esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Techniques  
Figure 1 details the proposed techniques. First, Lasso (least absolute shrinkage and selection operator) is adopted to identify key predictors that significantly affect the sales revenues of Walmart, Costco, and Kroger. Then, machine learning is applied to conduct sales forecasting. Second, the LVM (Lotka-Volterra model) is used to analyze market dynamics between the three retailers and estimate their stable equilibriums. Lastly, DEA (data envelopment analysis) is applied to derive operational efficiencies and indicate necessary actions for the improvement of inefficient firms. Without loss of generality, MARS (multivariate adaptive regression splines), SVR (support vector regression), and DNN (deep neural network) are adopted in sales forecasting.  
Specifically, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to measure forecasting errors [23; 29; 39]:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{e_{i}}^{2}}}}, \tag{1}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{{{|e_{i}|}}}, \tag{2}\]  
\[\text{MAPE}=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|\frac{{{e_{i}}}}{{{y_{i}}}} \right|}, \tag{3}\]  
where \(n\) denotes the number of observations, and \({e_{i}}={F_{i}}-{y_{i}}\) is an error measured between a predicted value (\({F_{i}}\)) and the real data (\({y_{i}}\)).
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### Resource Scheduling  
In long-term evolution (LTE), especially for non-real-time (NRT) or IoT applications, the discontinuous reception (DRX) mechanism has been implemented to optimize the end-users' battery lifetime. However, power consumption and wake-up delays are always trade-offs for end users. Consequently, the optimization of the DRX mechanism is required. A modified DRX Algorithm targeting NRT applications is suggested. This latter aims at improving battery life whilst achieving permissible delays. Without the DRX mechanism, user equipment had to continuously be in the 'ON' state, even if there is no traffic, which increases battery consumption drastically. The algorithm describes the communication state between BS and IoT applications. An inactivity timer starts after the end of the 'ON' state (active data transfer). When the inactivity timer advances to its expiration with no UL/DL data transfer request, the sleep mode mechanism will start again.  
The current DRX model initiates through short sleep time (\(\tau_{S}\)). If it can end successfully without any UL packet arrival or DL packet arrival during paging, long sleep time (\(\tau_{T}\)) initiates. On the other hand, the proposed DRX mechanism initiates a sleep time interval that keeps growing from a short to long cycle timer with a step size \(\tau_{ST}\). If, throughout this time, data transfer requests arrive, the fall will be followed by a rise again. Here, a number of cycles (\(n\)) are needed to reach from \(\tau_{S}\) to \(\tau_{L}\) or vice versa. However, once the UL packet transfer request has been detected within the DRX period or the DL/UL packet transfer request has been detected within the paging interval, an instant on period timer will initiate, and the sequence will keep rotating. The algorithm of the proposed DRX mechanism is depicted in the flowchart of Figure 3.  
Here, an M/G/1 queue model has been used to model the packet arrival and service rate, Markovian (Poisson process) for the packet arrival rate (\(\lambda\)), general distribution for service rate (\(\mu\)), and the server is singular. The equation for calculating the percentage of power-saving calculation has been modified from [55]:  
\[P_{Save}(\%)=[1-[\frac{E[T_{Data}]}{E[T_{Total}]}+\frac{P_{Watt}\times E[T_{ Watt}]}{P_{Data}\times E[T_{Total}]}+\frac{P_{OFF}\times E[T_{OFF}]}{P_{Data} \times E[T_{Total}]}]]\times 100 \tag{11}\]
## 6 Conclusions and Future Work  
In this paper, we proposed an integrated framework for addressing major challenges in IoT-edge systems using FedProx, integrated with ontology-driven data standards. The proposed technique will enhance the model training process in a decentralized environment with assured smooth semantic interoperability among various datasets. From the testresults, FedProx is showing better performance with higher accuracy, faster convergence, and lower transmission costs compared to FedAvg. Moreover, the ontology alignment process is successful in 95% of cases, while support for dynamic updates is possible without losing efficiency; data heterogeneity is also efficiently handled. The scalability of the framework has been tested by using larger datasets and an increasing number of edge servers, with positive results showing its resilience and appropriateness for extended IoT networks. These results allow us to consider its potentiality for real-time, privacy-preserving data analysis in applications like environmental monitoring and smart cities.  
Future studies will focus on how to dynamically update mechanisms within the ontology, improve communication effectiveness of federated learning, and also optimize the framework for seamless real-time deployment across large-scale IoT networks. All these endeavors will definitely enhance the efficiency and adaptability of the suggested system, thus allowing the development of more creative and scalable IoT-edge solutions.  
Methodology, C.K.; Formal analysis, S.B.; Conceptualization, A.Z.; Writing--original draft, A.B.; Writing--review & editing, C.K and E.H.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study.  
