esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Growth Measurements  
3.2.1. Growth Rate and Biomass Productivity  
The 9-day incubation period was selected to capture the most active growth phase of the microalgae, during which optical density (OD) and pH were monitored daily (Figure S2). The OD of the initial microalgae culture was 0.103 \(\pm\) 0.003 and increased  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Marker Gene** & **Length (bp)** & **Closest Match** **(Accession No.)** & **Query Cover** & **Identification** \\ \hline ITS & 653 & _Desmodesmus_ sp. & 100\% & 100\% \\  & & KNUA024 (MT603589) & & \\ \hline
18S rRNA & 407 & _Desmodesmus_ sp. & 100\% & 99.51\% \\  & & (OM893304) & & \\ \hline _tuf_A & 920 & _Desmodesmus spinosus_ & & \\  & & (MN218421) & 94\% & 96.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identification of microalgae KNUA231 using molecular markers.  
to 4.487 \(\pm\) 0.315 after 9 days of cultivation (Figure 1). The dry weight of the biomass at the end of the cultivation period was measured at 1.54 g L\({}^{-1}\).  
On day 9, chlorophyll \(a\), chlorophyll \(b\), and total carotenoids were quantified, yielding concentrations of 36.29 \(\pm\) 1.63 \(\upmu\)g mg\({}^{-1}\), 6.55 \(\pm\) 0.84 \(\upmu\)g mg\({}^{-1}\), and 12.98 \(\pm\) 0.13 \(\upmu\)g mg\({}^{-1}\), respectively.
## 4 Discussions
### Microporosity Changes in Mudstone
#### 4.1.2 Natural Mudstone  
Unlike the sealed sample, the natural mudstone sample will be recharged with water during the thawing phase. The water vapor inside the environmental chamber will attach to the surface of the piece, gradually penetrate the sample's interior, and hydrate with the interlayer water between the clay mineral particles within the model and the interior of the clay mineral particles under the action of microscopic forces. Therefore, the freezing and thawing cycle process of the natural mudstone sample is a superimposed damage by the joint effort of the freezing and expansion force generated by the water-ice phase transition and the hydration force. Hydrating of clay minerals leads to the generation of interlayer and intergranular swelling, which will create swelling forces acting on the rock skeleton under the constraints of the rock skeleton. If the expansion force is large enough, it will  
Figure 15: Pore size distribution of sealed mudstone under different freeze–thaw cycles.  
lead to the expansion of existing cracks and the creation of new cracks. The size of the expansion force depends mainly on the type and content of the expansive clay minerals.  
As depicted in Figure 16, an increase in freeze-thaw cycles leads to more natural mudstone samples undergoing the thawing process. During this process, water continuously penetrates the sample, causing the sample mass to increase gradually. Fitting curves for the sample mass changes during the three freeze-thaw cycles show that the group of natural mudstone samples grows linearly with the number of cycles. The water entering the piece moves to the voids between the mudstone skeleton, the pores between the clay mineral particles, and the interlayers within the clay mineral particles.  
The increase in pore size during freeze-thaw cycling of natural mudstone samples consists of two aspects: the increase in pore area and number due to the volume change of the water-ice phase change under freeze-thaw, and the growth in intergranular and intraparticle interlaminar pore space in clay minerals due to hydration of the clay minerals of the mudstone.  
By comparing the changes of \(T_{2}\) spectral area between the confined mudstone sample and the natural mudstone sample under different freeze-thaw cycles, it can be seen that the increase in \(T_{2}\) spectral area of the sealed mudstone sample occurs when the number of freeze-thaw cycles is 6-10. The increase in the size of the natural mudstone sample occurs after the first freeze-thaw process. The difference between the increase in the \(T_{2}\) spectral area of the natural mudstone sample and the sealed sample can be regarded as the increase in the pore space caused by the hydration of the clay minerals of mudstone, i.e., hydration-induced mudstone damage. As seen from Figure 17, with the increasing number of freeze-thaw cycles, the pore area increase induced by hydration also increases gradually. In contrast, the rate of pore area increase decreases linearly.  
