esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Analysis of Organic Ester Functional Groups in Carious Dentine
###### Abstract  
Background: With the implementation of minimally invasive selective caries removal protocols to treat cavitated, deep cautious dentine lesions, there is a need to investigate specific biochemical moiety distributions to help characterise and distinguish between infected (contaminated) and affected (demineralised) zones within the dentine lesion. The present in vitro investigation aimed to compare the distribution of ester functional groups (I740 cm\({}^{-1}\)) within cautious dentine tissue (infected and affected dentine). The null hypothesis stipulated that there are no differences in ester function intensity/distribution within cautious dentine lesions. Materials and Methods: From a total of five extracted human molar teeth with various dentine lesions, 246 points from 10 sections of carous dentine were examined using high-resolution Raman spectroscopy and characterised into infected, affected and sound dentine. The peak intensity of the characteristic vibration mode of the ester function was calculated from sample scans. Results: Analyses indicated a statistically significant difference in the spectroscopic vibration bands of esters between the infected and affected dentine zones. Conclusion: The ester functional group is higher in intensity in the caries-infected dentine zone compared to the affected tissue. This finding could be used to develop an objective indicator for the selective operative management of carous dentine.  
cites-infected; caries-affected; dentine; Raman spectroscopy; lucifer yellow; amide I; ester; minimally invasive dentistry; caries +
Footnote †: journal: Applied Sciences
## 4 Methodology
### Damage Classification Model  
The next step was to assess building damage levels using the proposed dual-branch UNet architecture. Both pre- and post-disaster images were fed into the dual-branch network, which was conceptualized based on the well-known Siamese-based networks [18] that perform semantic change detection or building damage assessment [36; 46; 57]. The backbone network that was used in this step was the already trained building localization UNet mentioned in the previous section. Having the weights to be shared between both branches in the initialization step, the trained network helped the dual-branch model to obtain more reliable deep features from both pre- and post-disaster images and converge faster. Deep features were then concatenated into a larger feature map which was then entered into a segmentation head containing convolutional and batch-normalization layers. Figure 9 illustrates the schematic diagram of such a dual-branch Siamese-UNet with similar weights. The proposed two-stage, dual-branch framework is capable of considering the spatial and temporal attributes of pre- and post-disaster images at the same time and, consequently, improves the building segmentation and damage assessment results.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 5 Conclusions and Future Work  
The vibration signal contains the information of gearbox wear deterioration progression, since tooth wear has significant effects on gear dynamics. The theoretical analysis reveals that MSB-SE can include sideband pairs simultaneously and suppress the random components effectively. The MSB-SE peak will appear at the bifrequencies, which are harmonics of the meshing frequency and multiples of the shaft rotational frequency. This study physically shows that the MSB-SE-based vibration signals are an effective approach to accurately extracting sideband feature signals from complex modulation signals. The peak amplitude of MSB-SE and that of MSB-SE coherence relating to characteristic frequencies can give more accurate information of a gear transmission system when taken together. Based on the MSB-SE peak amplitude, a dimensionless indicator is proposed in this paper to assess the effect of the gear wear process. The results show that the indicator can provide more detailed and realistic information about the stable status of gear wear progression and the fast deterioration of its accumulated effects.  
Obviously, further studies should be made to verify the proposed schemes for different wear process based on extensive experiments and theoretical analysis. Particularly, the threshold of the proposed indicator and remaining useful life prediction for gear transmissions still deserve further investigations.  
The support for this research under the Chinese National Key Technology Research and Development Program (Grant No. 2014 BAF08B01) and the National Natural Science Foundation of China (Grant No. 51375326) are gratefully acknowledged. Without their financial support, this work would not have been possible.  
[PERSON] improved the algorithm and wrote this paper; [PERSON] analyzed the data; [PERSON] and [PERSON] conceived the experiments and designed the experiments; [PERSON] provided some key ideas and revised the manuscript.  
