esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### ESLoss Function  
Datasets of student classroom behavior often suffer from sample imbalance issues. In most cases, students' behaviors are concentrated on simple samples such as read and lookup, resulting in a large number of samples for these two behaviors. In contrast, other difficult samples such as stand, raise hand, and turn head are relatively sparse and easily occluded by other behaviors, leading to poor model training performance. This issue caught our attention. In this work, we designed an improved version of the Slide Loss [43] function to address this problem, which we call ESLoss (EMASlideLoss). The distinction between simple and difficult samples is based on the IoU between the predicted box and the ground truth box. To reduce hyperparameters, the average value of the IoU values of all bounding boxes is used as the threshold \(\mu\). Samples with an IoU of less than \(\mu\) are considered negative samples, while those with an IoU that is greater than \(\mu\) are considered positive samples.  
However, due to unclear classification, samples near the boundary often suffer significant loss. We hope the model can learn to optimize these samples and make full use of them to train the network. However, the number of such samples is relatively small.  
Therefore, we attempt to assign higher weights to difficult samples. First, the samples are divided into positive and negative samples using the parameter \(\mu\), as shown in Figure 5. Then, the boundary samples are emphasized through the weighting function Slide. The specific process of the Slide weighting function is as follows:  
\[f(x)=\begin{cases}1,&x\leq\mu-0.1\\ e^{1-\mu},&\mu-0.1<x<\mu\\ e^{1-x},&x\geq\mu\end{cases} \tag{11}\]  
The function \(f(x)\) represents the sliding function operation, where \(x\) denotes the Intersection over Union (IoU) between the predicted box and the ground truth box, and \(\mu\) denotes the weighting threshold. Specifically, the SlideLoss method uses the average IoU of all bounding boxes as the threshold, considering values below \(\mu\) as negative samples and values above \(\mu\) as positive samples. In this study, we adopt the concept of Exponential Moving Average (EMA) to optimize the parameter \(\mu\) of the model. The specific optimization method is as follows:  
\[\mu_{t}=\beta\times\mu_{t-1}+(1-\beta)\times\theta_{t} \tag{12}\]  
Here, \(\theta_{t}\) represents the parameter weight obtained in the \(t\)-th update, and \(\mu_{t}\) represents the moving average of all parameters in the \(t\)-th update. \(\beta\) is the weighting parameter. The moving average can be regarded as the average value over a certain period of time. Compared with exponential functions, the moving average smooths the path of values, reduces jitters, and avoids significant fluctuations caused by occasional outliers. The moving average can enhance the robustness of the current model's performance in detecting student behaviors.
###### Abstract  
This article deals with the analysis of the accuracy of the geodetic real-time GNSS measurement procedure used in railway operating conditions in the Czech Republic. The purpose was to determine to what extent the operating conditions affect the accuracy of the measurement result and whether an accuracy of standard deviation \(\sigma_{x,y}=5\) mm in the horizontal plane could be achieved. The use of geodetic GNSS equipment with an IMU unit was also tested. The accuracy obtained in operational conditions is compared with the accuracy obtained on a calibration base using the same measurement procedure. The consistency between the accuracy of the primary system (satellite-based) and the secondary system (terrestrivially measured by the traverse method) is also discussed. The analysis includes the issue of residual inhomogeneities of the uniform transformation key when converted to the Czech national coordinate system S-JTSK. It is shown that a homogeneous accuracy in coordinate standard deviation better than \(\sigma_{x,y}=5\) mm can be achieved. The results indicate that the accuracy under operational conditions is two-three times worse than the accuracy achieved by the same procedure under ideal conditions on a calibration base. This is due to the non-ideal observing conditions, i.e., horizon occlusion by overlays, surrounding vegetation and multipath effects. It has been shown that the effect of multipath can be reduced by repeating short observations 3-4 h apart. Older GNSS instruments using an IMU unit in combination with an electronic compass (eCompass) are at risk of a systematic bias of up to several tens of millimeters, which can be detected by rotating the antenna by 180\({}^{\circ}\). The current uniform transformation key used in the Czech Republic for the conversion of GNSS coordinates into the national system has residual geometric inhomogeneities (\(p=0.90\) to 10 mm/km, sporadically up to 20 mm/km), which metrologically deteriorate the results of the calculation of the terrestrivially measured secondary system inserted into the GNSS measured primary system. Achieving homogeneous accuracy in coordinate standard deviation in a horizontal plane better than \(\sigma_{x,y}=5\) mm has been demonstrated in non-ideal railway operating conditions with increased risk of multipath. The innovative aspect of the approach used is that it simplifies and thus increases the efficiency of the measurement with respect to the availability of GPS, GLONASS, Galileo and BeiDou satellites, as well as reducing the effect of multipath on the noise by repeating the measurement procedure.  
