esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Objective OPP Model
### The Second Objective Function Considering ANOBC  
#### 3.2.1 Line \(N\)-1 Contingencies and D-PMU \(N\)-1 Contingencies  
\(N\)-1 contingencies include line \(N\)-1 contingencies and D-PMU \(N\)-1 contingencies. We can prove that, if the system is completely observable under D-PMU \(N\)-1 contingencies, the system is also completely observable under line \(N\)-1 contingencies. This principle is proven as follows.  
When a single line of the distribution network fails, set \(P_{1}\) is defined as the bus set connected to the bus \(i\) by only one line, and set \(P_{2}\) is the bus set connected to bus \(i\) by two or more lines. The constraints that make the bus \(i\) observable are as follows (if bus \(i\) meets just one, it is considered observed):  
1. Bus \(i\) has a D-PMU;
2. At least two buses in set \(P_{1}\) have a D-PMU;
3. At least one bus in set \(P_{2}\) has a D-PMU.  
The above constraints can be expressed as:  
\[2x_{i}+\sum_{j\in P_{1}}x_{j}+2\sum_{j\in P_{2}}x_{j}\geq 2\quad i\in H_{1}, \tag{3}\]  
where \(H_{1}\) is the set of buses to be observed.  
When any D-PMU of the distribution network fails, set \(P\) is defined as the bus set adjacent to bus \(i\). At this time, the constraints that make bus \(i\) observable are as follows, and bus \(i\) can be observed when it meets one:  
1. Bus \(i\) has a D-PMU, and set \(P\) has at least one D-PMU;
2. At least two buses in set \(P\) have a D-PMU.  
The above constraints can be expressed as:  
\[x_{i}+\sum_{j\in P}x_{j}\geq 2\quad i\in H_{1}. \tag{4}\]  
By comparing Equations (3) and (4), we see that, when each bus of the system can be observed under D-PMU \(N\)-1 contingencies, it must also be observable under line \(N\)-1 contingencies. This is because:  
\[\sum_{j\in P}x_{j}=\sum_{j\in P_{1}}x_{j}+\sum_{j\in P_{2}}x_{j}, \tag{5}\]  
\[2x_{i}+\sum_{j\in P_{1}}x_{j}+2\sum_{j\in P_{2}}x_{j}-x_{i}-\sum_{j\in P}x_{j} =x_{i}+\sum_{j\in P_{2}}x_{j}\geq 0\quad i\in H_{1}. \tag{6}\]  
Therefore, when considering observability under \(N\)-1 contingencies, D-PMU \(N\)-1 contingencies are the main consideration.
## 5 Implemented Example Use Cases and Configuration Details
### Collision Avoidance and Redundancy Measurements in Urban Scenario Using Various Sensors  
In this use case, we developed a scenario based on the car2 car grid configuration set provided by the Artery framework [4]. This scenario contained a grid of 5-5 perpendicular roads modeling an urban environment, as depicted in Figure 7. The scenario run for 400 s after a 100 s warm-up period. The penetration value was set to 20%, 50%, and 80% to keep the similarity with the previous experiments.  
Figure 5: Platooning app configuration.  
Figure 6: Highway traffic with platooning service use case.  
In the scope of this measurement, we focused on the effects of the various V2X services on the number of object detections on the receiver vehicle side. We applied three different sensor setups. The number of sensor-equipped vehicles depended on the V2X penetration parameter. Only a front sensor was applied to the equipped vehicles in the first case. This sensor had a range of 80 m and a field of view angle of 60\({}^{\circ}\). The vehicles were also equipped with CAM-sending capabilities and CAM sensors in the second sensor setup. The CAM sensors added the received CAMs to the object model. The third sensor setup included the CPM service and CPM sensors. The CPMs included the radar detections of the surrounding vehicles. In the scope of this scenario, we measured the level of redundancy and the overall number of detections. The number of vehicles in the simulation is presented in Figure 8.  
Figure 8: The number of vehicles in the simulation in the urban grid scenario.  
Figure 7: Urban grid scenario.