The authors declare no conflict of interest.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 3 Background
### Internet of Things
#### 3.1.2 IoT Architecture  
There are different proposed IoT architectures in the literature--for example, middleware based, SOA based, six-layer and three-layer architectures [16]. In this section, for the purpose of addressing the basic communication, this SLR focuses on the basic three-layer IoT architecture. The three layers are a perception layer, a network layer and an application layer, as follows:  
* The perception layer is a physical devices and communication layer that consists of sensors and actuators that aggregate, sense and process data, and then transmit the data to the network layer. This layer contains physical objects such as cameras, RFIDs and baby monitors.
* The network and transport layer is a communication layer which transmits and routes the aggregated data from the perception layer to the application layer using different devices, such as gateways, switches, and routers.
* The application layer is a messaging layer containing the application that interacts with users. E-health, smart factory and smart transport fields all utilize such applications.  
As described in Figure 2, each IoT layer uses different protocols and standards [17; 18]. The physical devices and communication technology use standards such as WiFi, 4G/5G and LoRaWAN. The network and transport are use different protocols, such as IPv6, 6 LowPAN, RPL, TLS and DTLS. The application and messaging protocols include MQTT, CoAP, HTTP and XML. In addition, there are different protocols that are used for authentication and key management, such as Oauth2.0, OpenId and PKI.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 4 Results and Discussion
### Model Performance and Comparison
Therefore, the accuracy of the static ML training model is reliable for the ranges of values of \(H\), \(S\), and \(T_{r}f\left(t-1\right)\), previously given in Table 1. When the training set is large enough, dynamic neural networks perform better on inputting extrapolative datasets. In the other case [3], the RNNs and GRU have a higher accuracy than static ANNs in predicting PWP. However, due to the frequency of PWP measurement in this study case, which is not enough for dynamic neural networks to study the features accurately, the accuracy seems worse than for the static ML models. From the comparison results, it is summarized that when the database is small and low-dimensional in size, employing classical MLP is more efficient and accurate. When the database is small and has high dimensions, the RF seems more capable. When the database is large enough, with thousands or more datasets, deep learning techniques perform better and can learn more non-linear relationships from the inputs and target outputs. Consequently, for different case studies, the different ML methods should be attempted to make a comparison.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 3 System Model  
We consider a MEC system composed of a set \(N\) of MECSs. A MECS \(j\) is said to be a neighbor of a MECS \(i\) if they exchange their local information. Each MECS \(i\) is configured to have a set \(N_{i}\) of neighboring MECSs. The neighboring MECSs are connected by a backhaul network. we denote the communication bandwidth between a MECS \(i\) and the neighboring MECS \(j\in N_{i}\) as \(b_{i,j}\). The control time is divided into discrete time slots of length \(\tau\). Each MECS possesses a task queue to accommodate the tasks it receives. There can be different types of tasks and they can have different QoS requirements. A MECS can accommodate them by maintaining a separate task queue for each task type. Because multiple queues share the computational resources of a MECS, optimally scheduling them affects the resource efficiency and energy consumption of a MECS. The task service time scheduling problem in a MECS is investigated in [10]. Thus, in the work, we assume that tasks are homogeneous.  
We denote the length of the task queue in a MECS \(i\) at the beginning of a time slot \(t\) as \(Q_{i}(t)\). We also denote the task service rate of a MECS \(i\) as \(b_{i}\). The number of tasks arriving at a MECS \(i\) during a time slot \(t\) is denoted by \(a_{i}(t)\). Since the stochastic characteristic of \(a_{i}(t)\) is not known and varies in time and space, we do not make any assumption about the task arrival process. Instead, we measure \(a_{i}(t)\) at each time slot and devise a sleep control mechanism by using the measured \(a_{i}(t)\)s. \(a_{i}(t)\) can be subdivided into two groups. The first group is composed of the tasks offloaded directly from the devices to a MECS. We denote the number of tasks in the first group as \(a_{1,i}(t)\). The second group of tasks is composed of the tasks migrated from the neighboring MECSs that decide to sleep. We denote the number of tasks in the second group by \(a_{2,i}(t)\). Then, \(a_{i}(t)=a_{1,i}(t)+a_{2,i}(t)\). We assume a MECS serves the tasks in its tasks queue in a FIFO manner. Since the control time is divided into a time slot, tasks are migrated at the end of a time slot. Thus, the tasks in the second group are stored after the tasks in the first group.