Figure 16: Mass of natural mudstone under different freeze–thaw cycles.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
###### Abstract  
Long Range (LoRa) is one of the promising Low-Power Wide-Area Network technologies to achieve a strong anti-noise ability due to the modulation of the chirp spread spectrum in low-power and long-distance communications. However, LoRa suffers the problem of packet collisions. Hence, we propose QR\(-\)LoRa,a novel PHY-layer scheme that can transmit data in both amplitude and frequency dimensions simultaneously. For the amplitude modulation, we modulate the constant envelope of a LoRa chirp with a cyclic right-shifted ramp signal, where the cyclic right-shifted position carries the data of the amplitude modulation. We adopt the standard LoRa for frequency modulation. We prototype QR\(-\)LoRa on the software-defined radio platform USRP N210 and evaluate its performance via simulations and field experiments. The results show the bit rate gain of QR\(-\)LoRa is up to 2\(\times\) compared with the standard LoRa device.  
LoRa PHY-layer design; high bit rate; joint frequency-amplitude modulation; high energy efficiency 2025142687  
2
## 2 Role of AI and IoT in the Hyperconnected Intelligent World
### Security Threats in IoT  
IoT networks consist of interconnected devices with unique identifiers that provide real-time interactions; examples include smart meters in IoT and medical sensors in IoMT. These devices often have limited computational capacity and small memory. IoT devices are interconnected, which means they are connected to the cloud and/or other IoT devices for data exchange. Despite the constrained capabilities of connected IoT devices, these can exchange services without the control of a central processor. Such communication is vulnerable to cyber attacks, where a malicious device could be disguised as an accepted IoT device and inject malware files. Moreover, connected devices are heterogeneous as they might run on different platforms and have different specifications. Finally, the increasing number of connected IoT devices generates an enormous scale of data across a large-scale network. The IoT system architecture consists of three main layers: the **perception layer,** which is the physical layer for the connected devices, the **network layer,** which is responsible for securing the connection between the device and the cloud, and the **application layer,** which is the front-end layer to deliver the service. Each layer is a target for different types of attacks. For instance, malicious node injection and battery draining are forms of security threats to the physical layer. MITM and denial of service are threats to the network layer. Malware attacks consist of malicious files (e.g., viruses) and are major threats to the application layer. For more information about the IoT system architecture and corresponding layers, readers are encouraged to refer to [5].  
Malware forms a major security threat to IoT systems and is difficult to effectively detect using malware attack detection algorithms. There are three known methods for analysing incoming files and detecting malware: static, dynamic, and hybrid analyses, which use signature and behavioural-based techniques. A signature-based technique reads a unique part of the file for signature detection; thus, it is efficient for detecting known malware files but is unable to detect unknown malware. On the other hand, the behavioural-based technique simulates the behaviour of a malicious or benign file for detection and, hence, is efficient in detecting unknown malware files; however, it is computationally expensive to run. Based on the IoT characteristics and malware detection techniques presented, the best way to secure the IoT is by implementing a lightweight (i.e., not computationally expensive) and adaptive method (i.e., capable of detecting unseen malware).
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.3 Building and Structural Monitoring  
Accelerometers measure the motion and vibration of a structure subjected to a dynamic load. This load can have various sources, such as human activity, heavy construction near the monitored structure, and weather conditions, such as wind gusts, earthquakes, and ground shocks.  
The paper [33] reviews the technologies, architectures, data processing techniques, damage identification techniques, and challenges in state-of-the-art results with structural health monitoring system applications. This study shows the effectiveness and cost-benefit of wireless sensors based on MEMSs. It also highlights some difficulties these systems face with computation and real-time processing, using the system presented in Figure 7.  
The reference [34] details the design of a structural health monitoring system using a wireless sensor network and a base station, with the signals diagram shown in Figure 8. The wireless network comprises 16 nodes with MEMS accelerometers and antialiasing filters.  
Figure 8: The diagram for the low-cost acceleration monitoring system—reprinted from [34].  
Figure 6: Test bench for centrifugal pump monitoring—reprinted from [30].  
Figure 7: Acceleration sensor node structure and components—reprinted from [33].
