esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Microseismic Evolution Characteristics of Rockburst Formation Process
### Frequency-Domain Characteristics of Microseismic Waveforms  
The analysis of microseismic data includes parameter analysis and waveform analysis. Waveform analysis can better reflect the rupture characteristics at the source. Based on the different fracture scales and research objects, microseismic monitoring or AE monitoring methods are adopted. Generally, microseisms with a low dominant frequency correspond to large-scale cracks in the engineering rock mass or large structures, and AE with a high dominant frequency corresponds to small-scale cracks in the rock in laboratory tests [32; 33]. Microseism and AE are essentially elastic waves due to the microfracture of brittle materials, and they are the same in terms of their physical mechanism. Therefore, as a tool for the experimental study of rockbursts, AE monitoring can extend the meso-mechanism of the rockburst formation process to large-scale microseismic monitoring. The spectral characteristics of AE can effectively reflect the failure characteristics of macroscale structures [34]. [PERSON] and [PERSON] [35] analyzed the characteristics of the AE spectrum in the process of granite rockburst experiment. They found that the dominant frequency value of AE had an evolution trend from low to high and then to low before a rockburst. [PERSON] et al. [36] conducted rockburst tests on granite specimens under true triaxial loading. The results showed that there was a \"quiescence period\" of AE before rockburst occurrence, and the sound amplitude continued to decrease. The dominant frequency of the AE signals showed a downward trend, and the spectrum distribution could be characterized by a low amplitude, wide band, and multiple peaks. [PERSON] et al. [34] summarized the dynamic failure process and characteristics of limestone by simulating the rockburst process in a single-plane dynamic unloading test under true triaxial conditions. They found that as the stress increased, the AE signal gradually changed from high frequency and low amplitude to low amplitude and high frequency. [PERSON] et al. [37] applied the wavelet transform to decompose the frequency of a complex microseismic waveform and to analyze precursor information related to rockbursts. The energy distribution of the microseismic frequency band shifted to the low frequency before rockburst occurrence. From the above research, the evolution characteristics of the acoustic signals or microseismic waveforms before rockburst occurrence can be used as useful information for rockburst prediction.
## 2 Materials and Methods
### Transmission Phase
#### 2.3.2 Data Communication and Energy Consumption from Cluster Head to Main Cluster Head  
The CH collects data from the nodes and forwards them in a compacted form to the MCH through at least one jump utilizing the minimum amount of energy that is assessed by Equations (6) and (7).  
\[E_{CH_{Tx}}=\Lambda\times l_{ength}\times\left(e_{t}+e_{fs}\times d^{2}\right) \tag{6}\]  
\[E_{CH_{Rx}}=\Lambda\times l_{ength}\times\left(e_{r}+E_{agg}\right) \tag{7}\]  
Here, \(\Lambda\) shows the sum of the nodes under one CH.  
In a multi-hop scenario, the CH finds the most reasonable way to reach the MCH. It can be found in the proposed route determination scheme given below.
## 6 Discussion
Although the proposed method achieved good results in this study, if factors as the precipitation or water level suddenly change, or an unusual rainfall pattern occurs, the error in point-based displacement predictions will inevitably increase. Thus, solving this problem becomes highly necessary [9]. As the Shengjbao landslide is a wading landslide in the TGRA, the factors considered including precipitation, water level and displacement. One should, in the future, consider means to develop prediction approaches for landslides with different triggering factors. There are three types of deformation zones divided on Shengjbao landslide. Any single point cannot represent the displacement characteristics of the entire landslide. To differentiate spatial zones of comparably the deformation characteristics and select the monitoring data of representative points are within these zones are vital preparatory steps for displacement prediction studies. According to the existed research on the application of ML techniques to landslide displacement prediction up to now, many hybrid models were established for prediction. However, this research mainly focus on the hybrid models of the coupled single ML models and models used to obtain optimal hyperparameters for these proposed single ML models. In this study, an ensemble model of the coupled LSTM and SVR algorithm with the weight coefficients based on linear combination theory was established for prediction, and the ensemble model performed well. Meanwhile, many approaches to landslide susceptibility mapping and landslide risk analysis have been developed [45; 46], including those based on artificial neural network [47; 48], support vector machine [49], alternative decision tree [36]. In order to achieve an accurate result in landslide susceptibility mapping and landslide risk analysis, it is necessary to research more advanced ML algorithms. Thus, the novel method proposed in this paper is recommended for conducting landslide susceptibility mapping, landslide risk analysis, and other fields.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 6 Advantages, Disadvantages, and Limitations  
While ultraviolet light has proven beneficial in food production, but still has a few drawbacks. Table 4 describes the key benefits, drawbacks, and shortcomings of Ultraviolet light treatment for food protection [1].  
