esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods for Analysis  
To analyze the behavior and performance of users, the relevance of the commands used by each specialist was studied. First, a preliminary analysis was conducted to detect certain behavioral patterns for each designer based on the overall quantity of executions of each sub-category of commands and the effect that these had on the development of the project. Additionally, the compliance of each designer's contributions with the Pareto principle (also known as the 80/20 rule) was studied. The Pareto principle states that in any group of elements that contribute to the same effect, a few (-20%) are usually responsible for most of that effect (-80%). Therefore, non-contributory commands were excluded from this analysis. Pareto charts consisting of a bar graph and a curve were created for each designer. In these charts, the commands are sorted in descending order by their total number of executions and the curve represents the relative frequency of these commands. The Pareto principle, supported by the chart, is a simple visual tool for focusing and analyzing relationships, studying results, and planning for continuous improvement, and has been proven to be valid in numerous situations [31]. Thus, in accordance with the Pareto principle, the number of contributory commands that make up 80% of the total number of executions (relative frequency) should be approximately 20% of the number of different contributory commands executed by a designer. In order to gain a better understanding of the events occurred during the experiment and their timing, the 18 sessions were divided into five periods each, for a total of 90 periods. This made it possible to detect behavioral patterns, such as pause times, non-modeling activities, and performance metrics, such as modeling intensities-the rate of commands per hour-, as well as training priorities. As a first step, pauses longer than two minutes were quantified, considering that designers frequently stop modeling for a short time to think, to consult other documents, such as standards, books or catalogues, or to solve issues with other designers, given that BIM workflows are known to be hybrid and not solely BIM-based [32], and sometimes they take longer pauses to rest. Therefore, the rate of commands per hour (work intensity) for each specialist in each section was calculated both with respect to real time (the actual duration of the period) and to active time (subtracting pauses longer than two minutes). Then, the distributions of real-time execution intensities of each command sub-category during the experiment were studied by generating boxplot charts for each designer, which help detecting differences between the types of commands each specialist executed, as well as identifying those modelers with higher collaborative behavior and those applying bad practices and therefore needing further training to improve their performance.
## 3 Results
### Evaluation of Synthesis of Cinnabarin from Pycnoporus sanguineus
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strain** & **Treatment** & **(Days)** & **Biomass Dissolved** & **Efficiency of the** & **Inhibition Area** \\  & & **(g) \({}^{*}\)** & **Extract (\%) \({}^{*}\)** & **(mm) \({}^{*}\)** \\ \hline  & 7 d & 0.001080 \(\pm\) 0.07 g \({}^{\mathrm{e}}\) & 2.45 \(\pm\) 0.15\% \({}^{\mathrm{d}}\) & 8.13 \(\pm\) 0.11 mm \({}^{\mathrm{i}}\) \\  & PDA & 14 d & 0.001654 \(\pm\) 0.019 g \({}^{\mathrm{e}}\) & 3.03 \(\pm\) 0.19\% \({}^{\mathrm{d}}\) & 9.51 \(\pm\) 1.04 mm \({}^{\mathrm{g}hi}\) \\  & (Control) & 21 d & 0.001357 \(\pm\) 0.09 g \({}^{\mathrm{e}}\) & 