esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases  
The energy management in the electricity distribution networks generally consists of production planning and control, energy procurement, electricity distribution management, energy demand management, building energy management, maintenance, and communications. DSM measures are used to reduce energy end-use or promote energy efficiency [71], for example, by adjusting consumption temporarily (peak shaving or load shedding). DSM also includes modification of consumer demand for power balancing purposes by transferring consumption from the high load and price hours to more inexpensive hours (load shifting). The DSM can be implemented via various marketplaces to the power system's energy and power balance management. The electricity utilities (in Finland, electric energy sellers, and aggregators) can offer DR capacity to the frequency-controlled reserve, balancing power, fast disturbance reserve, and strategic reserve markets in addition to the day-ahead and intraday energy markets. The implementation of DR is a crucial product considering the flexibility of the future MV and LV distribution networks. The value of DR depends on the accuracy of the estimation, the prediction, and the optimization of demand.  
In this research, the HL-UCs of energy management are electricity supply, electricity consumption, load control, electricity generation, generation control, and Volt/VAr control. Energy management covers the power balance management in the active distribution networks [43]. However, the power balance management UCs are presented separately from the energy management UCs for the microgrid phase in Section 4.2.
## 3 Proposed System Model
### Lagrange Duality Resource Optimization Algorithm (LDROA)
The primal problem involves minimizing the objective function subject to constraints, while the dual problem involves maximizing the Lagrangian function, which is a modified version of the objective function that incorporates the Lagrange multipliers. By solving these two problems iteratively, we can find the optimal resource allocation that minimizes the execution time of the task subject to the constraints. The Lagrange Duality algorithm allows for dynamic adjustment of the resource allocation in response to changes in the system and ensures that the task is executed efficiently and effectively while meeting the QoS requirements. The derived Functions (21)-(24) are just the process of mathematical formulas to calculate the minimum resource for task \(i\) assigned by the edge server. We derivate Equation (21) first with respect to \(\lambda\) so that we can obtain the maximum value of \(\lambda\). The value of \(\lambda\), which is the Lagrange multiplier, is ranked between zero and one for the purpose of optimization. So, based on Equation (22), if we can find the maximum value of \(\lambda\), then we can find the minimum value of required resource (\(V_{i}^{r}\)) for task \(i\), which is revealed in the mathematical formula in Equation (24). \[\frac{\partial yL(v,\lambda)}{\partial V_{i}^{r}}=\lambda-\frac{C_{i}}{\left(V_{ i}^{r}\right)^{2}} \tag{21}\]  
\[{(V_{r}^{r})}^{2}\lambda=C_{i}\to V_{i}^{r}=\sqrt{\frac{C_{i}}{\lambda}} \tag{22}\]  
\[\text{min}L(x,\lambda)=\sum_{i\in I_{off}}\frac{C_{i}}{V_{i}^{r}}+\lambda \left(\sum_{i\in I_{off}}V_{i}^{r}-V_{m}^{r}\right) \tag{23}\]  
\[V_{i}^{r}=\sqrt{\frac{C_{i}}{\lambda}}\to V_{i}^{r}=\frac{V_{i}^{r}}{\sum i \in I_{off}} \tag{24}\]
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 4: Four Antennas at BS/40 and 62 Users/MUSA Code Length of 31  
Figure 15 illustrates the performance of the system for an increased code length of 31. As expected, by increasing the length of the codes from 4 to 31, the system's performance also increases. Instead of using clusters, in this scenario, all users had allocated spreading sequences as follows: 31 users received classical pseudo-noise (PN) codes with a length of 31 [53], transformed into complex ones, and the other users had randomly allocated MUSA codes. This approach lets us highlight that accounting for the correlation between codes brings a considerable benefit to the performance of the system. When the system is 200% overloaded (62 users using 31 codes--the length of the code), due to only 31 out of 62 users using low-correlated PN codes, there is a slight difference in the BER compared to the case in which all users received random MUSA codes.  
Figure 14: BER vs. SNR for Scenario 3: (**a**) 700 bits transmitted; (**b**) 7000 bits transmitted; (**c**) Rice fading; (**d**) QPSK modulation.