The authors declare no conflict of interest.
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
###### Abstract  
Soil thermal conductivity is an important parameter for understanding soil heat transfer. It is difficult to measure in situ with available instruments. This work aims to propose a numerical model to estimate the thermal conductivity from the experimental measurements of soil heat flux and soil temperature. The new numerical model is based on the Fourier Law adding a constant empirical parameter to minimize the uncertainties contained in the data from field experiments. Numerically, the soil thermal conductivity is obtained by experimental linear data fitting by the Least Squares Method (LSM). This method avoids numerical indetermination when the soil temperature gradient or soil heat flux is very close to zero. The new model is tested against the different numerical methodology to estimate the soil heat flux and validated with field experimental data. The results indicate that the proposed model represents the experimental data satisfactorily. In addition, we show the influence of the different methodologies on evaluating the dependence of the thermal conductivity on the soil water content.  
Keywords:fourier law; gradient method; soil heat flux; soil temperature; soil thermal conductivity; soil water content +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Rotation search and point cloud registration are two cornerstones in computer vision, robotics, remote sensing, and photogrammetry. The former has found broad applications in satellite attitude estimation [1], image stitching [2], etc., while the latter has been widely applied in 3D scene reconstruction and modeling [3, 4, 5, 6], object localization and recognition [7, 8, 9], medical imaging [10], Simultaneous Localization and Mapping (SLAM) [11], archeology [12], etc.
## 1 Introduction
In general, there are few experimental studies focusing on the combination of dynamic and static loads on coal and rock under realistic stress conditions for coal mines. Rock failure is the result of initiation, propagation and coalescence of internal cracks under loading. The understanding of fracture and deformation evolution of specimens is of importance to describe the damage and failure process of rock materials [40]. Many scholars have applied a variety of measurement methods to investigate the fracture evolution, such as scanning electron microscope (SEM) [41], X-ray computed tomography (CT) [42] and electromagnetic radiation (EMR) [43]. In most cases, rock instability and failure are caused by the development of deformation localization [44]. Therefore, the digital image correlation (DIC) technique has been used to study the deformation localization of rock specimens. As a non-contact optical method, the DIC technique can directly give full-field displacement and calculate the strain on a specimen surface. The DIC technique has been widely used in laboratory tests because of its advantages of real time, covering full-field, online and flexibility. [PERSON] et al. [45] investigated the failure mechanisms of rock specimens with flaws under superimposed static and dynamic loads by using a modified SHPB device, and the cracking process was record in real time by DIC technique. Similarly, [PERSON] et al. [46] used the DIC technique to investigate the fracture process of a sandstone under three-point bending with a variety of notch positions. [PERSON] et al. [47] also employed the DIC technique to study the crack propagation process of rock-concrete interfaces under three-point bending. Most of the studies were conducted based on either static or dynamic loading condition. However, few studies have investigated failure characteristics, damage monitoring and precursor identification recognition of roof rocks under superimposed static and dynamic loading conditions. In this study, we carry out superimposed static and dynamic loading tests at medium strain rates on roof rocks from the Zhaolou coal mine, Shandong Province, China. We apply the DIC technique to capture a range of failure patterns in specimens under superimposed static and dynamic loads. We analyze the mechanical and energy concentration, dissipation behavior and explore the damage evolution and crack propagation process based on the global strain field collected by the DIC technique.