## 2 Stand-Alone Ground-Based Technology Applications in Transport Infrastructure Monitoring  
The occurrence of dramatic events due to the vulnerability of transport infrastructure to natural hazards (e.g., earthquakes, subsidence and landslides) and endogenous events (e.g., the end of pavement service life, raising traffic volumes, the ageing of materials, corrosion of rebars in reinforced concrete structures) have emphasised the importance of routine monitoring and the provision of effective maintenance plans at the international level. Countries with transportation network systems highly exposed to major natural hazards are proactively investing money to promote more effective asset-management programs. This is the case in Italy, where the Ministry for Transport and Infrastructure has urged the implementation of new measures to achieve continuous infrastructure monitoring though the provision of dedicated guidelines [20]. In this framework, monitoring the structural integrity of transport infrastructure is a priority for national authorities and asset administrators to guarantee the structural integrity and the operational safety. This aims to prevent infrastructure damage and deterioration prior to the occurrence of any expensive rehabilitation or structural failure. In the last decade, several sensors and technologies, such as accelerometers, the Internet of Things (IOT) sensors, wireless network systems and ground-based NDTs, e.g., ground-penetrating radar (GPR) and light detection and ranging (LiDAR), have proven effective for the provision of dense arrays of data and their incorporation into more comprehensive asset management systems, as reported in Table 1.  
The main limitations of the stand-alone use of these technologies in infrastructure monitoring include the difficulty of implementing routine inspections for multi-temporal data acquisition, a relatively limited land coverage and physics constraints from the technologies' working principles that limit the data spatial resolution. A limited repeatability of measurements in time and the high costs of non-destructive inspections for implementation at the network level stand as additional constraints. To overcome these limitations, satellite-based remote sensing technologies have been increasingly used in the last few years. Advantages include the provision of higher temporal revisiting time and spatial resolution of measurements, and the possibility of creating archives from the historical time-series of the displacements [13; 18; 34; 35]. Amongst the most important limitations of the InSAR techniques, we can mention:  
1. the necessity of collecting datasets with different orbit geometries for the calculation of the vertical and horizontal components of the displacements, using ground-truth reference points;
2. the high computational requirements for the processing of SAR imagery at the network level, especially in case of long-term investigations and high-frequency datasets (i.e., X-Band).
## 2 Design Problem and Ground Conditions  
The following design problem in Nur-Sultan, Kazakhstan, is presented to demonstrate the differences between the Kazakhstan and European approaches. The design problem is used for calculating the bearing resistance and elastic settlement of three different types of foundations (plain slab raft, pile, and piled raft foundations), shown in Figure 2.  
Figure 2: Idealized soil profile and designed foundations.  
The soil profile (Figure 2) and engineering properties (Table 1) for loam, which is widely distributed in Nur-Sultan, were used to design the foundations. The groundwater level (G.W.T.) was assumed to be located at a 2 m depth from the surface level [14]. A comparative analysis of the considered design codes was performed for a raft foundation with length 20 m and width 10 m, a concrete driven square pile foundation with cross-sectional area 300 mm \(\times\) 300 mm and length 6 m, and a piled raft foundation representing the combination of the first two foundations. The piles were driven by hydraulic and diesel hammers. The standard structure situation was simulated so that the load ratio of variable to permanent load was 0.25 (Live Load = 0.25*Dead Load). For design simplicity, load eccentricity was neglected.
## 3 Results
### Motion Tracking Verification  
Figure 11 shows the curves of the parameters (\(P_{elbow}\), \(P_{urist}(z)\), \(\alpha\), and \(\beta\) of the second type verification test mentioned in Section 2.4) obtained from the two systems (M system and the presented system). These curves show that the movement of the arm is slow, about more than 2 s per one cycle. The purpose for doing this is to eliminate the possible error caused by the low sampling rate of Mediapipe Pose. Finally, the root mean square errors of three parameters \(\alpha\), \(\beta\), and \(P_{urist}(z)\) (the z-coordinate of the wrist joint) between two systems were calculated.  
