle swarm optimization algorithm is then used as an optimization technique to find the optimal deployment positions of the sensor nodes, using the performance surface information to estimate the communication radii of the sensor nodes in each generation. The algorithm is evaluated by comparing simulation results between two different seasons (summer and winter) for an area located off the eastern coast of Korea as the selected targeted area.  
underwater acoustic sensor network; performance surface; virtual force-particle swarm optimization; optimal deployment +
Footnote †: journal: Sensors
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 7 Discussion
### Assumptions That Influence Software Defect Prediction
Since the naturalness assumption has been proved in software defect prediction via neural language models, and the code pattern assumption has also been proven by various researchers in deep learning-based software defect prediction, it is worth investigating the relationship between the two assumptions. From our perspective, these two assumptions are demonstrated independently in the existing experimental design. For example, the use of AST nodes and AST paths proves that simplifying source code and highlighting components that are more related to defects can improve prediction performance in software defect prediction. The reason behind it may be that adding redundant information introduced by grammar definitions does not help improve prediction performance for software defect prediction. However, the CodeBERT model does not take advantage of ASTs and performs tokenization directly on the source code, which is generally accepted in text processing but can be improved in programming language processing. If a programming language model could be pre-trained using AST sequences or AST paths as input, it is more likely that the code pattern assumption and naturalness assumption can be combined in experimental design to further improve prediction performance. Figure 8: Assumptions that influence software defect prediction.
## 4 Results and Discussion  
This section contains the findings from the tests that were carried out and the evaluations of PV-ISM are presented. We include in-depth specifications of the computational environment that we used for this research to ensure the reproducibility of our studies. The experiments were conducted on a system running Ubuntu 22.04.5 LTS (Canonical Ltd., London, UK) as the operating system, equipped with an AMD Ryzen 9 7900X CPU (Advanced Micro Devices, Inc., Santa Clara, CA, USA), and GeForce RTX 3090 24G GPU (NVIDIA Corporation, Santa Clara, CA, USA), supported by 32 GB of RAM. The development environment was based on Python 3.10.12, and the primary deep learning framework utilized was TensorFlow 2.16.0-rc0 (Google LLC, Mountain View, CA, USA). For GPU acceleration, CUDA 12.2 was used as the communication agent between the TensorFlow framework and the GPU, with the NVIDIA driver version 535.183.01 specifically installed to ensure compatibility and performance. Throughout the evaluation process, accuracy (\(ACC\)), defined in Equation (10), is employed as the primary metric for comparison. To guarantee a thorough evaluation of model performance, in addition to (\(ACC\)), precision (\(P\)), recall (\(R\)), and F1 score (\(F1\)), given in Equations (11)-(13), are also provided in the results. \(R\) is the percentage of true positive cases that the model accurately recognized, whereas \(P\) indicates the percentage of cases that were projected to be positive and turned out to be positive. The \(F1\) score provides an accurate evaluation of recall and precision by taking the harmonic average of the two factors. These metrics, which are referred to above, are derived from the confusion matrix, which has four main elements. Accurate predictions  
Figure 9: Implementation of ResNet model. are indicated as either true negative (\(TN\)), which indicates that negative cases are correctly identified, or true positive (\(TP\)), which indicates that positive cases are correctly discovered. Conversely, false positive (\(FP\)) and false negative (\(FN\)) are examples of classification errors, where a negative case is mistakenly predicted as positive and a positive case is mistakenly predicted as negative. \[ACC\ =\ \frac{TP+TN}{TP+FP+TN+FN} \tag{10}\]  
\[P\ =\ \frac{TP}{TP+FP} \tag{11}\]  
\[R\ =\ \frac{TP}{TP+FN} \tag{12}\]  
\[F1\ =\ \frac{2 xPxR}{P+R} \tag{13}\]  
The analytical outcomes and performance metrics obtained from the implementation of PV-ISM on the CIFAKE dataset are presented. To address a wide range of potential scenarios, the model parameters were systematically adjusted to determine the most effective configuration. Several significant hyperparameters utilized throughout the study were the learning rate scheduler (LRS), batch size (BS), epoch number (EN), test set percentage (TSP), image resize (IS), patch size (PS), and transformer block (TB). It was observed that the model demonstrated satisfactory performance even without fine-tuning. However, to ensure the stability and generalizability of the model, parameter optimization was conducted, and notable improvements were identified and are discussed in Table 2.