GNSS; railway track; accuracy; primary system; secondary system +
Footnote †: journal: Applied Sciences  
_Applied Sciences_
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 2 Requirement for Dispatch-Able Substation
### Frequency and Voltage Support Operation  
For the interconnection of DIRs to a bulk power system (BPS), technical regulations or requests should be satisfied. The IEEE 1547 standard (revised in 2018) describes the requirements for the interconnection of distributed energy resources (DERs), including power quality, reactive power capability, and response to abnormal conditions. In [22], the allowable voltage variation induced by a DER was limited to 3-5% per second of its nominal voltage. Furthermore, the reactive power control capability of DERs, which is not included in most of the existing DERs, may become mandatory for voltage control by utilizing volt-VAr curves. In terms of response to abnormal conditions in a BPS, allowable operating modes, including mandatory operation and momentary cessation modes, are defined according to the voltage and frequency. Ref [23] introduced regulations not only for the disturbance withstand capability on abnormal voltage, frequency, and rate-of-change-of-frequency (RoCoF), but also for grid support functions, such as reactive power support and frequency response.  
The regulations applicable for interconnection consistently demand DIRs to act as conventional generators with various support functions. However, considering the unique characteristics, including intermittency and variability, it is difficult for RESs to solely act as conventional synchronous generators. Specifically, satisfying these regulations could be challenging for DIRs that have already been installed based on the assumption of no further modifications.  
In a contingency situation, a DSS is required to support frequency mitigation by supplying or absorbing active power. The droop response of synchronous generators can be easily replaced by the fast response of the ESS in a DSS. Figure 3 shows the frequency support response of a DSS. \(\mathbf{R}_{ESS}\) refers to the droop response of a DSS that makes the active power response of the ESS proportional to the frequency deviation. The coefficient \(\mathbf{K}_{IR}\) is related to the inertia response by creating a response that is proportional to the derivative value of the frequency. Theoretically, this response can create synthetic inertia for the power system and mitigate the rate of change of frequency [24].
# The Impact of Drilling Parameters on Drilling Temperature in High-Strength Steel Thin-Walled Parts
## 1 Introduction
Previous studies have employed experimental or numerical approaches to investigate the drilling temperatures of several typical high-strength steels, uncovering some fundamental laws closely related to their thermal behaviour. However, AF1410 steel possesses distinctive properties, and for the specific application of drilling thin-walled AF1410 components, the influence of drilling parameters on temperature remainsinsufficiently addressed in the literature. This gap highlights the scientific value of the present study. In summary, due to the difficulty of drilling temperature detection, although more studies on drilling temperature have been carried out through simulation and thermocouple measurement, there are still problems such as large differences in the actual drilling temperature, and there are fewer studies on the temperature change law of the drilling process of thin-walled parts made of high-strength steel and the impact of drilling dosage on the drilling temperature. Therefore, this paper takes the thin-walled parts made of AF1410 high-strength steel as the research object, and designs a specialized. Therefore, this paper takes AF1410 high-strength steel thin-walled parts as the research object, designs a special fixture, and carries out real-time dynamic observation of the temperature change on the bottom surface during thin-wall drilling by using infrared thermography. This paper focuses on the evolution of the temperature field on the bottom surface of AF1410 high-strength steel thin-walled workpieces during drilling under fixed constraints on two opposite edges, and investigates how varying drilling speeds and feed rates influence this temperature field.