## 2 Materials and Methods
### Glycaemic Analysis (Blood Glucose Measurement and Calculation of Glycaemic Index)  
Ethical approval for this study was granted by the Medical Research Ethics Committee of Universiti Malaysia Sabah [JKEtika 2/21(16)]. The analysis was conducted at the Nutrition and Dietetic Laboratory, Faculty of Food Science and Nutrition, Universiti Malaysia Sabah. The protocol for glycaemic analysis was conducted according to [PERSON] et al. [31].  
Ten healthy volunteers participated in this study. Participants were given a subject information sheet with details of the study protocol and had the opportunity to ask questions. They signed an informed consent before participating in the study. Inclusion criteria of study participants are as follows: age 23 to 31 years old and non-smoker, normal body mass index (BMI) (19 and 25 kg/m\({}^{2}\)), and has normal fasting blood glucose level (<5.6 mmol/L). Individuals that were pregnant, allergic to food, hypersensitive, restricted or specified diet, inflammatory or metabolic diseases, and prescribed with medications that may interfere with carbohydrate metabolism, were excluded from participating in this study.  
Each participant completed five sessions of glycaemic index trials: three sessions for reference carbohydrates (50 g of glucose dissolved in 250 mL of water) and one session for control instant noodles and one session for instant noodles incorporated with 5% WEM as a test food. The sessions were carried out on separate days. The order of the test food, control and reference that were tested by each participant was randomised using online tool Research Randomizer (Version 4.0) [32]. The analysis was conducted in the morning after the participant fasted overnight for a minimum of 10 h. Blood glucose levels were measured in capillary blood obtained by finger prick using a lancet (ACCU-CHEK(r) Safe T-Pro Plus, Roche, Basel, Switzerland), blood glucose test strip (Freestyle Freedom Lite(r), Abbott Laboratories, Chicago, IL, USA) and glucometer (Freestyle Freedom Lite(r), Abbott Laboratories, Chicago, IL, USA). Blood sugar levels were measured at seven time points (0, 15, 30, 45, 60, 90, and 120 min). The glycaemic response or the incremental area under the curve (IAUC) for each reference and test food was calculated by the trapezoid rule [33].  
The glycaemic index (GI) was calculated by using Equation (1) [31].  
\[\text{GI}=(\text{IAUC of the test food}/\text{the IAUC of reference food })\times 100 \tag{1}\]
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 3 LoRa/LoRaWAN Standard
### LoRa Radio Interface
The choice of values depends on specific application requirements, such as the expected range, data volume, reliability, and energy consumption. In long-range, low-throughput networks like LoRaWAN, it is essential to find the optimal balance between these parameters to maximize the technology's potential while minimizing energy usage and ensuring adequate transmission quality. An example case study of LoRaWAN use in transportation could be a medium-sized city that decided to implement a smart fleet management system for city buses. The goal was to improve operational efficiency, monitor fuel consumption, and optimize routes in real time. Traditional systems based on GSM technology proved too costly, and the GPS transmitters in the vehicles consumed significant amounts of energy. By using spreading factors (SF9 and SF10), a suitable compromise was achieved between throughput and transmission range. In the urban environment, where transmissions occurred over relatively short distances, a medium SF provided stable connections and allowed near-real-time information transmission. A bandwidth of 125 kHz was sufficient for transmitting small data packets, such as GPS updates and fuel consumption information, while effectively managing interference.
###### Abstract  
The increase in food demand in Indonesia is one of the consequences of the imbalance between population growth and declining food products. One of alternative technologies that can be used in plant breeding programs to increase agricultural production, in order to meet food demands, is genetically modified organism (GMO) technology. This technology presents a lot of pros and cons among the public-related impacts that will be accepted by consumers. The purpose of this study was to determine the level of sustainability between GMO and non-GMO foods. The performance measurement model for GMO and non-GMO foods was considered according to the seven issues of sustainability that represented environmental, social, and economic aspects. The assessment method was conducted by using Adjusted Profit (AP) with Total Price Recovery (TPR) indicators and Total Factor Productivity (TFP) by utilizing the Data Envelopment Analysis (DEA) Method. Assessments made on each supply chain component included agriculture, processing, and transport to wholesalers/retailers. This study used numerical examples of rice production in Indonesia. The research results found that the performance of non-GMO rice chain is better than GMO rice. It indicates that non-GMO rice is more sustainable. The results show that the proposed model can be applied to measure the sustainability of GMO and Non-GMO agri-food supply chain performance.  