## 4 Discussion
### Localized Adjustment of the Equivalent Factor Value  
This research clarifies the dynamics and spatial patterns of LUCs in the Southern Jiangsu urban agglomeration, employs the revised equivalence factor method to assess its ESV, and explores the responsive relationship between LUCs and ESV in the study area from 2000 to 2020. Furthermore, by utilizing the PLUS model in conjunction with relevant national land planning policy requirements, this study simulates and forecasts the trends in LUCs and ESV changes with four evolutionary scenarios by 2035. The study's findings  
Figure 11: ESV spatial patterns across envisioned scenarios.  
Figure 12: Differences in ESV grade distribution under simulated scenarios.  
indicate that farmland and water bodies play a dominant role in the ESV of the Southern Jiangsu urban agglomeration. This is attributed to the fact that farmland and water bodies are the primary land use types in the study area, with the ESV coefficient for water bodies being relatively high. Additionally, although the ESV coefficient for forest land is higher than that for farmland, the ESV for forest land areas is lower due to their smaller area. During the process of revising the equivalent factor values, to avoid the phenomenon of directly using coefficients without considering regional differences, this paper also refers to the actual situation of land use types in the Southern Jiangsu urban agglomeration. By adjusting the ESV coefficients on a nationwide basis according to local grain prices, the paper ensures that the values are more reflective of the actual conditions of the region. This method is consistent with related studies by scholars such as [PERSON] [31; 54], who used grain prices to adjust the equivalent factor values. The revised values range from CNY 1230/hm\({}^{2}\) to CNY 2602.91/hm\({}^{2}\), which, when compared to the revised equivalent factor value of CNY 2532.42/hm\({}^{2}\) in this study, shows that the revised figures are within a normal and reasonable range. This, to some extent, proves the scientific validity and feasibility of the method for revising equivalent factor values based on localization.
## 2 Impact of Process Variations (PV) on Different Operation Modes
### Impact of PV on SRAM Read/Write Failures  
While the strength mismatches between different transistors of an SRAM cell are good to skew its power-up value to resist SPUF response bit flipping under temporal variations in operational condition, they can increase the read failure (RF) and write failure (WF) rates in the memory-mode. RF occurs when the voltage (\(V_{read}\)) sensed at BIT (or BITB) rises above the inverter read trip point (\(V_{tripRD}\)) to cause the bit stored in the SRAM cell to flip accidentally (see Figure 6a). As shown in Figure 1b, when the bit-line \(BL\) is precharged, the access transistor M6 and the pull-down transistor M4 act as a voltage divider. The PV induced voltage deviation at BITB may trigger a flip of the data  
Figure 5: Three types of SRAM cells: (a) 0-skewed cells, (b) 1-skewed cells and (c) neutral cells.  
bit stored in the SRAM cell [14]. To avoid RF, the relative strength of M4 and M6 (known as beta ratio (\(\beta_{npd-nax}\))) should be designed larger, where \(BR_{npd-nax}\) is given by  
\[BR_{npd-nax}=\frac{\beta_{npd}}{\beta_{nax}}=\frac{\beta_{M4}}{\beta_{M6}}=\frac {\frac{\mu_{eff}\zeta_{ox}W_{M4}}{L_{M4}}}{\frac{\mu_{eff}\zeta_{ox}W_{M6}}{L_{ M6}}}, \tag{4}\]  
where \(\mu_{eff}\) is the effective mobility and \(C_{ox}\) is the oxide capacitance. WF refers to the failure of SRAM cells to respond to the data bit written into it within an acceptable time (see Figure 6b) [14]. Consider the SRAM cell in Figure 1b, if \"0\" is to be written into the cell that stores logic \"1\", the voltage at BIT will be discharged to a low value. If this value is higher than the trip point of INV-2, the write operation fails and the stored data bit remains unchanged. To reduce the probability of WF, the beta ratio of the access transistor and pull-up p-channel metal-oxide-semiconductor field-effect transistor (PMOS) (\(BR_{nax-pup}=\beta_{nax}/\beta_{pup}=\beta_{M3}/\beta_{M2}\)) should be designed such that the write time is less than the word-line turn-on time.  