## 2 Principle of Operation
### Parity Generator  
A parity generator is a digital circuit that generates a parity bit based on a set of data bits. The purpose of the parity bit is to provide a simple error-checking mechanism. Parity can be either even or odd, and the generator calculates the parity bit in such a way that the total number of set bits (either 1s or 0s) in a data word, including the parity bit, is either even or odd. If an error occurs during transmission, the parity check can help identify that an error has occurred. If the received data does not satisfy the chosen parity (even or odd), an error is likely, and corrective measures can be taken. The process involves utilizing XOR and XNOR logic gates, respectively, for even and odd parity generators, as illustrated in Figure 3. To generate an even parity bit (Pe), the data bits A and B are incorporated into CR-SOAs-MZI1, resulting in XOR1 = A \(\oplus\) B at output port 4, where \(\oplus\) denotes modulo-2 addition. Simultaneously, the digits C and D undergo processing in CR-SOAs-MZI2, producing XOR2 = C \(\oplus\) D at output port 8. These two outputs are then fed into CR-SOAs-MZI3, generating Pe = A \(\oplus\) B \(\oplus\) C \(\oplus\) D (i.e., XOR gate) at output port 10. A continuous wave (CW) beam is applied to all these three XOR gates. This approach utilizes CW as the probe light, effectively balancing power relationships and ensuring a constant power output. By doing so, it mitigates the fluctuations in carrier concentration within the active region when multiple lights are simultaneously incident on the (CR)-SOA [3]. Conversely, for odd parity generation (Po), the Pe output from the CR-SOAs-MZI3, along with a clock signal (CLK),  
Figure 2: Diagram and truth table for XOR logic gate utilizing CR-SOAs-MZI [8; 18].  
are routed through CR-SOAs-MZI4, resulting in \(\text{Po}=\overline{\text{A}\oplus\text{B}\oplus\text{C}\oplus\text{D}}\) (i.e., XNOR gate) at output port 13. CLK plays a crucial role in synchronizing the operations within the parity generator. It ensures that data processing stages occur in a coordinated and timely manner, providing a consistent timing reference for the precise alignment of input data streams, the CW beam, and the resulting optical interference patterns. This synchronization is essential for the accurate modulation and interaction of optical signals within the CR-SOAs-MZI framework, contributing to the reliable generation of the desired parity output. In this context, the CLK signal also comprises intensity-modulated RZ Gaussian-shaped pulses. This format helps optimize aspects such as bandwidth efficiency and signal-to-noise ratio, ensuring effective modulation and interaction within the CR-SOAs-MZI structure, thereby enhancing overall performance and reliability.