In contrast to chemical disinfectants, UV does not introduce toxins or contaminants into product water and therefore does not modify the disinfected fluid's chemical constituent, flavor, scent, or pH. It is particularly essential in the milk sector, where chemicals can produce off-flavors and modify the basic characteristics of the product by dosing the entering treated water. Since UV light does not have a residual impact, the ideal place for the treatment plant would be right before the usage. This assures the deactivation of incoming microbial pollutants and a very low likelihood of contamination after treatments. Other than this, it can also disinfect water that has direct contact with the product, thus allowing process water to be reused, cuts costs and improves efficiency without compromising quality standards [2].  
There is another mode of UV light than continuous mode and it is termed as pulsed UV light mode. In this mode, the Ultraviolet light is held in a capacitor and discharged in the form of small pulses which enhances the immediate intensity of energy. As a result, pulsed UV light tends to generate more immediate energy in comparison to continuous UV light for the very same power provided. The inactivation of microorganisms by pulsed UV light irradiation is perhaps faster and better than continual UV-light, as the energy multiplies by many multiple sets. Furthermore, the continuous and pulsed UV sources create wavelength spectrums between 100-400 nm and 100-1100 nm respectively [45].
## 6 IoT Encapsulation Network Layer Standards
### 6 Lo  
IPv6 over a network of resource-constrained nodes (6 Lo) covers data-link layers that are not addressed by 6 LoWPAN and 6 TiSCH (discussed in the coming section). IEEE 802.15.4 and IEEE 802.15.4e do not cover protocols and they are covered in 6 Lo. Bluetooth technology, near-field communication (NFC), industrial sensory network, and 1 GHz Wi-Fi (802.11 ah) are modified for the development of 6 Lo [39]. Recent advancements in 6 Lo are tabulated here in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Description and Characteristics** & **Area/Application** \\ \hline \multirow{4}{*}{IPv6 over Bluetooth} & Enabled in Bluetooth v4.1, 6 LoWPAN, & Mobile healthcare, networked computing, \\  & Peer-to-Peer comm., Fragmentations, link-local & headsets and enhancements, notebooks, \\  & addresses, stateless IPv6 auto-configuration, & sensing smart meters, appliances, etc. \\  & neighbor discovery, header compression & \\ \hline IPv6 over IEEE RS-485 Master Slave/Token & Master/Slave/Pole control frames, & Used for supporting large payloads in \\  & Passing (MS/TP) networks [40] & 256 / 2 addresses for master and slave, & building automation and control networks \\  & uses LoGAC encapsulation & & within 1 km diameter \\ \hline \multirow{2}{*}{IPv6 over DECT/ULE [37]} & Low-Power Operation, Extended Security & Home automation, climate control \\  & (64 bit AES), dedicated radio frequency link & \\ \hline \multirow{4}{*}{IPv6 over NFC [41]} & Peer to peer, read/write mode, and card & Payments via phone, passive and active tags \\  & emulation modes, 80\% packet success rate, & based electronic token system, control NFC \\  & 1 to 10 cm range & devices with active tags \\ \hline \multirow{4}{*}{IPv6 over IEEE 802.11 ah [33; 37]} & Uses extended ISM band (1 GHz), typically & Smart sensors and meters, backhaul \\  & with wider range and low SNR, multi hop & aggregation using 802.11 ab routers/gateways, \\ \cline{1-1}  & relay and mesh networking, Supports MIMO & extended hotspot range and cellular offloading \\ \cline{1-1}  & & using sub-1 GHz band \\ \hline \multicolumn{3}{c}{IPv6 over Wireless Networks for Industrial} & \multirow{2}{*}{Improved stack architecture,} & Connects industry to the internet, connecting \\ \multicolumn{3}{c}{Automation Process} & & individual process data together for \\ \multicolumn{3}{c}{Automation (WIA-PA) [35; 39]} & & management in industry \\ \hline \hline \end{tabular}
\end{table}
Table 6: 6 Lo Frontiers.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 2 Background on Concepts and Requirements