2.74 \(\pm\) 0.17\% \({}^{\mathrm{d}}\) & 11.14 \(\pm\) 1.02 mm \({}^{\mathrm{g}hi}\) \\ \hline  & 7 d & 0.001484 \(\pm\) 0.01 g \({}^{\mathrm{e}}\) & 2.87 \(\pm\) 0.18\% \({}^{\mathrm{d}}\) & 11.74 \(\pm\) 0.22 mm \({}^{\mathrm{e}f}\) \\  & PDA+P & 14 d & 0.005344 \(\pm\) 0.03 g \({}^{\mathrm{d}}\) & 5.44 \(\pm\) 0.35\% \({}^{\mathrm{c}}\) & 13.33 \(\pm\) 2.68 mm \({}^{\mathrm{d}e}\) \\  & & 21 d & 0.009181 \(\pm\) 0.06b g \({}^{\mathrm{c}}\) & 7.13 \(\pm\) 0.46\% \({}^{\mathrm{a}be}\) & 15.62 \(\pm\) 0.91 mm \({}^{\mathrm{b}e}\) \\ \hline  & 7 d & 0.006735 \(\pm\) 0.04 g \({}^{\mathrm{c}}\) & 6.11 \(\pm\) 0.39\% \({}^{\mathrm{b}}\) \({}^{\mathrm{c}}\) & 13.03 \(\pm\) 1.28 mm \({}^{\mathrm{d}e}\) \\  & PDA+C & 14 d & 0.007481 \(\pm\) 0.05 g \({}^{\mathrm{c}}\) & 6.44 \(\pm\) 0.41\% \({}^{\mathrm{b}}\) \({}^{\mathrm{c}}\) & 14.31 \(\pm\) 0.38 mm \({}^{\mathrm{c}}\) \\  & & 21 d & 0.007493 \(\pm\) 0.05 g \({}^{\mathrm{c}}\) & 6.44 \(\pm\) 0.41\% \({}^{\mathrm{b}}\) \({}^{\mathrm{c}}\) & 16.58 \(\pm\) 1.26 mm \({}^{\mathrm{b}}\) \\ \hline  & 7 d & 0.013179 \(\pm\) 0.00 g \({}^{\mathrm{a}}\) & 8.54 \(\pm\) 0.55\% \({}^{\mathrm{a}}\) & 9.16 \(\pm\) 1.38 mm \({}^{\mathrm{h}}\) \\  & PDA+G & 14 d & 0.008639 \(\pm\) 0.06b g \({}^{\mathrm{c}}\) & 6.92 \(\pm\) 0.45\% \({}^{\mathrm{a}be}\) & 10.24 \(\pm\) 0.15 mm \({}^{\mathrm{g}hi}\) \\  & & 21 d & 0.010635 \(\pm\) 0.07 g \({}^{\mathrm{b}}\) & 7.68 \(\pm\) 0.49\% \({}^{\mathrm{a}be}\) & 11.02 \(\pm\) 1.72 mm \({}^{\mathrm{g}g}\) \\ \hline  & 7 d & & & & \\ Control & 14 d & - & - & 0.00 mm \({}^{\mathrm{j}}\) \\  & 21 d & & & & \\ Control & 7 d & & & & \\ (+) & 14 d & - & - & 25.02 \(\pm\) 0.91 mm \({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular}  
* Means with different letter indicate statistically significant differences (\(p<0.05\)) by [PERSON]’s test.  
\end{table} 
Table 2: Biomass dissolved, efficiency of the extract and zone of inhibition of effect of cinnabarin on _X. campestris_ pv. _campestris_. The PDA (Control) treatment presented the lowest cinnabarin yield at 7, 14 and 21 days, with 26.10, 30.51 and 36.15 InU/g, respectively (Figure 4).
## 5 Results and Discussion
### The NPRACH Channel: A Detailed Discussion  
In this part of the investigation, border values of NPRACH repetitions were assumed (i.e., 1 and 128). Additionally, a performance comparison was made between NB-IoT radio chipsets acquired from two different vendors. Two major conclusions that can be derived from observing Figure 5 are the following:  
* An average shift between the performance DSR curves, corresponding to the same percentages, yielded 9.7 dB for Vendor #1 and 8.1 dB for Vendor #2, with \(\sigma\) = 0.26 dB and \(\sigma\) = 0.07 dB, respectively. These small values of \(\sigma\) indicate an almost constant reaction to the change in Nrep from 1 to 128. Both averages, in turn, oscillating at about 9 dB, again indicate a significantly lower response to the change in the number of repetitions than theoretically predicted in the available literature [13; 14], according to which 128 repetitions should bring a processing gain closer to 21 dB;  
Figure 4: Average distances between NPUSCH detection success ratio (DSR) profiles, corresponding to the same Nrep and (**a**) a different MCS or (**b**) within the same MCS.  
* Chipsets from both vendors responded similarly; that is, they differed on average by 0.6 dB (with \(\sigma\) = 0.36 dB) when no extra repetitions (N1) were used (solid curves). Concerning the highest number of repetitions (N128), this separation between the two performance curves extends on average to 2.2 dB, with \(\sigma\) = 0.28 dB (dotted curves).