## 2 Experimental Investigation
### Instrumentation  
The disk was instrumented with a PZT (piezoelectric patch) (PI-876A12) glued to the disk with an epoxy component, LOCTITE 454 (Loctite, Hartford, CT, USA). This PZT was used to excite the disk at the desired frequency bands. The PZT works in a range of \(-\)100 V to 250 V. For the signal generation, a NI-9263 (National Instruments, Austin, TX, USA) module was used. This module could generate four independent analog outputs with an amplitude of \(-\)10 V to 10 V. With an OEM-835 (OEM Systemas Company, Reno, NV, USA) amplifier the analog signal was amplified by 25, so the sent signal to the patch had the desired level. Additionally, a signal to monitor the excitation was sent to the data acquisition system.  
To measure the excitation and response of the rotating structures, the disk was instrumented with a miniature and submergible accelerometer (ADISK in Figure 2) (Dytran 3006-A, sensitivity: 100 mV/g) and the shaft with another accelerometer (ASH in Figure 2) (MMF Type KS91B, sensitivity: 10 mV/g) and an AE sensor (ESH in Figure 2) (Bruel and Kjaer Type 8313) (Bruel & Kjaer, Naerum, Denmark).  
Moreover, different sensors were located in the stationary parts of the test rig: one accelerometer in the upper cover (ATT in Figure 2) (Kistler 8752A50, sensitivity: 100 mV/g) (Kistler, Winterthur, Switzerland), one in the lower cover (AINF in Figure 2) (MMF Type KS91B, sensitivity: 10 mV/g) (Metra Mess-und Frequenztechnik, Radebeul, Germany), one in the side cover (ALAT in Figure 2) (Dytran 3006-A, sensitivity: 100 mV/g) (Dytran Instruments Inc, Chatsworth, CA, USA) and another one in the bearing (ABEAR in Figure 2) (Dytran 3006-A, sensitivity: 100 mV/g). An AE sensor (Bruel and Kjaer Type 8313) (Bruel & Kjaer, Naerum, Denmark) was also installed in the upper cover (ETT in Figure 2), as well as a microphone (Bruel and Kjaer Type 4957) near the test rig.  
Figure 2: Sketch of the experimental test rig and the sensors installed. ADISK, accelerometer in the disk; AINF, accelerometer in the inferior cover; ABEAR, accelerometer in the bearing; ASH, accelerometer in the shaft; ESH, acoustic emission sensor in the shaft; ETT, acoustic emission sensor in the superior cover; ATT, accelerometer in the superior cover; ALAT, accelerometer in the lateral cover.  
The signals were acquired using an acquisition system (Bruel and Kjaer Type 3038) module. The acquisition frequency was set to 25.6 kHz.
# An Overview of Quantum Machine Learning Research in China
## 2 QML Development History and Current Status in China
### Current Status
#### 2.2.1 Development Trend  
Nowadays, the global QML field primarily focuses on classification tasks, with image classification being the most prevalent among them. Regarding the research modes, most studies follow the quantum replacement mode, aiming to design quantum implementations of classical machine learning algorithms. Meanwhile, there are also quite a few studies based on the quantum improvement mode, which strive to upgrade the circuit implementations of quantum algorithms for the improvement of the QML model performance.  
As for China's QML field, as mentioned before, the number of studies focusing on supervised learning tasks is obviously rising nowadays. Supervised learning tasks mainly contain classification and regression, with classification tasks being more popular currently. Therefore, China's QML research is aligned with the global trend in terms of the target task. With regard to the research modes, the number of studies based on quantum improvement is gradually surpassing that of the quantum-replacement-based studies. So, it might be concluded that China's QML investigation holds a leading position internationally in terms of the research mode.
## 4 Experiments and Results  
This study addresses power consumption challenges in IoT devices, utilizing a low-cost weather station as the experimental model. Two primary issues were identified: power consumption during polling and the implementation of deep sleep techniques, as well as delays encountered in the waiting state of the next cycle with varying delay durations. Power consumption was systematically measured in both the microcontroller and during network transmission. This study highlights critical strategies for reducing power consumption in IoT devices.  
In Figure 5, we demonstrate the power consumption of getData()and sendData() functions. The test cases have four delay points: turnOn(), the getData() function, connecting the network, and waiting for the next cycle. The polling and deep sleep techniques are applied in the poll_delay() and deep_delay() functions. An execution cycle consists of two periods: active (\(tACT\)) and sleep (\(tSLP\)) period. The active period is the functional state. The devices sense and send data to the data store (cloud) with the getData() and sendData() functions, whereas the sleep period waits for the next cycle with the delay technique. There is a short delay in the getData() and sendData() functions.  