It is possible to reduce the probability \(\textbf{Pr}(\text{RF};\text{WF})\) of memory access failures by manipulating the transistor sizes [14]. The relationship between \(\textbf{Pr}(\text{RF},\text{WF})\) and the sizing of each transistor in an SRAM cell is shown in Figure 7. Curve fitting by polynomial functions shows that sizing up the transistors helps to reduce failure probability. This is expected since \(\sigma_{V_{th}}\) decreases when the size of transistors increases according to Equation (1).  
Figure 6: (**a**) Read and (**b**) write failure effects in an SRAM cell. The original bit preserved in the cell is 1.  
Figure 7: The relationship between the failure probability and the transistor widths (**a**) M1; (**b**) M2; (**c**) M3.
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 3 Results
### PP Prototype  
The Road Sensitivity (_RSek_) prototype aims to identify potholes using real-time data from the gyroscope sensor integrated into the MPU-6050 module. The gyroscope captures angular velocity changes, providing insights into abrupt vertical displacements indicative of a pothole. This analysis ensures timely detection and reporting of road irregularities to enhance route safety and comfort.  
Potholes are identified by analyzing gyroscopic values, particularly abrupt variations in angular velocity across the Z-axe. These values are monitored and processed using threshold-based algorithms. The thresholds are set to capture sudden spikes typical of vertical oscillations caused by potholes. Based on experimental and field tests, a threshold of 0.8 rad/s is flagged as a potential pothole encounter. After exceeding the threshold, a record with the latitude and longitudinal coordination of the identified event is added to the database.  
Figure 6 illustrates the temporal variations in the gyroscopic Z-axis angular velocity as a vehicle encounters a pothole over 100 s. Initially, the gyroscopic readings are relatively constant, reflecting the vehicle's steady movement on a smooth road surface.  
After 30 s, a sharp spike in angular velocity is observed, marking the moment the vehicle interacts with the pothole. This abrupt variation exceeds the defined threshold (approximately 1 rad/s), confirming the presence of a road anomaly. The gyroscopic readings gradually stabilize as the vehicle resumes normal motion on a smoother road.
## 5 Experimental Verification
### Experimental Verification with \(k<1\)  
Figure 18 presents the experimental waveforms of the EIOS-TPS modulation strategy under a voltage conversion ratio of \(k=1.5\) (\(V_{1}=80\) V,\(V_{2}=120\) V). When the converter operates in mode G with an output power of 160 W, the transformer voltage and inductor current waveforms in Figure 18a closely match theoretical predictions. The theoretical current stress is 7.3 A, while the measured value is 6.6 A. As shown in Figure 18b, all the switches in the DAB converter achieve ZVS.  
Figure 16: Experimental waveforms of low power (313 W) by using EIOS-TPS when \(V_{1}=80\) V and \(V_{2}=53.33\) V. (**a**) Operation waveforms of the voltages and current. (**b**) Switching waveforms.  
Figure 17: Current-stress variation with output power \(P\) when \(V_{1}=80\) V and \(V_{2}=53.33\) V.  
Figure 18: Experimental waveforms of low power (313 W) using EIOS-TPS when \(V_{1}=80\) V and \(V_{2}=120\) V. (**a**) The waveforms of the voltages and current during operation. (**b**) Switching waveforms.  
Figure 19 presents the experimental waveforms of the EIOS-TPS modulation strategy under a voltage conversion ratio of \(k\) = 1.5 (\(V_{1}\) = 80 V, \(V_{2}\) = 120 V). When the converter operates in mode D with an output power of 1000 W, the transformer voltage and inductor current waveforms in Figure 19a closely match theoretical predictions. The theoretical current stress is 21 A, while the measured value is 20.2 A. As shown in Figure 19b, all switches in the DAB converter achieve ZVS.  