a _Article_  
Sustainable Agri-Food Supply Chain Performance Measurement Model for GMO and Non-GMO Using Data Envelopment Analysis Method
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
## 3 Security Analysis  
### The Optimal Value  
We need to find the optimal value for \(V_{a}\), because this value will directly determine whether the performance of the eight-state CVQKD protocol shows the best performance. First, we use the content in Section 2 to build a data model of the original eight-state protocol. We keep the other parameters unchanged in the data model, but only change the transmission distance parameter \(d\) (excess noise \(\epsilon\) is fixed to legitimate value), and establish a relationship diagram between \(V_{a}\) and the secret key rate. As shown in Figure 2, we find that the secret key rate achieves the highest value when \(V_{a}=0.25\).  
We need to further verify the \(V_{a}\) value. The transmission distance \(d\) is fixed to a legitimate value and the other parameters are kept unchanged. Here, we choose different values of the excess noise \(\epsilon\). From Figure 3, we observe that the optimal \(V_{a}\) value still exists in a public interval including 0.25.  
Therefore, we take the value of \(V_{a}\) to be 0.25 in the following analysis.
# Vortex Breakdown Control by the Plasma Swirl Injector
## 1 Introduction
Several theories were proposed to explain the process of vortex breakdown, such as critical state or wave phenomena theory [37], boundary layer separation or flow stagnation [38], and hydrodynamic instability [39]. [PERSON] [40] recommended the swirl-decay mechanism (SDM) as a simple physical reason for the vortex breakdown occurrence, and also pointed out that SDM indicated efficient means for vortex breakdown control. The flow and boundary conditions have a strong effect on the breakdown process. [PERSON] [34] observed a periodic transition between bubble-type and spiral-type depending on the flow and boundary conditions. [PERSON] et al. [41] observed the transition from bubble-type and spiral-type, both in experiments and numerical simulations, and proposed a feedback model for the initiation and development of the bubble-type breakdown. It is of practical important to study methods of controlling VB, so that it can be enhanced when it is beneficial and weakened when it is detrimental. The main techniques employed to control VB include temperature gradient ([PERSON] and [PERSON] [42]), shape modification ([PERSON] and [PERSON] [43]), blowing ([PERSON] and [PERSON] [44], [PERSON] and [PERSON] [45]), rotation of the end walls ([PERSON] et al. [46]), the addition of near-axis swirl ([PERSON] et al. [47]), and energy deposition ([PERSON] et al. [48]). [PERSON] and [PERSON] reviewed the early research on VB control in detail [49], while the developments in this area are continuously evolving. Although vortex breakdown should be suppressed on a delta wing, it is commonly adopted in combustion and acts as a stabilizer to enhance reactant mixing and stabilization of the flame by recirculating hot gases into its base. Inspired by the idea of accelerating the same fluid particles continuously, we designed a plasma swirler with the electrode placed in the streamwise direction [50]. The plasma swirl injector is effective in diffusion and premixed flame control [51; 52], and can be integrated into a low swirl injector to control the flame lift off height accurately [53; 54]. The electrode of the plasma swirler was optimized as a helical shape to enhance its performance [55]. In this study, the concept of using the plasma swirler to control vortex breakdown was proposed and tested. A plasma swirler with a helical shape was adopted to control the vortex breakdown. The plasma actuation, in affecting the onset and development of the vortex breakdown, was captured and analyzed.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 5 Results
### Performance Improvement via Dual Kalman Filter Structure  
Table 4 shows the pass rates of the ratio tests and the 3D RMSE of the relative positions between the UAVs with conventional and dual KFs. In this test, measurement time synchronization and the proposed cycle slip detection approach were implemented in both KFs. Overall, the dual KF performed better than the conventional KF in both aspects. Compared to the conventional KF, average improvements in the ambiguity fix ratio and 3D RMSE of position errors are 23.54 and 65.31 percent, respectively. A total of 43 out of 45 tests showed an improved performance in terms of 3D RMSE reduction along with ratio test pass rate. The remaining two cases had 100 % ratio test pass rates with the conventional KF; thus, there were no advantages to using the dual KF. Although an increase in the ratio test pass rate seemed to result in a reduction in 3D RMSE, the dataset that showed the largest improvement in fix rate differed from the dataset with the largest RMSE reduction. This is because the reduction in 3D RMSE was highly dependent on the overall accuracy of the conventional KF's float solution, in addition to the ratio test pass rate. The conventional KF produced inaccurate float solutions when it frequently failed the ratio test in the early stages of the KF. Therefore, improving the ratio test pass rate in the early stages of the KF contributed to result in a greater reduction in 3D RMSE for the dual KF structure. With the proposed dual KF structure, all 45 datasets had 3D position RMSE less than 10 cm. Nonetheless, some datasets did not achieve a 100% pass rate on the ratio test, and some fixed solutions had instantaneous 3D position errors over 10 cm. The results suggest that if the ratio test failures were due to problems other than the inclusion of new satellites into the KF, the proposed dual KF structure is subject to the same problems. To address these concerns, implementing partial ambiguity resolution or residual tests needs to be explored in conjunction with the dual KF structure, which will be a topic for our future research.  