## 4 Discussion
### Business Sectors  
It is rational that different sectors have varying potentials to influence the environment, and the conditions to conduct GPP in sectors are also diverse. With limited budgets and resources, prioritizing some sectors before others is an efficient strategy for public procurement. In our data and variables, ten important sectors were identified, according to their contract volume and EU's decisions.  
First, it was evaluated that government construction, including materials and infrastructure, accounts for 28% of total emissions for the construction sector and for 12% of government emissions [6]. Construction is regarded as one of the most important sectors for conducting GPP theoretically. From our analysis, architectural, construction, engineering, and inspection services sectors have a larger possibility (by 2.3%) of developing green contracts than other sectors in total samples. However, the construction work sector has a lower possibility (by 6.4%) of green contracts in a sample of services. Compared with a significant share of the contract value and the environmental impact of constructions,it is not sufficient to tap the potential of GPP in the construction sector. More effort is needed to design and implement GPP in the construction sector.  
In addition, transport, food, and electrical products are three other sectors recognized as essential sectors for promoting GPP. There are two sectors within the transport sector: the transport equipment/auxiliary products to transportation, and transport services (excl. waste transport). We obtained a positive estimated marginal effect (0.058 or 5.8%) for transport equipment, but not for transport services. The food sector was estimated to possess higher green likelihood by 8.4% than other sectors, but no significant results were found for electrical products. It is implied that GPP in the transport equipment and food sectors has been promoted to some extent, especially in product subsamples, while no evidence supports good implementation in the transport service sector.  
The medical equipment, pharmaceuticals and personal care products, and the health and social work service sectors were sectors with large contracting values in European countries in 2018. Their estimated effects on green contracts are both negative and larger than 0.05 (or 5%). This may demonstrate that in these sectors, it is substantially difficult to promote GPP. Green criteria are indispensable in GPP to convey environmental requirements to the market. Nevertheless, it seems difficult to embody green criteria into medical products, and health and social services.
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group
### Results  
#### 5.1.1 Analisys and Characterization of Ligands  
The theoretical study of the ligands seeks to determine their response as electron acceptors. They can be caracterized through the electrophilicity and the charge distribution in the SOMO. For the SHE study, the ligands are visualized as decomposed into three basic fragments (B, Q, and R), as shown in Figure 14. The fragment \"B\" is identified with bipiridine, \"Q\" with the quinoxaline group, and \"R\" with the anchor.  
In Figure 15, the contribution of the different fragments to the frontier orbitals is shown. The HOMO orbitals contain the B fragment, while the LUMOs are dominated by the Q fragment. In LUMO + 1, B or Q contributions are observed, depending on the substituent R.  
Looking for deeper information in regard to the properties of the ligands, two parameters were visualized and analyzed: electrophilicity and charge distribution.  
The effect caused by R = OH is interesting since this group lowers the acceptor capacity of the ligand, which is in accordance with its electron donor behavior.  
Charge Distribution in the SOMO  
A basic characteristic for a ruthenium complex to act as a sensitizer is that it has an MLCT absorption band in the visible spectrum. This absorption band is composed ofelectronic transitions of the type d \(\rightarrow\pi^{\ast}\) involving an excitation from an occupied atomic orbital of the metal to the empty LUMO of the ligand. The incoming charge is distributed throughout the different fragments that make up the SOMO orbital.  
Figure 17 shows that for the reference dcbpy ligand, 29% of the incoming charge is on the carboxylated moiety (R) and 71% on bipyridine. For the ppl ligand (R = H), a different pattern is observed because an important fraction of the charge is concentrated in the Q fragment and not in R. The participation of the anchor group increases according to the acceptor capacity of the anchor. The behavior is different when R = OH since most of the charge tends to concentrate in the B (bipyridine) fragment.  
Figure 16: Electrophilicity index for R\({}_{2}\)ppl ligands.  
Figure 17: Charge distribution on B, Q, R fragments in the SOMO orbital.
