esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperability of Digital Tools for the Monitoring and Control of Construction Projects
## 2 Background
### Digitized Construction Monitoring
Some research has shown that monitoring of work and comparison with the project baseline can be used to assess work in progress, providing an accuracy error of less than 20% [22]. Studies in the literature indicate that greater standardization of work will make the application of automated procedures less complex [36]. For this reason, the means to represent possible discrepancies between planned progress and constructed progress is an important factor in facilitating decisions on corrective measures [10]. The results of the adoption of digital approaches in construction are increasingly showing positive results; for example, projects that would otherwise present a high risk of cost overruns are being delivered on time and within budget. The implementation of digital tools allows for the integration of teams, processes and organizations, reducing the problem of fragmentation present in traditional methods [31] (Figure 2). In the last few years, with the increasing level of competition in the AECO industry, research efforts have focused on the application of information technology as a way to improve the process of integration of construction supply chain management [37]. This collaboration has emerged as computer-assisted collaborative learning, facilitating interactions between two or more individuals who may be geographically and/or temporally separated [31]. As a result, there is a need to adapt technology to improve design and planning processes in a common and secure data environment (CDE). This type of environment allows for the integrated collaboration of project participants and their interaction via accessible information that supports decision making [38]. This concept could be achieved by using different technologies that provide a richness of varied and complementary information, facilitating the joint work of the teams via collaborative work and problem solving without considering the geographical distance. These technologies could also work both synchronously and asynchronously, allowing for the sharing of documents not only anywhere, but also at all times. In this way, the provided tools help communication and collaboration and provide a means of solving problems in the early stages of the project [31]. Figure 2: Integrated construction environment. Adapted from [31].
# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 1 Introduction  
Since the reform and opening up, the rapid development of the national economy has promoted the rapid development of various industries. For example, it has promoted the rapid development of road freight transportation. With the rise of online shopping, it has promoted the growth of China's logistics industry to an extent that has led to the development of China's freight transportation. The increase in the investment share of social fixed assets has promoted the development of trucks. In the 'Twelfth Five-Year Plan', the state clearly proposed to vigorously develop road freight transportation, improve the level of standardization and intensification, promote the development and improvement of freight transportation methods, and expand the radiation range of less-than-truckload and fast-operating business networks. With the completion of a well-off society in an all-round way, China's poverty alleviation has achieved great results. The economy has developed steadily, the income level of urban and rural residents has been continuously improved, and consumer demand has been increasing. In addition, China's highway network has been continuously improved. For instance, the structure of the highway has been continuously rationalized, the connection between urban and rural areas has been strengthened, and the highway network has been gradually built and improved. The progress of informatization technology will promote the development of highway transportation, improve the quality of transportation, and promote the transformation and upgrading of transportation modes while promoting the development of transportation industry [1].  
As the most important mode of transportation, highway transportation occupies an important position in the whole of cargo transportation, and the connection between cargo transportation and the highway is inseparable. Highway freight volume is affected by many factors, such as the ownership of freight vehicles with highway diesel consumption as the evaluation index, as well as highway freight volume and freight turnover. It is also inseparable from the relationship between the population and the economic development level. The prediction of highway freight volume is of great significance. The fluctuation of highway freight volume can reflect the economic fluctuation to a certain extent, which is of great significance to the improvement of the national and international economic levels. In order to predict highway freight volume more accurately, we extract the key factors affecting freight volume from historical data and construct a multi-factor BP neural network model to achieve the accurate prediction of highway freight volume. Through the research of this paper, we expect to provide a more accurate and effective method for the prediction of highway freight volume. Through the prediction of highway freight volume, we can adjust the industrial structure of highway freight and the investment in infrastructure and provide strong support for the sustainable development of highway transportation [2; 3; 4; 5; 6].
## 3 System Model  
This section considers the system model comprising the network model, task and computation model, transmission service model, and link transmission rate model.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Cement-Based Material Industry  
Concrete is the most commonly used building material worldwide, with ordinary Portland cement (OPC) traditionally serving as the binding agent. Cement is made by combining materials rich in lime, silica, alumina, and iron oxide. This blend is then sintered and ground, with gypsum added as a setting retardant. As reducing carbon dioxide emissions becomes crucial in combating global warming, it is essential to seek alternative low-emission binding agents for concrete. Kaolin, which is a good source of alumina and silica, also contributes to a whiter cement. Although kaolin's use in cement is currently minimal, the potential of metakaolin as a pozzolan is considerable [91]. The reactive alumina and silica in metakaolin react with excess calcium to form calcium aluminum silicate, enhancing the concrete's strength, workability, and durability while preventing autogenous shrinkage. Recently, metakaolin, a partially calcined form of kaolin, has been introduced as a pozzolanic additive in high-strength cement. Moreover, studies have shown that incorporating metakaolin can boost the strength of oil well cements by up to 40%. Therefore, Kaolin-based cement materials offer excellent versatility, making their development prospects highly promising. The _LafargeHolim_ company utilizes kaolin in their products to enhance performance.
# Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
## 5 Performance Evaluation
### Test Result  
In this subsection, we aim to test our scheduler on 100 incoming data streams (\(T=100\)) for a Fast Ethernet communication channel and calculate the on-line instantaneous overall energy consumption and energy savings that are shown in Figure 4. From this figure, we conclude that the total energy cost changes over time; the figure even shows the energy saving correlated to time.  
On the other hand, in order to evaluate the instantaneous energy consumption for 1000 slots using two different schemes of the communication network (Fast and Giga), we evaluated the Fast/Giga Ethernet energy consumption in the SVF scenario. The results of these comparisons are presented in Figure 5. We understand that when the \(\Omega_{s,v}^{c}\) increases, the correlated energy consumption rises at the same scale due to the rising communication constant; this trend continues over time.  
Finally, in the last test scenario, the plots of Figure 6a show the time behaviors in relation to the number of VMs and physical servers that are dynamically turned ON by our algorithm scheduler under the real-world workload of Figure 2. A comparison of the plots of Figure 6a with the arrival trace of Figure 2 supports the conclusion that the consolidation action performed by the proposed algorithm is capable of tracking the abrupt (and unpredicted) changes of the input workload with a delay of 1 slot period. An examination of Figure 6a reveals that (i) the number of turned ON VMs/servers increases with the increasing values of timeslots (\(T\)) (i.e., the network state pushes the scheduler to keep more FNs and corresponding servers turned ON and responds to the injected traffic over the FIs); and, (ii) the rate of turned ON servers in each timeslot is higher than the rate of turned ON VMs; this confirms that the servers are consuming more energy than VMs and when we use VMs we can control the server energy consumption. Also, this rate depends on the adopted Ethernet technology applied. Focusing on Figure 2, it is interesting to note that the numerically evaluated average energy consumption over 100 VMs (10 servers residing in four heterogeneous FNs) of the proposed heuristic for 100 slots is about 136.5 (J) and 138.5 (J); for 1000 slots, this value is about 155 (J) and 160 (J) for GIGA and FAST Ethernet communication cases, respectively. This confirms that even increasing the slots to 10 times the average energy consumption in the proposed scheduler will not lead to a significant increase-at most, 23% in the worst case scenario.  
Figure 4: Energy saving of SVF (dashed plot) and energy consumption of SVF (continuous plot) for the Fast Ethernet LAN for \(T=100\).  
Figure 5: Fast/Giga Ethernet LANs on the energy consumption of SVF for \(T=1000\).
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 3 System Testing
### Power Consumption  
In order to achieve power efficiency and long operational life, as described in Section 2, some designing decisions, on the hardware and software levels, had to be investigated. On the hardware level, the smart tag includes a rechargeable battery, a solar panel, a charging and power level monitoring module and a USB port. As a result, it is capable of being powered by multiple power sources, and use the solar energy can continue its operation endlessly. On the software level, the smart tag is programmed to switch between different power modes, depending on its required functionality at the specific time frame. Every power mode that the devices uses is listed below in sequential order:  
* **Normal mode:** The device initialize the sensors, acquires and stores the measurements;
* **Deep sleep mode:** Most parts of the device are powered off in order to minimize the power consumption;
* **Data transmission:** The device connects to the network and transmit the stored data.  
In order to have an estimated power profile, current consumption measurements of the smart tag have been acquired using Pro's Kit MT-1705 digital multi-meter. The measurements was recorded every second. For this test, the smart tag was programmed to acquire the climate measurements and transmit them every 1 min. During the time frame of the 1 min, it goes into deep sleep mode. The power profile of the device for 2 min is presented in Figure 9. From the measurements, it is concluded that the mean current consumption of the device during normal mode is approximately 36 mA and during the deep sleep is 0.06 mA. The data transmission is the most demanding process, as it reaches a peak of 86 mA.  