### Privacy Requirements in Healthcare  
Effective privacy management in healthcare is based on a solid framework of rules, regulations, and policies that define how medical data are collected, managed, stored, and shared. Examples of these frameworks include the GDPR, HIPAA, and the ETSI EN 303 645 standard [10] for cybersecurity in the IoT. Compliance with these privacy requirements is critical to developing successful healthcare privacy solutions. A lack of compliance could compromise the strategies employed to protect the confidentiality, integrity, availability, and accountability of healthcare data. The following requirements serve as the foundation for securing sensitive medical information in healthcare:  
* Confidentiality Requirement: Healthcare data remain confidential (not disclosed to unauthorized parties) through access control and encryption techniques.
* Integrity Requirement: Solid data validation methods are implemented to provide data accuracy and consistency.
* Availability Requirement: The backup and disaster recovery policy guarantees that approved entities have access to the healthcare information.  
Figure 1: Core dimensions of privacy.  
Accountability Requirement: Audit trails and monitoring mechanisms manage accountability, confirming that the use and disclosure of healthcare data are in compliance with laws and regulations.
* Access Control Requirement: Access control ensures that only authorized parties have access to patient medical information on a need-to-know basis.
* Data Minimization Requirement: The collection of patients' personal information is limited only to that which is necessary for the provision of healthcare services.
## 3 Results and Discussion
### Effects of Various Parameters on MB Removal
#### 3.2.2 Effect of Contact Time  
In this study, the optimal contact time for the removal of the methylene blue (MB) dye using the ZGO2.5, ZGO5, and ZGO10 nanocomposites was determined. The efficiency of these composites was compared to that of their individual components graphene oxide and zeolite. To achieve this, the decrease in MB concentration (100 mg L\({}^{-1}\)) was monitored as a function of the contact time.  
A significant reduction in the concentration of methylene blue (Ce) was observed upon contact with graphene oxide (GO). This decrease occurred mainly during the first 30 min, reaching equilibrium at around 50 min (Figure 6a). The adsorption capacity of GO (qt) approached 90 mg g\({}^{-1}\) (Figure 6b).  
In contrast, pristine zeolite allowed for a slower and less efficient reduction in Ct during the first 60 min. The minimum methylene blue concentration in the solution was reached at approximately 120 min, with an adsorption capacity of qt = 37 mg g\({}^{-1}\).  
The difference in adsorption efficiency between GO and pristine zeolite is attributed to their distinct porous structures and physicochemical properties. Due to its laminar structure and high specific surface area [46], GO exhibits a superior adsorption capacity compared to pristine zeolite.  
The compounds ZGO2.5, ZGO5, and ZGO10 exhibited adsorption kinetics intermediate between those of pristine zeolite and GO, reaching equilibrium in approximately 70 min. In all three cases, dye removal was close to 90%. The enhanced adsorption capacity compared to that of pristine zeolite suggests a synergy between GO and zeolite in these  
Figure 6: Contact time dependence of (**a**) MB concentration (Ct) and (**b**) adsorption capacity (qt) for pristine zeolite and GO; (**c**) MB removal efficiency (RE%) vs. contact time for the composite adsorbents.  
compounds. This synergy could be attributed to a greater availability of active sites and a possible cooperative interaction between the two materials, facilitating faster and more efficient dye adsorption.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 3 Network and Jamming Models
### Jamming Model  
The Reactive Jamming Attack (RJA) model was first defined in ref. [18]. In this model, a jammer continuously scans all the available channels in the network to sense some activity. _As soon as_ it senses a signal on some channels, it injects an adversarial signal (we call this signal _Noise_ in this paper) to drastically decrease the signal to noise ratio and communication throughput of that channel.  