Based on the experiment with six repetitions of lateral raise on the frontal plane, the root mean square errors between the two systems are 2.434 cm for \(P_{wrist}(z)\), 5.654\({}^{\circ}\) for \(\alpha\), and 4.385\({}^{\circ}\) for \(\beta\).
## 2 Architecture of the Multi-Park IEMG System
### Model of CCS and P2G Coupling  
While the micro CCHP outputs electrical power to meet the electrical load, it inevitably produces CO\({}_{2}\) to maximize carbon emission reduction and system revenue. This paper utilizes CCS to capture CO\({}_{2}\) by the micro CCHP, although it is not guaranteed that all of CO\({}_{2}\) can be absorbed, it can be maximized according to system conditions, and the captured CO\({}_{2}\) from CCHP is further supplied to the P2G unit as a raw material for synthesizing natural gas. The P2G unit generates hydrogen by electrolyzing water, which then reacts with CO\({}_{2}\) and O\({}_{2}\) to produce CH\({}_{4}\); this can be supplied to the micro CCHP to continue generating electrical power, thus achieving the benefits of energy savings and emission reduction. The CCS and P2G coupling model is summarized as follows:  
\[Q_{\text{CO}_{2},t}^{\text{CCS}}=\eta_{\text{CCS}}Q_{\text{CO}_{2},t}^{\text{ CCHP}} \tag{14}\]  
\[Q_{\text{CO}_{2},t}^{\text{CCS}}=Q_{\text{CO}_{2},t}^{\text{P2G}}+Q_{\text{ CO}_{2},t}^{\text{f}} \tag{15}\]  
\[P_{\text{CO}_{2},t}^{\text{CCS}}=\lambda_{\text{CCS}}Q_{\text{CO}_{2},t}^{ \text{CCS}} \tag{16}\]  
\[P_{\text{CCS},t}=P_{\text{CO}_{2},t}^{\text{CCS}}+P_{f,t}^{\text{CCS}} \tag{17}\]  
\[F_{\text{gas}}^{\text{P2G}}=\varpi\Big{(}Q_{\text{CO}_{2},t}^{\text{P2G}}+Q_{ \text{CO}_{2},t}^{\text{GIRD}}\Big{)} \tag{18}\]  
\[P_{\text{P2G},t}=\beta Q_{\text{CO}_{2},t}^{\text{P2G}} \tag{19}\]  
\[P_{\text{c,min}}^{\text{P2G}}\leq P_{\text{P2G},t}\leq P_{\text{c,max}}^{\text{ P2G}} \tag{20}\]\[\Delta P_{e,\min}^{\text{P2G}}\leq P_{\text{P2G},t+1}-P_{\text{P2G},t}\leq\Delta P _{e,\max}^{\text{P2G}} \tag{21}\]  
wherein \(Q_{\text{CO2},t}^{\text{CCHP}}\) represents the CO2 emitted during the operation of the micro CCHP, \(\eta_{\text{CCS}}\) represents the efficiency value of CCS in capturing CO2 under normal working conditions, \(Q_{\text{CO2},t}^{\text{CCS}}\) represents the amount of CO2 captured by CCS. Half of the CO2 captured by CCS is stored as \(Q_{\text{CO2},t}^{f}\), \(P_{f,t}^{\text{CCS}}\) represents the electric power consumed for storing \(Q_{\text{CO2},t}^{f}\), and the other half is used to supply P2G for methane production \(Q_{\text{CO2},t}^{\text{P2G}}\).\(P_{\text{CO2},t}^{\text{CCS}}\) represents the electric power consumed by the CCS device, \(\lambda_{\text{CCS}}\) represents the capture efficiency; \(\overline{\omega}\) represents the size of the balance coefficient, \(\beta\) presents the efficiency of processing CO2. \(P_{e,\max}^{\text{P2G}}\) and \(P_{e,\min}^{\text{P2G}}\) are the upper and lower limits of P2G output, \(\Delta P_{e,\max}^{\text{P2G}}\) and \(\Delta P_{e,\min}^{\text{P2G}}\) are the upper and lower limits of P2G ramping.