For the network active period, the send() function consists of two sub-functions: network connection and data transmission sub-functions. This experiment turns the WiFi module off to reduce power consumption during the network deactivation mode. Furthermore, there is a delay function connecting the network and trial time. After the device is connected, it transmits and sends data, and suddenly, the network function turns off.
###### Abstract  
Aimed at addressing the difficult problems existing in extremely low speed calibration facilities for hot-wire anemometers, where calibration accuracy is often insufficient and vulnerable to the contamination from temperature and humidity discrepancies between the calibration environment and the application environment, a calibration rig with a velocity range from 0.10 m/s to 1.0 m/s, an air temperature range of ambient temperature to 60 \({}^{\circ}\)C, and a humidity range from 20%RH to 80%RH was designed, developed, and constructed. The overall layout arrangement and the mechanical structure of the facility are illustrated. The master control system, the motion control system, the temperature and humidity control system are designed, tested and adjusted. The adjustment results are demonstrated and discussed. The analysis of the results reveals that the maximum velocity control error is 0.000989 m/s, satisfying the design target of 0.003 m/s; the corresponding maximum relative error is 0.241%, which is less than the design target of 0.4%; the maximum temperature control error is 0.9 \({}^{\circ}\)C, meeting the design target accuracy of 1 \({}^{\circ}\)C; the maximum humidity control error is 2.9%RH, which is below the design target of 4%RH. When the facility is applied to the calibration of a hot-wire anemometer, the maximum error of the fitting curves in modified King's law is 0.02236 m/s, while that in [PERSON]'s formula is 0.023217 m/s, both of which satisfy the design target accuracy of 0.03 m/s. The maximum relative errors of fitting curves using the two formulas are 5.214% and 8.527%, respectively. Analysis of calibration data reveals that the discrepancy in temperature and humidity between application site and calibration site may bring errors that can reach up to 1.2676% per unit relative humidity discrepancy, and 5.672% per degree Celsius of temperature deviation, respectively.  
extremely low speed; calibration facility; velocity control; temperature control; humidity control; calibration of hot-wire anemometers +
Footnote †: journal: Applied Sciences
# V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
## 2 Daily Load Curve Evaluation Method
### Charging and Discharging Modeling of EVs by Location  
For an analysis of grid impact based on the charge and discharge locations of EVs, the amount of traffic flowing in and out of downtown Seoul was analyzed based on data from the Seoul Metropolitan Police Agency's Comprehensive Traffic Information Center (Seoul Metropolitan Police Agency) [22]. To analyze the grid effect according to the charge and discharge location, the traffic volume in and out of downtown Seoul was analyzed on an annual average basis in 2020 based on data from the Traffic Information Center (Seoul Metropolitan Police Agency) of the Seoul Metropolitan Police Agency. Based on such traffic data, the difference between vehicles entering the city and those flowing out of the city centering on the Han River in Seoul was analyzed. More specifically, when analyzing the inflow/outflow of the city center, it was assumed that if there was a large amount of inflow, it would be discharged at work, and if there was a large amount of outflow, it would be charged at home. In Figure 1, a normalized value is shown.  
Figure 1: Inflow and outflow normalized traffic data for downtown Seoul.  
As can be seen in Figure 1, traffic entering the city on the way to work peaks between 7:00 a.m. and 9:00 a.m., but traffic flowing out of the city after work is evenly distributed from 5:00 p.m. to midnight.  
In this paper, the following assumptions are made about the inflow and outflow of vehicles in the city in order to evaluate the impact of the grid on EV charging and discharging.  
1. Coming to work (incoming vehicle-outgoing vehicle) \(\rightarrow\) EV discharge (6:00-15:00).
2. Leaving work (outbound vehicle-inbound vehicle) \(\rightarrow\) EV charging (16:00-05:00).  
Based on these assumptions, the traffic data model was used as an uncontrolled mode due to the difference between EV inflow and outflow. In addition, for the controlled mode, a model that applies both the non-controlled-mode traffic data model and the EV charging/discharging rate model was used.  
Here, the SMP and TOU rate data presented in KEPCO [23] were used as the charge/discharge rate model applied to the control mode.