Figure 15: (**a**) The TDDFC metrics from both static GNSS receivers and flight test data. (**b**) Number of carrier phase measurements passing the thresholds of the prior art and proposed methods, respectively. The threshold in [36] excludes numerous GNSS data from flight test data (UAVs) compared to the proposed modeled cycle slip detection threshold.
## 3 Simulation Results
### Complexity Analysis  
The suggested combined model's computational complexity is represented by the following formula: \(\mathcal{O}(B_{s}\times E_{s}(r_{c}\times p_{c}\times t_{c})+t_{s}t_{h}(3t_{i}+3t _{h}+3)\), where \(B_{s}\) represents the number of input packets obtained, \(E_{s}\) represents the size of an OFDM block, and \(r_{c}\) represents the size of the CNN input; the number of filter size and neuron size of CNN are \(p_{c}\) and \(t_{c}\), respectively; the number of features in the input vector is \(t_{i}\); \(t_{s}\) is the input time sequence size; and \(t_{h}\) is the number of hidden units. On the other hand, the computational complexity of Demod-CNN [40] can be defined as \(\mathcal{O}(B_{s}\times E_{s}(r_{c}\times p_{c}\times t_{c})\times 2)\). Additionally, the computational complexity of the traditional OFDM system can be expressed as \(\mathcal{O}(M_{s})\), where the modulation order is \(M_{s}\)[53]. Because only IFFT and FFT are employed, the traditional OFDM is computationally efficient. We consider the parameter values to be \(B_{s}=100\), \(E_{s}=128\), \(r_{c}=8\times 1\), \(p_{c}=3\times 64\), \(t_{c}=100\), \(t_{s}=64\), \(t_{i}=300\times 64\), and \(t_{h}=300\times 100\) for example evaluation. After the consideration of these parameters using the Demod-CNN expression, we require \(3.9\times 10^{9}\) operations. In the case of the proposed model, we need \(2.8\times 10^{11}\) operations for the given parameters. The research above demonstrates that the time complexity of the suggested model is greater than that of the conventional technique and the Demod-CNN model. Although the proposed model requires more complexity than others, it outperforms them all in terms of performance, and GPU parallel computing can speed up the process.
# Smartphones as a Light Measurement Tool: Case of Study
## 6 Conclusions  
As a conclusion, this study shows how the smartphones can be used in lighting measurement tasks when high precision of data is not required, being a great tool to have a reference of the luminance levels, but they are not as accurate as professional hardware designed for the task.  
The results do show how the accuracy of each of the three methods used to perform the evaluation of the luminance is high after calibration. However, if the results are compared between the three methods, it can be seen how the precision of the internal sensors, as ambient light sensor or the digital camera, regarding to an external sensor is lower.  
This can be caused by the perturbation of ambient light and the fact that the mobile ambient light sensor, due to its physical characteristics, is only able to read direct light, while the luxmeter used on the experiment is able to also measure indirect light.  