## 5 Conclusions  
This paper designs and investigates a satellite-ground-underwater OWC system based on OAM technology with 4 \(\times\) 4 \(\times\) 10 Gbps transmitting nodes. Based on the results of the analysis, it can be concluded that OAM using different LG modes offers significant benefits in all three scenarios within space, air, and the ocean for enhancing the range and quality of transmissions, the data rate, and the channel capacity [32]. With a BER of 10\({}^{-9}\), a maximum satellite-to-ground transmission distance of 21,500-30,000 km can be achieved. Considering weak, moderate, and strong scintillation effects, a maximum transmission distance of 12-27 m can also be achieved for ground-to-underwater communication in pure sea, clear ocean, coastal ocean, and harbor water. Additionally, the system provides a high gain, NF, received signal, and SNR in 5 m underwater communications of \(-\)75.02 dB, 75.02 dB, \(-\)78.32 dBm, and 21.67 dB, respectively. This proposed satellite-ground-underwater OWC system demonstrates wide and open eye patterns for an underwater range of 5-30 m. Additionally, this design offers an optimum performance compared to other existing designs. It will be possible to implement this system in the future for satellite-to-ground transmissions with high bandwidths and long ranges, and vice versa.  
[PERSON] and [PERSON] discussed the plan and agreed on it. [PERSON], drafted designs for the manuscript. The manuscript original was written by [PERSON] Manuscript edited by [PERSON]. They all reviewed and commented on the original draft of the manuscript. All authors have read and agreed to the published version of the manuscript.  
[PERSON] wants to thank CTC for providing the resources to conduct this research.  
Not applicable.  
Data availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 1 Introduction  
Climate change on a global scale constitutes a profound challenge, evidenced by a rise in the mean global temperature, an escalation in the frequency of severe meteorological occurrences, and an elevation of sea levels. This alteration is predominantly attributable to the increased concentrations of greenhouse gases in the atmosphere. According to data from Our World in Data (Figure 1), coal, oil, and natural gas are the primary sources of global CO\({}_{2}\) emissions. The combustion of fossil fuels and deforestation are primary contributors to these emissions. These gases create a greenhouse effect in the atmosphere, trapping solar radiation and elevating Earth's surface temperature. The consequences of climate change are profound, affecting natural ecosystems and human societies alike, with impacts on agriculture, water resources, marine ecosystems, and health.  
For this reason, countries around the world are working together to reduce greenhouse gas emissions. On the one hand, there is a need to advocate for the use of renewable energy sources and to adopt adaptation strategies. Another viable measure is energy conservation. According to a study by [PERSON], [PERSON] [1], buildings account for as much as 40% of global energy consumption, and this figure is expected to continue to grow in the coming years.  
In reaction to this, countries across the globe are making concerted efforts to diminish the emission of greenhouse gases, advocate for the utilization of renewable energy sources, and adopt strategies aimed at adaptation [2].  
The demand for buildings has evolved from aesthetic and comfort considerations to prioritizing green and smart environmental features. Low-carbon smart buildings representa synthesis of green and intelligent design, utilizing intelligent controls, data analytics, and statistical measures. Leveraging cloud computing and artificial intelligence, these buildings optimize structure, systems, services, and management to meet user needs, maximizing energy efficiency and providing an eco-friendly, comfortable living environment.  
Nevertheless, when developing deep learning models for the prediction of energy consumption in buildings, it is imperative to account for the intricate interrelations between energy usage and a multitude of factors. These include temporal aspects, climatic conditions, the occurrence of public holidays, and historical data pertaining to energy consumption. Traditional temporal prediction models, such as Convolutional Neural Networks (CNN), often struggle with vanishing gradients and information dilution. Temporal Convolutional Networks (TCNs) offer superior long-term dependency capture. The present manuscript introduces an innovative methodology that integrates a Temporal Convolutional Network (TCN) with self-attention mechanisms and gating control techniques to augment the precision of energy consumption forecasts. This model aims to closely align predicted values with actual data, facilitating its application in practical digital twin modeling.
## 3 Results and Discussion
### Isothermal Analysis
#### 3.3.6 Tempkin Isotherm  
The Tempkin isotherm model was applied. Consequently, linear plots of (\(q_{e}\)) vs. (In \(C_{e}\)) allowed for the calculation of the Tempkin isotherm parameters \(K_{T}\) and \(b_{T}\) from the slope and intercept, respectively, as presented in Figure 17 and Table 2. The data indicate that the Tempkin isotherm model applies to the adsorption of IV2R dye onto MWCNTs, as shown by the high linear regression correlation coefficient (R\({}^{2}\) = 0.965).