## 2 Enabling Satellites Technologies and Related Works
The architecture and messaging system allow for dynamic ship route updatesin the case of cold chain disruptions and can accommodate future expansions to monitor additional parameters like \(\text{O}_{2}/\text{CO}_{2}\) levels and cargo volume. The inherent limitation of such an architecture is that WiFi networks and gateways for WSNs are also required ashorc if containers are to be tracked not only at sea. In addition, although the guidelines issued by IMO (International Maritime Organization) and IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities) do not exclude the possibility of the transmission of extended security messages (Application-Specific Messages), which can be used to provide additional information about cargo [26], the current AIS messages do not provide adequate protection against cyber threats and have several cybersecurity vulnerabilities. The AIS system was designed in the 1990s and was not originally conceived to deal with modern cyber threats, which is why it will be difficult for it to be adopted by maritime operators to communicate economically sensitive information about the status of containerized cargo. The integration of more secure and up-to-date protocols in the \"backhauling\" approach was initiated by the ESA-funded IOT SATBACK project [27] (see Figure 3), which demonstrated a prototype of the E-SSA-based solution [28] for the backhauling NB-IoT network via the Ku-band. This technology has been further developed and integrated into the 5 GT System for maritime transport services by the 5G SENSOR@SEA project [2], which completed the field-testing activities in April 2023. The \"NB-IoT backhaul solution\" offers a real-time connectivity solution for container traffic monitoring with global coverage that exceeds the current capabilities of direct access on low-orbit satellites. In addition, even ashore, containers continue to be monitored thanks to NB-IoT cellular coverage, which, among IoT LPWAN networks, is the most extensive globally and continues to expand rapidly [29]. An updated review of recent work on applied research, enabling technologies and standardization initiatives in the scope of \"smart containers\", was presented in a recent article [30]. Since nothing newer has emerged in recent months, we refer to that article for a review of related work as of the date of writing this contribution.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Time Synchronization Requirements
#### 2.4.1 Energy Efficiency  
In the current protocols, the RBS protocol estimates clock drift by linear regression based on past clock phase offset which considerably increases the computational complexity and require large number of message exchanges. However, it saves energy consumption by not updating the local clock of the nodes which require expensive clock updates which makes the RBS protocol the most energy efficient method currently in use [33]. The TPSN has high energy consumption because of its computational and communication requirements [31]. It requires three message exchanges (Sync, Pulse, and Ack) which result in a high communication load. Clock drift computation is not complex but local clocks of the nodes are updated regularly which also increases energy consumption. The TS/MS protocols are also energy efficient by keeping the usage of storage space and computational resources low [34]. The FTSP is the most inefficient method because the number of message exchanges is high and local clocks of nodes are updated regularly which results in higher computational costs [32]. Limited energy resources available to sensor nodes should always be considered while designing the protocol for wireless sensor networks [35]. To achieve this goal, a technique for time synchronization should be designed with a minimum number of synchronization exchange messages.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 3 Proposed Effective PSCCH Searching
### Receiver Model
After that, the CP-OFDM demodulation is performed (line 6), the PSCCH candidate for subchannel \(n\) is extracted (line 7), the channel estimation using \(\mathbf{d}_{PSCCH}^{nj}\) is performed (line 8), and then the MMSE equalization is done using the estimated channel (line 9). Finally, we try to decode the PSCCH and SCI (lines 10-11).
## 3 Results and Discussions
The SNR is about 55 dB, indicating a stable mode-locking regime. By tuning the angle of the grating, a wavelength tunable Q-switched laser is achieved, as shown in Figure 6a. The wavelength is tunable from 2710 to 2810 nm. At a pump power of 1.4 W, the output power changes from 85 to 104 mW. The peak power is measured at a wavelength of 2760 nm, which is also the center of the tunable range. The power variations are caused by the gain of Er\({}^{3+}\) ions; see Appendix A, Figure A1. For our setup, the gain of the fiber is maximum at 2760 nm. The gain decreases as the spectrum increases and decreases, eventually becoming not high enough outside the tunable range. Figure 6b shows that as the wavelength increases, the repetition rate of the pulse decreases while the pulse width increases. In previous reports of wavelength tunable Q-switched lasers, with the increase in output wavelength, the repetition range increases and then decreases, while the pulse width has an opposite trend [22,26]. The potential reason is that the SAMs have different saturation intensity at different wavelengths. Figure 4: (**a**) Repetition rate (triangle) and pulse width (circle) as a function of pump power. (**b**) Peak power (triangle) and pulse energy (circle) as a function of pump power. Figure 5: (**a**) Laser pulse trains in 10 μs and 0.028 μs time durations at a pump power of 10.7 W. (**b**) The RF spectrum at a pump power of 10.7 W, measured with a RBW of 300 Hz.
# Ontology-Based Production Simulation with OntologySim
## 7 Technical Description
### Visualization  
The website (Frontend) visualizes the information available during and after the simulation. In the following, the three most elementary pages are presented in more detail: (1) simulation with back and forward data, hover functions for more information (Figure 14); (2) dashboard and charts with KPIs (Figure 15); and (3) event logging including filtering and sorting of events (Figure 16).  