###### Abstract
Based on the above, numerical approaches are preferred over experimental methods for studying the long-term behavior of CPCR bridges and better understanding the effects of different damage parameters such as concrete cracking, creep, and prestress loss on the long-term deflection of CPCR bridges. To investigate the primary cause of the long-term deformation of long-span CPCR bridges, this paper first presents a refined numerical modeling approach for the accurate modeling of CPCR bridges. Then, the validated refined model is utilized to determine the change law of the deflection curve of the main span under four performance degradation behaviors. Finally, an investigation is conducted into the local stress and deformation redistribution caused by box girder cracking, concrete creep, joint damage behavior and prestress loss. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bridge Name** & **Span Distribution (m)** & \
\begin{tabular}{c} **Completion** \\ **Year** \\ \end{tabular} &
\begin{tabular}{c} **Country** \\ **Deflection (cm)** \\ \end{tabular} \\ \hline Kingston Bridge & 62.5 + 143.3 + 62.5 & 1970 & United Kingdom & \textgreater{}30 \\ Kono-Babeldaob Bridge & 72 + 241 + 72 & 1978 & Palau & 120 \\ Parrotts Bridge & 99 + 195 + 99 & 1979 & United States & 63.5 \\ Sanmencia Yellow River Highway Bridge & 105 + 4 \(\times\) 160 + 105 & 1992 & China & 22 \\ Stovset Bridge & 220 (Main span) & 1993 & Norway & 20 \\ Jinsha Bridge & 66 + 120 + 66 & 1994 & China & 22 \\ Huangshi Yangtze River Bridge & 62.5 + 3 \(\times\) 245 + 62.5 & 1995 & China & 30.5 \\ Jiangjin Yangtze River Bridge & 140 + 240 + 140 & 1997 & China & 31.7 \\ Auxiliary Channel of Humen Bridge & 150 + 270 + 50 & 1997 & China & 22.2 (2003) \\ Stolma Bridge & 94 + 301 + 72 & 1998 & Norway & 9.2 (2001) \\ Auxiliary Channel of Yajisha Bridge & 86 + 160 + 86 & 2000 & China & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Engineering examples of the excessive deformation of continuous rigid-frame bridges.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Communication Technologies
#### 4.3.2 Mobile Telecommunication  
In 2010, 4G was first launched by Telia Sonera in Finland, offering significant improvements over 2G and 3G through higher data transfer rates, better security, and advanced technologies like LTE-Advanced and Wi-Max [93]. It uses OFDMA for efficient spectrum use, MIMO for enhanced signal performance, and MU-MIMO to improve multiuser connectivity. However, LTE supports both FDD and TDD for seamless broadband wireless service. Also, 4G enables high-speed internet access, multimedia streaming, and mobile network interoperability, albeit introducing security risks such as IP vulnerabilities, VoIP spam, and potential network-wide failures due to shared core infrastructure [93]. It revolutionized ITS by enabling real-time vehicle communication, Smart Infrastructure, enhanced traffic management, and improved safety measures.  
[PERSON] et al. [94] indicated a 4G-based V2I road data communication mechanism that supports a publish/subscribe paradigm to multicast road safety messages according to the user's location. It introduces an advanced filtering method based on the traveling road and the user's heading direction to filter the most relevant road events. The authors used Global System for Mobile Communications (GSM) to transfer EDR sensor data to a server and subscribe to configuration settings from an MQTT broker version 3.1. The system backs up sensor data and allows users to access them when the EDR breaks during an accident. However, 5G is better for ITS. Experiments in [95] demonstrated that 5G outperforms 4G in speed, latency, capacity, and efficiency, making it ideal for real-time applications like autonomous driving, remote surgery, and IoT. Also, 5G achieves up to 20 Gbps, compared to 150 Mbps in 4G, and reduces latency from 30 to 50 ms to just 1 ms, enabling Ultra-Reliable Low-Latency Communication (URLLC) applications. Additionally, it supports up to 1 million devices per square kilometer, making it far superior in high-density environments. Technologies like massive MIMO, beamforming, and millimeter-wave frequencies further improve spectral efficiency, network slicing, and reliability, whereas 4G struggles with congestion and scalability.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 3 Method  
In this paper, a channel soft attention improved sequential video VLAD (SeqVLAD) [6] with a semantic reconstruction network (CSA-SR) for video captioning is proposed. The overall structure is shown in Figure 1. The proposed CSA-SR mainly contains three parts, including encoder, decoder, and semantic reconstructor. The encoder extracts video features and encodes features for the decoder. Frame-level features are extracted from video by using 2D convolutional neural networks, and we also employ 3D convolutional neural networks to extract video features from video clips. The fine motion information among successive video frames is encoded by our proposed channel soft attention and SeqVLAD. In addition, the features extracted from 2D and 3D convolutional neural networks are both used in the semantic detection network (SDN). As for decoder, the semantic LSTM is used to combine the video representation and semantic vectors to generate sentences. The reconstructor will rebuild semantic vectors. By fully using the bidirectional flows of semantic vector and sentence, the performance of video captioning can be further boosted. In the following part, we briefly introduce the VLAD [3] and SeqVLAD first. Then present the details of the channel soft attention and semantic reconstruction network of our model.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 5 Experiment and Evaluation
### Performance Evaluation
#### 5.2.1 Impact of the Dense Layers on the SCAE Model  
As mentioned previously, the number of dense layers plays an important role in improving the encoding and decoding processes of the SCAE network model for data reconstruction. To evaluate the efficiency of different numbers of dense layers in the SCAE, we conducted an experiment using the real PM 2.5 dataset.  
All member sensors collected PM 2.5 data from the environment and transmitted them to CH nodes in a LEACH-based WSN system. The impacts of external and internal events, such as extreme weather conditions and noise, on the PM 2.5 dataset were simulated based on the phenomena presented above. After the SCAE is trained, the results shown in Table 4 prove that the number of dense layers must be set appropriately for the proposed learning system.  