Figure 20 shows the experimental current stress versus transmitted power for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS under a voltage conversion ratio of \(k\) = 0.667. When the converter operates in mode D with an output power of 706 W, the measured current stress for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS is 11.17 A, 7 A, 6.8 A, and 6.6 A, respectively. The current stress of CSO-TPS and EIOS-TPS is nearly identical, while EIOS-TPS achieves a 40.9% reduction compared to SPS. At \(P\) = 706 W, the measured current stress for SPS, CSO-DPS, CSO-TPS, and EIOS-TPS is 19 A, 18 A, 17.25 A, and 17 A, respectively. EIOS-TPS achieves a similar current stress to CSO-TPS, with a 10.5% reduction compared to SPS. These results confirm the advantage of EIOS-TPS in reducing current stress.
## 6 Use Case: Cyber and Physical Attacks against a Water Critical Infrastructure
### Jamming Signal  
In case the attacker decides to perform a jamming signal to block the communications of the target infrastructure, the cross-layer data analytic platform can use the Jammer detector (JDet) to identify these events in real time and alert the SCADA administrators.  
Jamming events are sent to the XL-SIEM agent using a predefined format, which will trigger an alert in the platform. JDet generates an alert when a jammer generates noise in the area covered by the sensor. An example of the events received by the XL-SIEM from JDet is as follows:* Dec 18 12:24:04 localhost SDRJD[769]: INFO:sdrjdysylog:[\"user\": \"prototype\", \"jnr\": 20.364, \"event_duration\": 82025, \"nodeld\": \"3\", \"srclp\": \"172.16.4.235\", \"dstlp\": \"192.168.66.6\", \"time\": \"2019-12-18T12:22:40.000\", \"freq\": \"241200000\", \"type\": \"Pulsed\", \"event\":\"Attack Ended\"] Events coming from the JDet and processed by the XL-SIEM are normalized with the platform format. Examples of the security events coming from the NTSA and displayed by CLAP are given bellow:
* Pulsed; 2019-12-18 13:24:03; xlsiem-server; 172.16.4.235; 192.168.66.6; 5
* Jammer detector; 2019-12-18 13:25:41; xlsiem-server; 162.12.144.202; 192.168.66.6; 5 Similar to the log processed from Suricata, the events coming from the JDet indicate a name (signature), a date where the event was received by the CLAP, the sensor in charge of the detection (in this case the xsiem-server), the IP source and destination (here ports are not indicated), and the risk level (in this case 5 means a medium level of risk).
## 4 Case Study Results and Discussion  
The distribution system selected for the simulations is the one taken from the test feeders of the Distribution System Analysis Subcommittee of the Institute of Electrical and Electronics Engineers (IEEE) [34], shown in Figure 8. The main characteristics of the distribution network on which the short circuits were simulated are the following:  
* The voltage across the distribution network is 15 kV.
* The short-circuit power of the external network will be 100 MVA.
* The power demand of the electrical loads connected to the network is 12 MW and load flow analysis they have been considered balanced.
* Overhead network with double circuit with LA-110 conductor. The electrical characteristics of this conductor are shown in [35].  
Three-phase and single-phase short circuits were simulated on the distribution network, at busbars 2, 7, and 21, as shown in Figure 8, and with different DG penetration levels ranging from 33% to 50%. A first distributed generation unit, DG1, was installed at busbar 20, whereas a second one, DG2, was installed at busbar 22.  
The DG protection device should be properly coordinated with the other protection devices in the distribution network. When a fault occurs on the line to which the DG unit is connected, the DG unit must be disconnected before the protection device on that line is disconnected. This ensures that the DG does not cause disturbances in the operation of the line protections. Furthermore, when a fault occurs on a branch line, the DG must remain in service and will not disconnect before the fault line does [8]. According to this, the coordination criterion shown in Table 2 is proposed.  
The transformation ratio of the current transformers used in the test is 400/5, chosen according to the full-load currents of the system and the short-circuit currents detected by each of the protection devices according to the location of the fault (shown in Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bus** & **Location Faults** & **Main Protection** & **Backup Protection** \\ \hline
2 & F2 & OCR2 & OCR1 \\
7 & F7 & RC & OCR1 \\
21 & F21 & RC & OCR3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordination criteria according fault location.  
Figure 8: Modified IEEE 34-node test feeder system [34].