Figure 13: OntologySim structure.  
The following elements are displayed in the graphic visualization of the simulation: Queue, Process Queue, Machine, and Transporter. In addition, the last event is displayed for the transporter and the machine. To be able to distinguish individual products, various product types are colored differently, and the progress is symbolized by the fill level of the circle and hovering over the product provides further information, such as queue time and the start of production. Furthermore, the visualization makes it possible to go back to steps in the production to better understand the agents' decisions.  
The KPI overview enables quick analysis of the production. For this purpose, the summarized value and the course of the KPIs are displayed during the simulation. Selected KPIs are visualized graphically, and the remaining KPIs are displayed in tabular form, as shown in Figure 15.  
Figure 14: Simulation with back and forward data, hover functions for more information.  
Figure 15: KPI Dashboard of a simulation run.  
Despite the previously described advantages and approaches to circumvent the disadvantages of an ontology-based production simulation, in general, detrimental issues are as follows: Simulation speed is extenuated due to numerous knowledge graph or ontology queries. While queries, for instance, with SPARQL, are more flexible than existing frameworks, they are yet typically more complex to apply than semi-graphical GUIs. The direct integration into existing MES or ERP systems is not (yet) regarded.
# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 3 Results
### Functional Categories Involved in E. coli Response to Multiple Stressors

Table 3: Downregulated gene functions in multiple conditions. The functional categories of upregulated genes common between at least three stressors are presented in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Function Type** & **Z** \\ \hline \multirow{6}{*}{**Metabolic processes**} & Transferses & 21 & _tusb, rlmB, yjgX, ydiU, elaA, yafK, tusE, ldtc, alaC, trmN, opgE, ltdcD, wecH, opgC, yajB, fic, lnt, mau, yafE, rlmE, rlnG yacC, yjic, ryld, yajL, sixA, casE, ygBr, glpG, rnd, yjhl, yahA, cdd, dbpA, phoA, fes ilvG, metA, gltA, asnA, ilvc, tdcG, argl, argR, rilvM, acnA, fbaB, pnuB, pnuA, eutQ, yhfx nrdG, nrdD, dadA, mnpB, dmsC, torZ, dusC, nirB, gorA, ndh, lcr wcaA, vwaZ, vucaF, vzzX, vuzzB, wcaD, vucaE yihG, pgC, dsc, yegS, fadD, yiiD \\  & Amino acids biosynthesis and metabolism & 16 & _ilvG, metA, gltA, asnA, ilvc, tdcG, argl, argR, rilvM, acnA, fbaB, pnuB, pnuA, eutQ, yhfx_ \\  & Oxidoreductase & 11 & _ntrdG, nrdD, dadA, mnpB, dmsC, torZ, dusC, nirB, gorA, ndh, lcr_ \\  & Lipopolysaccharide & 7 & _twaA, vwaZ, vucaF, vuzzC, vuzzB, wcaD, vucaE_ \\  & biosynthesis & 6 & _yihG, pgC, dsc, yegS, fadD, yiiD_ \\  & Axorbate and aldarate metabolism & 5 & _garD, guudD, lgoD, sgbE, lyxK_ \\  & Carbohydrate metabolism & 5 & _fsaB, anC, fucR, mlc, gltNS_ \\  & Starch and sucrose metabolism & 5 & _glgA, amyA, bglB, treF, pgm_ \\  & Secondary metabolites biosynthesis & 4 & _caA, entF, cysN, ubiX_ \\  & Carbon metabolism & 3 & _acs, gntK, mao_ \\  & Galactose metabolism & 2 & _dgQK, ebgC_ \\  & Other pathways & 6 & _ybdZ, thtiC, pyrF, atpC, torY, hofM,_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Upregulated gene functions in multiple conditions (at least three stressors). Figure 4: Pie chart displaying the distribution of functional categories among (**A**) upregulated genes, (**B**) downregulated gene shared between at least four stress conditions.