The power profile of the smart tag can be expressed by the Equation (1), which calculates the operational hours of the device based on the time frame of each power mode and the current that they demand:  
\[\begin{split} hours=BCap/[(NMcur*NMt/3600)&+(DPcur* DPt/3600)\\ &+(DTcur*DTt/3600)]\end{split} \tag{1}\]  
with _BCap_ being the capacity of the battery in mAh and _NMcur_, _DPcur_ and _DTcur_ being the current consummations on normal mode, deep sleep and data transmission, respectively. In the same way, _NMt_, _DPt_ and _DTt_ express the time frame in seconds of each power mode.  
Taking into consideration that smart tags acquires measurements every 30 min and transmit the data every 12 h, we have approximately every hour: NMcur = 36 mAh, DPcur = 0.06 mAh, DTcur = 86 mAh, NMt = 60 s, DPt = 3539.58 s and DPt = 0.42 s. Using a fully charged battery with 3000 mAh capacity, we can calculate that the smart tag can operate, with no solar energy, for approximately 4484.72 h, which is 6.23 months. If sunlight  
Figure 8: NB-IoT signal strength—RSSI.  
is present, the device can use the solar energy to recharge the battery at the same time and continue its operation endlessly. The estimated time that a 3000 mAh battery needs to be fully recharged is 5 h on a day with normal to high sunlight.
## 4 Simulation and Experimental Results
### Simulation Results  
Simulation tests were performed using MATLAB/Simulink software (version 2018b, The MathWorks, Natick, MA, USA). During the simulations, the estimator was tested for the open-loop as well as closed-loop operation modes. In a simulation model, effects such as signal offsets, measurements noise and inverter nonlinearity were not taken into consideration.
An IT system can provide real-time energy consumption feedback to the occupant. It can provide useful details about the energy consumption of appliances during different times of the day and the total consumption of each section within the home, such as kitchen, rooms, etc. This information will be an economic motivation for consumers to improve their energy efficiency and change their energy consumption habits. Sustainable development requirement and significant raise in energy costs have necessitated the reduction in energy consumption without compromising occupants' comfort. Deploying intelligent energy management systems in buildings will lead to energy and cost savings. In this regard, the smart home concept has attracted great attention in the last decade because of its potentials in providing comfort and energy management to occupants. The concept of \"smart city\" is now also well established. Smart city systems not only improve the inhabitants' comfort, but also makes improvement in the efficient management of asset; this includes smart transportation systems, smart energy grids, and environmental monitoring. As a city's founding elements, smart homes can exchange information with the city and coordinate activities essential to a smart city. The motivation for this study is to identify and review smart home design/operation concepts and technologies that can reduce energy consumption without compromising occupants' comfort. The study considers both the user- and building- approaches. Section 2 reviews several types of smart home architectures and focuses on Cloud-based architecture models. Section 3 is an overview of communication protocols used by interconnected devices in a smart home. Section 4 is a review of sensor technologies in a smart home; how sensor technologies can be used to monitor energy performance and associate them with household activities is addressed. Section 5 is a review of the inhabitants' activity and concentrates on activity recognition and discovery approaches. Section 6 is an overview of smart schemes for energy management and briefly touches upon building energy system modelling and on the connection of smart homes and smart grids. A review of perspectives on future smart homes is provided in Section 7. The final Section 8 is a summary with major conclusions.
# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 3 Research Questions  
The goal of this research is to study how explanations are affected by underlying PPM workflow-related choices. It is crucial to study the different characteristics of XAI methods that influence the final outcome of explanation process. Overall, this leads to the following research questions (RQ)s:  
**RQ: How can different XAI methods be compared?** Conducting a benchmark study that allows comparing all available explainability approaches is not likely to be possible [18]. This is due to the varying characteristics of these approaches in terms of the dimensions (cf. Section 2.2). However, as many techniques have been proposed for evaluating explanations [18; 19], a constrained study would be useful to compare the relative performance of explainability methods that have been applied in the context of PPM approaches. In addition, the consistency of explainability methods applied to PPM results needs to be studied in order to shed light on one of the potential vulnerabilities of XAI methods, namely sensitivity of explanations, i.e., how several conditions can affect the ability of XAI methods to generate the same explanations again under the same settings. An example of the conditions affecting the reproduction of explanations is the inability to regenerate the same neighborhood of the process instance to be explained in presence of high-dimensionality data. To this end, we subdivide **RQ** into the following sub-research questions:  
_RQ1: To what extent are explanations consistent when executing an explanation method several times using the same underlying settings?_  
_RQ2: How are the explanations generated by an XAI method affected by predictive model choices?_  
Explainability methods vary by the extent to which an explanation can be generalised over several data samples that belong to the same vicinity (i.e., the same neighborhood which defines underlying similarities unifying data samples belonging to it). Explainability methods also vary in the granularity of the explanations they provide and their suitability to explain a number of data samples independent of whether they are small or large, i.e., independent of whether the explainability method is local or global. However, the number of explained data samples comes with computational costs. Therefore, we add another sub-research question to compare explainability methods with respect to their execution time:RQ3: How do explainability methods differ in terms of execution time? How does needed time differ with respect to different dataset characteristics, preprocessing choices, and chosen predictive models?