If the number of dense layers is too small, the learning model struggles to learn the characteristics of the dataset. In contrast, if the number of dense layers is excessive, the learning model can take too long to run and may readily fall into a local optimum. As can be seen, when the number of dense layers is one, the mistakes are the greatest, and when the number of dense layers is five, the errors are minimized. As a result, we selected five dense layers for the architecture proposed in this study.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 1 Introduction
### Manufacture of Ion Exchangers and Main Processes of Production  
During over sixty or seventy years of commercial production, the ion exchangers called Amberlite with the trademark of the Rohm & Haas Company, Philadelphia, Penn., USA, Diaion, with the trademark of Mitsubishi Corporation (previously Diamond Shamrock Corporation, Pittsburgh, Penn., USA), Dowex with the trademark of Dow Chemical Company (previously Dia-Prosin, France and Rohm & Haas, Chauny, France), Duolite, with the trademark of Dow Chemical Company, Imac with the trademark of Dow Chemical Company, Ionac/Relite with the trademark of the Sybron Corporation, Kastel as a trademark of Dow Chemical Company (previously Montedison, Milan, Italy), Lewatit, with the trademark Lanzess (previously Bayer, Leverkusen, Germany) as well as Purolite, with the trademark of Purolite International, and Resintech as a trademark of Resintech, Inc. were and are still used [9; 10; 11].  
They are obtained from the two principal reactions, namely polycondensation and polymerization of monomers such as phenol and formaldehyde, styrene and divinylbenzene, acrylonitrile, acrylates and polyamines [12; 13; 14; 15; 16; 17], leading to the cross-linked ion exchangers with a typical 4 to 20% cross-liking agent (mostly DVB) (Figure 1) [15].
## 5 Results
### Case Study I
4. We assumed that the PMU measurements were updated at each iteration, i.e., \(\tau_{SCADA}=5\tau_{PMU}\), and, at each iteration, the SCADA measurements were predicted from a training model. 5. The training model was updated every time N_MC was a multiple of \(20\) (\(6020\), \(6040\), \(6060\), etc.) using the DSSE results obtained from the SCADA measurements and PMU measurements during that time. 6. In order to verify the proposed method performance, the DSSE results were compared to when the SCADA measurements were replaced using a sample-and-hold technique. 7. The estimated value from WLS calculations and the actual value from power flow calculations were compared using MAE and RMSE criteria and shown in Table 1.  
It is clear from Table 1 that the MAE and RMSE values for the state variables (voltage magnitude and phase angle) were improved from the proposed method compared to the sample-and-hold technique. This means that predicting the SCADA measurements based on updating a training model has a better performance. This was true even after more PVs were introduced to the DN, thus showing that our method is robust in a more dynamic environment.
## 3 The Proposed Method
### Sample Selection  
* Small loss sample selection  
Traditional sample selection uses a small loss sample selection strategy. Since the DNN tends to fit simple samples first [18], small loss samples are more likely to be clean samples. This standard method usually selects a predefined proportion of small loss samples in each small batch. Forget rate is an important parameter in the small loss sample strategy. According to [14; 15; 17], the forget rate takes the value of the noise rate assumed that is known, and there is an initialization process for the forget rate:  
\[\varphi(e)=\min\Bigl{\{}\frac{e}{k}\sigma,\sigma\Bigr{\}} \tag{5}\]  
Forget rate \(\varphi(e)\) gradually increased to noise rate \(\sigma\) in the first \(k\) (=10) epochs \(e\).  
However, when training the DNN, the noise ratio in different small batches inevitably fluctuates. As shown in the figure above, and due to the deepening of the DNN training, the agreement part in the small sample selection quickly rises and then stabilizes, while the disagreement part in the small sample selection has a clean rate even lower than our 80% clean rate. Combined with the prediction accuracy map, this part of the noise samples began to affect the accuracy of the DNN model and reduce the prediction accuracy.  
* Pure sample  
The small loss samples are subdivided twice. Because \"co-teaching\" uses the comparative training of the two networks to promote the model from \"Disagreement\" to \"Agreement\" and because the DNN tends to fit simple samples first [18], we have reason to believe that a sample label that quickly reaches an \"Agreement\" is more likely to be credible. In the initial training stage of the \"Agreement,\" although the label purity rate did not widen the gap from the disagreement part, with the deepening of the DNN training, the label purity rate opened a large gap.  
* Complex sample  
When choosing to use only pure samples for high purity samples, there are limitations. The limitations are as follows: Although the small loss sample strategy combined with the \"Agreement\" for secondary classification can help us quickly screen out high purity samples, this is under the premise of greatly reducing the total number of input samples, which causes the network model to overfit. Second, if only high clean rate samples are considered, the network model converges too slowly. Therefore, we also need to introduce the Disagreement part named the Complex sample.