However, a light meter can never be 100% accurate. There will always be slight variances between light meters, even if they are identical models. It is important to take into consideration that luxmeters can also drift over time, making it necessary to calibrate them to eventually ensure readings as accurately as possible. This characteristic that makes it necessary to calibrate each device separately, even in identical models, hinders the use of smartphones as an accurate measurement tool due to the need to perform the calibration on each device separately. On the other hand, the use of an external device not only improves measurements accuracy, but it can also be used by more than one mobile device.  
During the experiments, it was also showed how the use of more than one calibration's parameter increases the accuracy of each sensor. However, the lighting section used to perform this calibration can be different in each device, a reason why, before performing this evaluation, it is necessary to determine values that fit better for each case.  
Nevertheless, the work performed does not stop here. For future directions of study, the use of artificial intelligence is proposed, specifically a multi-agent, to evaluate the values obtained for the external sensor to help in decision-making about lighting.  
Likewise, the use of both ambient light sensors and digital cameras as tools for home automation control in offices, centers and homes, where the measurements do not need to be as accurate, is also proposed.  
Finally, creating a collaborative system that allows the combination of measures from more than one mobile phone, with the aim of self-discovering the calibration needed for every device, is proposed.  
**Author Contributions:** [PERSON] and [PERSON] have developed the mobile application used along the experiments and performed the analysis of the results obtained on the different experiments; [PERSON] and [PERSON] have been in charge of the measurements performed, have perform the measurements and [PERSON] have coordinate the work performed and validated the results.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Setup
### Hardware Implementation
#### 3.1.1 Reused Energy Storage System (R-ESS)  
The R-ESS is an energy storage system built using repurposed second-life batteries from EVs [38; 39; 40], particularly electric buses that still retain usable capacity. Compared to traditional ESSs, R-ESSs improves resource efficiency and cost-effectiveness by utilizing EV batteries that would otherwise be discarded. This approach extends the life cycle of the batteries and reduces waste. The R-ESS stores excess energy generated by the PV panels and discharges power during peak demand periods to stabilize the grid. With a total storage capacity of 150 kW and a maximum output of 100 kW, the R-ESS is critical for peak shaving and demand response. By utilizing high-capacity batteries from electric buses, the R-ESS provides enhanced reliability and scalability for grid stabilization.
## Preliminaries
### System Components  
Eight algorithms are involved in CL-PGSDP system.  
1. Setup is a probabilistic algorithm run by the group manager. It takes a security parameter \(\lambda\) as input and outputs the system parameters _params_ and the master key _msk_.
2. Partial-Private-Key-Gen is a probabilistic algorithm run by the group manager. It takes the master key _msk_, a random value \(\delta\), and the identity \(ID_{i}\in\{0,1\}^{*}\) of the user \(u_{i}\) as inputs, and outputs the \(u_{i}\)'s partial private key \(psk_{ID_{i}}\).
3. Secret-Value-Gen is a probabilistic algorithm run by the group user who randomly selects \(y_{ID_{i}}\) as the secret value. Thus, the private key of the group user contains two parts: secret value \(y_{ID_{i}}\) and partial private key \(psk_{ID_{i}}\).
4. Public-Key-Gen is a probabilistic algorithm performed by the group user \(u_{i}\) to compute the public key. It inputs the \(u_{i}\)'s secret value \(y_{ID_{i}}\) and outputs the \(u_{i}\)'s public key \(pk_{ID_{i}}\).
5. Tag-Gen is a probabilistic algorithm executed by the group user \(u_{i}\) to generate authentication tags for data blocks. It takes the \(u_{i}\)'s partial private key \(psk_{ID_{i}}\), the secret value \(y_{ID_{i}}\), and the data block \(m_{j}\) as inputs, and outputs the tag \(\sigma_{j}\) of \(m_{j}\).
6. Challenge is a randomized algorithm run by the TPA. It takes the system parameters _params_, a unique file name, and the count \(c\) of the challenged data blocks as inputs, and outputs the challenge information _chal_.
7. Proof-Gen is a probabilistic algorithm run by cloud server to obtain a data possession proof P of the challenged blocks. The inputs include _chal_, the challenged data blocks and tags of the challenged data blocks.
8. Proof-Check is a deterministic algorithm run by the TPA. It inputs the proof P, the challenge information _chal_, and the user's public key. If P is correct, this algorithm outputs 1, otherwise it outputs 0.  
Figure 1: System model of our protocol.