## 5 Related Works  
### Cryptographic Integration Approaches for Cloud System Security  
Various studies have been proposed for combining file encryption in cloud systems with advanced cryptographic algorithms to enhance security. [PERSON] et al. [81] proposed an encryption technique combining AES and RC4, leveraging the strong security of AES and the high processing speed of RC4 to improve the avalanche effect and overall file protection. [PERSON] et al. [63] focused on increasing encryption performance for large-scale data through parallel optimization by combining XTS and AES, utilizing GPU-based parallelism to accelerate encryption and improve scalability. [PERSON] et al. [82] proposed combining AES and RSA encryption with steganography to hide encrypted data within image files, adding a layered security model that enhances confidentiality and integrity in cloud-based storage systems. [PERSON] et al. [37] proposed a hybrid PQC-QKD protocol for secure key distribution across networked systems. Their work constructs tree-based protocol compositions using XOR and secret sharing to evaluate key rate and vulnerability across multiple communication paths, focusing on protocol-level optimization in hybrid quantum-classical networks.  
Our work is in line with these studies in proposing an encryption scheme for cloud systems. However, it also addresses the limitations of existing approaches by considering both classical and quantum security threats. By integrating Kyber-based post-quantum key encapsulation with BB84-based Quantum Key Distribution (QKD), our scheme provides a layered encryption architecture optimized for secure and scalable encryption in distributed cloud systems. While [PERSON] et al. [37] proposed a hybrid PQC-QKD protocol focusing on the key-distribution layer and protocol modeling, their work does not address practical encryption workflows or system-level integration for file protection. In contrast, our study designs a unified encryption framework for file-level protection that tightly integrates key generation, AES-based data encryption, and transmission coordination, enabling secure and scalable execution across cloud systems. Thus, our proposed scheme enables scalable and secure file protection by decoupling key management from data encryption, supporting parallel AES encryption across slave nodes while maintaining quantum-resilient key confidentiality through centralized coordination and multi-layered key masking.  
Figure 12: CPU utilization comparison between AES (Original) and our proposed scheme (PQC + QKD + AES) across multiple nodes.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were carried out on JASP (version 0.14.1, JASP Team 2020) and SPSS (version 26.0, IBM Corp., Armonk, NY, USA). Data are expressed as mean (standard deviation). An alpha level of \(p<0.05\) was set as the level of significance. Inter-unit consistency was assessed using intraclass correlation coefficients (ICC). ICC was interpreted as _slight_ (\(<\)0.20), _fair_ (0.21-0.40), _moderate_ (0.41-60), _substantial_ (0.61-0.80), or _almost perfect_ reliability (\(>\)0.80) [36; 37]. Standard error of measurement (SEM) was calculated from the ICC results using the formula: \(\mathrm{SEM}=\mathrm{SD}\times\sqrt[]{(1-\mathrm{ICC})}\).  
For the validity assessment, one-sample \(t\)-tests were performed to compare the distance measured using each GNSS unit with the known distance of 45.72 m. Effect sizes were indicated by [PERSON]'s \(d\) and interpreted as _small_ (0.2 \(\leq\) d \(<\) 0.5), _medium_ (0.5 \(\leq\) d \(<\) 0.8), or _large_ (d \(\geq\) 0.8). Since the speed data were not normally distributed, non-parametric statistical tests were employed. Specially, Wilcoxon signed-rank tests were used to compare each GNSS unit's peak speed with the reference speed measured using video analysis. Effect size (r) for the Wilcoxon signed-rank tests was calculated from the Z-value and interpreted as _small_ (0.1 \(\leq\) | \(r\) \(<\) 0.3), _medium_ (0.3 \(\leq\) | \(r\) \(<\) 0.5), or _large_ (| \(r\) \(\geq\) 0.5).  
Figure 3: Representative raw speed-time from one participant measured using Kinovea video analysis and one GNSS unit.