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 2 Structural Calculation Model for Anti-Sliding Short Piles
### Finite Difference Solution for the Anti-Sliding Short Pile Model
This unified system, referred to as Equation (14), can be employed to determine the internal forces throughout the entire pile of the anti-sliding short pile. \[\left[ \begin{array}{c}x_{-2}\\ x_{-1}\\ x_{0}\\ x_{1}\\ \cdots\\ x_{m}\\ x_{n-1}\\ x_{n}\\ x_{n+1}\\ x_{n+2}\end{array} \right]=\begin{bmatrix}0&1&-2&1&0&\cdots&\cdots&\cdots&0&0&0&0&0 &0\\ 1&-2&0&2&1&\cdots&\cdots&\cdots&0&0&0&0&0\\ 1&-4&a_{0}&-4&1&\cdots&\cdots&0&0&0&0&0\\ 0&1&-4&a_{1}&-4&1&\cdots&\cdots&0&0&0&0&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\ddots&&&&\vdots&\vdots&\vdots& \vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&\vdots&&&&\ddots&\vdotsat different nodes along the pile, enabling the determination of shear forces and bending moments along the pile. \[M_{i}=\frac{EI}{h^{2}}(x_{i+1}-2x_{i}+x_{i-1}) \tag{15}\]  
\[Q_{i}=-\frac{EI}{2h^{2}}(x_{i+2}-2x_{i+1}+2x_{i-1}-x_{i-2}) \tag{16}\]
## 3 Results and Discussion  
### The Fluid Dynamics of the Flue Gas  
To fully understand the motion state of the flue gas and further optimize the physical structure of the HE, a fluid simulation was conducted by regarding the flue gas as the thermal fluid, while the ambient air flowing into the HE was regarded as the cold fluid.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Unit** & **Uncertainties** \\ \hline Measured parameters & & \\ Ambient air temperature \(T_{0}\) & \({}^{\circ}\)C & \(\pm 0.5\) \\ Ambient air humidity \(RH_{0}\) & \% & \(\pm 0.3\) \\ Inlet flue gas temperature \(T_{fg,in}\) & \({}^{\circ}\)C & \(\pm 0.5\) \\ Outlet flue gas temperature \(T_{fg,out}\) & \({}^{\circ}\)C & \(\pm 0.3\) \\ Tube temperature \(T_{tube}\) & \({}^{\circ}\)C & \(\pm 0.5\) \\ Flue gas velocity \(v_{fg}\) & m\(\cdot\)s\({}^{-1}\) & \(\pm 0.3\) \\ Hot air velocity \(v_{air}\) & m\(\cdot\)s\({}^{-1}\) & \(\pm 0.2\) \\ Calculated parameters & & Relative uncertainty \\ \hline Average heat transfer coefficient \(h_{s}\) & kW\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\) & \(\pm 6.03\) (\%) \\ Pressure drop \(\Delta p_{t}\) & Pa & \(\pm 3.25\) (\%) \\ Nusselt number of thermal fluid \(Nu_{in}\) & - & \(\pm 3.18\) (\%) \\ Nusselt number of cold fluid \(Nu_{out}\) & - & \(\pm 2.83\) (\%) \\ Improvement potential \(IP\) & - & \(\pm 7.21\) (\%) \\ Sustainability index \(SI\) & - & \(\pm 8.82\) (\%) \\ Exergy efficiency \(\eta_{HE}\) & \% & \(\pm 5.85\) (\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Uncertainties of the measured parameters and calculated parameters.  
The fluid model was set as the \(k\)-\(e\) model and the calculation steps were set to be 350. The velocity and the pressure distribution of the thermal fluid under different velocities (3, 5 and 7m\(\cdot\)s\({}^{-1}\)) of flue gas were investigated, and the detailed results are analyzed in Section 3.1.1 and 3.1.2.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Indoor Measurements  
After having established a baseline in the outdoor environment, the measurement setup was moved indoors. The indoor environments are described in detail in the following subsections and a comparison can be found in Figure 4, which shows data from two satellite passes per environment. Each environment contains one satellite pass in the elevation angle range of 20\({}^{\circ}\) and 40\({}^{\circ}\).