This paper utilizes the RJA model explained above. Generally, the duration for the transmission of _Noise_ by the jammer is larger than or equal to the duration for transmission of a trigger node. For instance, let the duration for transmission of a trigger node be \(\Delta t_{t}\) and the duration for the transmission of _Noise_ from the jammer be \(\Delta t_{j}\). Then if \(\Delta t_{t}>\Delta t_{j}\), after \(\Delta t_{j}\) time the jammer will be done with its transmission, but still it will sense some signal on the channel. So, the jammer will again start sending the _Noise_ signal and it repeats this until it detects no signal on the channel. Therefore, \(\Delta t_{t}\) should be less than or equal to \(\Delta t_{j}\).
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 1 Introduction
Generally, phenotyping is limited not only by the absence of suitable approaches but also by the fact that it is an expensive activity. The cost depends not only on the choice of appropriate tools (e.g., sensors) but also on their carrying platforms (e.g., speed and volume of data extraction and analysis, handling, and data processing). As reported by [PERSON] et al. [15], low-cost devices based on phenotyping applications based on vegetation indexes assessments are highly desirable. In detail, the goal, and innovation, of field-practical phenotyping is to implement high-performance and cost-effective precision phenotyping systems under real-world conditions while making analysis techniques more user-friendly [16]. This work aims to test the ability of a phenotyping prototype system constituted by an electric phenomobile integrated with a MAIA multispectral camera for real in-field plant characterization. This was done by acquiring the spectral signatures of F1 hybrid Elisir (Olter Sementi) tomato plants and calculating their vegetation indexes.
## 3 Verification Results
### Case Study 1  
The first case study which was used to test the SyPreF included a timing belt tooth (Figure 10(a)), specifically--a tooth part which has a factory error (Figure 10(b)). Specific features of this case study are very small object dimensions and non-contact 3D digitization, which was performed by a laser triangulation sensor (Figure 11). The resulting point cloud (Figure 12(a)) consists of 1,482,000 points and contains substantial errors which are inherent to this non-contact method, and it also features high resolution (5 \(\upmu\)m).  
Point-data pre-processing was started by the application of the tools for cross-sectional filtering/smoothing of point data. Primarily filtering by the angle method (\(\alpha=10^{\circ}\)) was conducted. The result was 489 filtered and 1,481,511 retained points (Figure 12(b)). Bearing in mind the application of the powder layer sprayed on the object before the 3D digitization, smoothing of point data by the median method (with \(N=5\)) was performed after the filtering (Figure 13(a)).  
Figure 12: **(a)** Result of 3D digitization (with magnified detail). **(b)** Result of filtering by the method of angle.  
Figure 13: **(a)** The result of point data smoothing. **(b)** The result of point data reduction.  
The next phase of pre-processing was the extraction of point data in cross sections. In order to efficiently reconstruct the surface model, the cross-sectional resolution was changed from 5 to 10 \(\upmu\)m. In this way the number of cross-sections was lowered from 3,000 to 1,500, which in turn decreased the number of points to 494,332.  
A special focus here was on the tooth part with a factory error, so the frontal segment of the point data was extracted from the whole by application of a volume filter. According to this, the point data reduction was performed on the point cloud consisting of 263,791 points in 801 cross-sections. The reduction was performed by the fuzzy-chordal method with \(MAD\) = 0.01 mm. Results are shown in Table 1 and in Figure 13(b). The generated cross-sectional curves and the resulting surface models are shown in Figure 14.  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline MAD & 0.01 mm \\ Maximum error & 0.00968 mm \\ Average error & 0.00159 mm \\ Reduction level & 65.69 \% \\ Number of retained points & 90,494 \\ Reduction time & 73.38 min * \\ \hline \end{tabular}  
* Obtained on PC configuration: _Intel i7-870 (8M Cache, 2.93 GHz); DDR3 Kingston 8 GB 1,333 MHz._  
\end{table}
Table 1: The results of point data reduction.  