###### Abstract
The geo-space of the research is covered by the 4G telecommunication network managed by the provider M:tel BL, so the main research goal in this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user (_Average User Downlink Throughput_ in Mbps) and average DL data throughput per cell (_Cell Downlink Average Throughput_ in kbps) within the LTE network technology. The initial step in the realized research process was the establishment of institutional cooperation between the scientific research organization--Faculty of Transport and Traffic Engineering Doboj--and the mobile operator company--M:tel Banja Luka--on the basis of the request for availability of the data on classes of realized telecommunication traffic in M:tel network in a certain observation period. Network performance modeling is a current challenge and problem area that is solved using multiple approaches. In this paper, the MLP machine learning model was chosen because it is characterized by very good predictive abilities due to the possibility of adaptive approximation of nonlinear dependences of the output on the input. The contribution of research is reflected in the fact that providers of telecommunications services need to address more complex issues of their quality, especially in the dimension of end-user satisfaction. The input data in the created predictive MLP model are presented with 17 independent M:tel network variables in accordance with the theoretical analysis of the research results from the relevant literature and the initial 3 GPP standard, whose detailed analysis is given in Section 4 of this paper. Multiple training and testing of 30 different variants of the MLP model, with different combinations of metaparameters, based on relative indicators of prediction accuracy influences the choice of the final model. The quality of the results of this research is checked by comparing the accuracy of the prediction given by the selected MLP model with the performance of the multiple linear regression model (MLR) as one of the representative models of statistical analysis of the research. In addition to the introduction (Section 1) and the above mentioned Section 4, the syntactic structure of the six sections in this paper consists of theoretical analysis of previous research (Section 2), materials and methods (Section 3), and the main focus next to Section 4 is placed on Section 5 on the presentation of the research results with discussion. Concluding remarks are given in the last, Section 6, followed by an overview of the used literature.
## 7 Discussion of the Results
Although the Hardening Soil model has been used in comparative analyses alongside NS and other advanced models, its ability to capture sudden strength loss and post-liquefaction behavior is limited compared to critical state-based formulations such as NS [46; 50]. Direct comparisons between NS and advanced models (e.g., CASM and Hardening Soil) indicate that, while the predicted failure mechanisms may appear similar, significant differences exist regarding the level of disturbance required to trigger liquefaction. The model's ability to represent sudden strength loss is the most relevant feature when assessing static liquefaction potential, while the specific constitutive formulation is of secondary importance [46]. NS stands out for its ability to simulate localized stress-strain paths and the progression of failure due to liquefaction, both of which are critical aspects in the evaluation of tailings dam stability [47; 48; 46]. Nevertheless, each model presents inherent limitations. In the case of NS, the main challenges include the following:  
* High sensitivity to parameter calibration: NS requires accurate calibration of critical state parameters, such as the CSL. Uncertainty in these parameters can lead to significant variability in the prediction of displacements, strains, and safety factors, particularly under low stress levels [47; 50];
* Simplified behavioral representation: Although NS is capable of reproducing liquefaction-induced strength loss, its CSSM-based formulation may not fully capture the behavior of tailings with variable composition or partial saturation ([51; 52; 53]);
* Strong dependence on high-quality laboratory data: Reliable parameter determination is contingent upon high-quality triaxial testing, which can be challenging for heterogeneous tailings or in the absence of comprehensive datasets [47; 54]. Similarly, limitations associated with the HSS model include the following:
* Inability to simulate liquefaction: The HSS model is not designed to simulate the sudden loss of strength associated with static liquefaction, as it lacks the state parameter and does not account for pore pressure generation mechanisms under monotonic loading [46];
* Restricted applicability: Its use is better suited to soils not susceptible to liquefaction. Applying it to tailings with high liquefaction potential may lead to underestimation of risks [46]. In summary, while NS offers advanced capabilities for modeling static liquefaction and associated failure mechanisms in loose tailings, it requires careful calibration and high-quality input data. HSS, though easier to implement and calibrate, is limited in its ability to model liquefaction phenomena. The selection of an appropriate constitutive model must therefore balance the complexity of the soil behavior, data availability, and the objectives of the geotechnical analysis.