## 2 Overview of OM Structure and Functions
### Overview of LPS Biosynthesis
The increased level of LpxC may, thus, effectively deplete the R-3-hydroxymyristoyl ACP pool, thus, leading to an imbalanced phospholipid/LPS ratio in the OM [47]. FtsH also controls the turnover of WaaA, the enzyme catalyzing incorporation of Kdo residues in lipid IV\({}_{\rm A}\)[49]. Therefore FtsH dependent proteolysis seems to be a crucial point of control to balance the levels of membrane lipid synthesis according to the physiological state/growth conditions of the cell. Indeed it has been recently reported that LpxC is degraded rapidly during slow growth, presumably to avoid toxic overproduction of LPS, and that the alarmone ppGpp is needed together with FtsH to control LpxC stability [50]. Very recently an additional player of this regulatory mechanism, YciM a conserved tetratricopeptide repeat protein, has been identified. YciM is essential and its depletion results in increased LpxC levels leading to higher amount of LPS and, ultimately, to cell death suggesting that FtsH proteolytic activity is also modulated by YciM [51]. Control of LPS biosynthesis by FtsH mediated proteolysis, however, is not a widespread mechanism across Gram-negative bacteria but seems restricted to _Enterobacteria_; indeed the _C_-terminus of LpxC (where sequence specific degradation signals are located) differs significantly among species whereas the overall sequence of LpxC is highly conserved [52].
## 3 Results
### Drying Rate  
The drying rates of the different dryers were calculated and their variation by time was plotted in Figure 2. At the beginning of the drying process, the product moisture content was high; thus, the moisture loss occurred at a higher pace. The evaporation rate of the product was gradually decreased by the decrease in the moisture content. The obtained results indicated that at the initial seconds, the evaporation rates showed a rapid enhancement for all treatments and reached a peak at which the evaporation rate was maximal. Then, by the passing of time and the decrease in the product moisture content, the evaporation rate decreased as well. According to Figure 2, the highest evaporation rate was observed in US+MW-HD (0.61 kg-water/(kg-dried-matter-min) while the HD method exhibited the lowest drying rate (0.11 kg-water/(kg-dried-matter-min) (\(p<0.05\)). This is due to the increase in the internal mass transport resistance as the result of passing of time ([PERSON] et al., 2020). The results obtained by studying the raspberries drying with various dryers showing that the highest and the lowest evaporation rates were detected in MW-HD and HD, respectively ([PERSON] et al., 2020). Moreover, the application of US pretreatment increased the evaporation rate in comparison with the conditions in which no pretreatment was considered. It seems that US can cause pores in the carrot slices (cavitation phenomenon) and form fine channels on the cell walls. By reducing the internal resistance against the moisture diffusion and the number of boundary layers, US pretreatment can provide a proper condition for accelerated moisture removal during the drying process ([PERSON] et al., 2020).  
In the study performed by [PERSON] et al. (2020) on raspberries drying with various dryers (MWD and HD with US pretreatment), the lowest evaporation rate was detected in the hot air dryer. They also indicated that the application of US pretreatment can elevate the evaporation rate. Moreover, they reported that the highest evaporation rate was obtained by the hybrid MW-HD with US pretreatment. The impact of various dryers (HD, INFD and US pretreatment) on the evaporation rate of potato sweet was addressed by [PERSON] et al. (2019). They declared that the highest evaporation rate can be obtained by US pretreatment.  
Figure 1: Drying kinetic of the carrot samples pretreated with ultrasound in different drying methods.
## 2 Materials and Methods
### Statistical Analyses  
Descriptive data are presented as mean \(\pm\) SDs. The Shapiro-Wilk test confirmed the normal distribution of all variables (\(p>0.05\)), except for CR-10. A one-way repeated-measures analysis of variance (ANOVA) and Friedman test were used to compare peak HR and CR-10 between protocols, respectively. A mixed model ANOVA was conducted on each VBT performance indicator (numbers of repetitions, fastest velocity, and average velocity) with the protocol and set as within-subject factor and sex as between-subject factor. A mixed model ANOVA was applied to the MAS, with the protocol as a within-subject factor and sex as a between-subject factor. The Greenhouse-Geisser correction was used when [PERSON]'s sphericity test was violated and pairwise comparisons were identified using Bonferroni post hoc corrections. The magnitude of the differences was quantified through the standardized mean differences ([PERSON]'s \(d\) effect size [ES]). The following scale was used to interpret the magnitude of the ES: _trivial_ (\(<\)0.20), _small_ (0.20-0.59), _moderate_ (0.60-1.19), _large_ (1.20-2.00), and _extremely large_ (\(>\)2.00) [24]. All statistical analyses were performed using the software package SPSS (IBM SPSS version 25.0, Chicago, IL, USA) and statistical significance was set at an alpha level of 0.05. _Post hoc_ statistical power was conducted using G*Power (Version 3.1) with an ES of 0.30 and \(\alpha\) of 0.05, and this revealed a 0.93 statistic power.