Figure 13: Cont.
## 4 Proposed Architecture Deployment
### Metadata and Users' Preferences Interpretation  
The impact column of Table 1 is defined by positive impacts that a device can cause in the room--in other words, the system only considers impact as an increase reaction in the metadata value. For instance, the Fan Heater device has an increasing impact on the room's temperature. Currently, the system does not consider negative or decreasing, impacts. The system uses Boolean values to represent the device's impacts, to enable negative impacts the system should consider another type of values, such as integers or bytes.  
The system uses metadata to combine same zone devices according to the measured values and the impacted values. In this case, only two matches are found:  
* The temperatures value measured by the intelligent desk light are affected by the fan heater;
* The shutters status measured by the intelligent light control are affected by the same device; intelligent light control.  
The users' preferences enter the system as triggered values that are able to turn off subsystems. For instance, if the temperature of room N 115 reaches 24 \({}^{\circ}\)C, IoH will automatically turn off the heating devices of that room (i.e., Fan heater). Moreover, IoH can manage subsystems according to users' preferences, as will be seen in Section 5.3. To enable this control, the same mapping of measured/impact is performed between users' preferences and the devices' impact values. The final matches are:  
* The fan heater device has an impact in the temperature of office N 115, where the users' preference is established for 24 \({}^{\circ}\)C;
* Intelligent HVAC control subsystem has an impact in the temperature of office N 111, where the users' preference is established for 24 \({}^{\circ}\)C;
* Intelligent light control subsystem has an impact in the light of office N114, that is defined for 500 lux in the users' preferences;
* Intelligent desk light subsystem has an impact in the light of office N115, that is defined for 1000 lux in the users' preferences.  
The mapping between measured values and impact values, and the mapping between measured values and users' preferences enable the smart control of IoH using third-party devices and subsystems. IoH will actuate on top of these devices and subsystems to provide a top-level control to improve users' comfort. IoH is also able to produce security warnings and alarms, as will be seen in Sections 5.4 and 5.5.
# Combination of Permanent and Variable Load Is Dependent
## 1 Introduction
### Review  
A comprehensive review on load combination has been given recently in [7]. The current hypotheses for the combination of the permanent and the variable load is based on random load pairs with random single loads, the ILC and load reduction [1; 7; 8; 9; 10; 11; 12; 13; 14; 15].  
The structural codes and articles regarding codes are divided into three accuracy levels: I, II, and III.  
The Eurocodes [1] were earlier based on level I, i.e., on deterministic, historic, and empiric methods.  
The safety factors of the current Eurocodes are based on level II, i.e., on the first order reliability method. The primary assumption of this method is the independent load combination where a load reduction occurs. It is implemented by sensitivity factors \(\alpha_{\rm E}\) and \(\alpha_{\rm R}\), which decrease the target reliability and cause the load reduction as in this load combination it is improbable that the highest permanent and variable load occur simultaneously.  
The accuracy level III is a full probabilistic method which is not applied in any codes so far.  
In the current codes, the load combination is ambiguous:  
1. In the ULS design, the permanent and the variable loads are often combined under the assumption that the loads are statistically independent, and the ILC is applied although this is not consistent throughout the codes [1]. However, the DLC is always applied in the SLS design. In the ULS the loads are high and therefore a load reduction is applied in the ILC. In the SLS the loads are low and the theory of the ILC correspondingly demands a load increase but it is not applied.
2. Permanent loads are independent with each other in the same way as the permanent and variable loads which suggests the ILC. However, the DLC is always applied.  
Thus, one can argue that the codes do not always treat the load combination consistently. This article suggests that permanent and variable loads should be combined dependently, an approach which would remove any ambiguity related to choosing the combination of these loads.