## 2 Monitoring Protocols and Methods
### Regulatory Aspects
#### 2.7.3 Regulations Governing Post-Injection Monitoring
The changes induced by this modification of the threshold levels will be discussed hereafter. As the measurements were made above a depleted natural-gas field, CH\({}_{4}\) concentrations and flux levels were specifically checked in the soil gas and on surface. This was done to ensure that CO\({}_{2}\) injection would not induce migration of residual CH\({}_{4}\) from the depleted natural-gas reservoir.
## 4 Optimisation Results
### Modified CHP Control Algorithm  
To reduce the CHP switching frequency, 'Remove Glitch' and 'Fill Gap' methods are used in this paper, as described in Section 2.3. In order to distinguish a CHP state signal as 'Glitch' or 'Gap', the probability of CHP'switch on' in each minute needs to be acquired in advance. Figure 10 shows the probability of CHP'switch on' of all the days in averaged. After getting the probability of CHP switch on, the modified CHP switch state can be acquired and it is shown in Figure 11. In Figure 11, the solid line shows modified CHP state in that day.  
Compared to the standard CHP control algorithm, the modified CHP switch algorithm has successfully reduced switching frequency through 'Fill Gap' and 'Remove Glitch'. However, 'Fill Gap' and 'Remove Glitch' increases system operational cost slightly to 566 pence, which is about 3 pence higher than the CHP control model. Simply using CHP control model can reduce the energy cost for the domestic building; however, energy efficiency is reduced due to the redundant energy generated by CHP. Figure 12 shows the total electrical energy and heat energy that can be stored if the energy storage system is available. After calculation, total redundant heat generated by the CHP is 0.197 kWh and the redundant electricity is 1.33 kWh.  
Figure 11: The states of CHP by applying CHP control algorithm and modified CHP control algorithm.  
Figure 10: The probability of CHP Switch ‘ON’.
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 3 DMP Software Architecture Design
### DatAna  
DatAna is a component distributed across all three E2F2C layers, with a separate instance deployed at each infrastructure node. DatAna is complemented by an MQTT message broker, which is also deployed at each infrastructure node, alongside DatAna. Each instance of the MQTT ([[https://mqtt.org/](https://mqtt.org/)]([https://mqtt.org/](https://mqtt.org/)), accessed on 16 January 2023) message broker is responsible for collecting structured data, i.e., inference results, from the AI components residing on the same layer as the respective MQTT instance. Specifically, AI components publish their raw inference results to dedicated MQTT topics in real time as they are being produced through the analysis of the AV data streams they receive. The input inference results of each AI component are formatted as JSON documents according to a dedicated distinct data model that fits the requirements of each AI component. The following are the most notable fields in these data models:AV source ID. The ID of the AV source that produced the stream that was analysed to produce the inference result.
* Inference result ID. A unique identifier for the inference result.
* Timestamps. In case the inference result refers to an instant in time, a single timestamp is provided. In case the inference result refers to a period in time, two timestamps are provided, corresponding to the start and end of the time period of the result. All the time information is absolute and following the ISO 8601 UTC format.  
Besides the above, the raw inference results contain other fields that are specific to the needs of each AI component.  
Each DatAna instance residing on the same infrastructure node as an MQTT broker subscribes to the broker's topics to receive all the incoming input AI inference results. Subsequently, DatAna transforms the input inference results into SDM-compliant counterparts. Three data models that belong in the collection of smart data models of the SDM standard have been identified to be relevant to H2020 MARVEL project, which have been modified by adding additional fields to account for the project's needs:  
* **MediaEvent**: to describe general AI inference results.
* **Alert**: to describe AI inference results that should be perceived as alerts.
* **Anomaly**: to describe AI inference results that should be perceived as detected anomalies.  
DatAna autonomously selects the most appropriate data model to perform the transformation, whose output is then relayed to higher-level layers.  
Specifically, the SDM-compliant inference results produced by DatAna at the edge layer are relayed to DatAna at the fog layer and the SDM-compliant inference results produced by DatAna at the fog layer are relayed to DatAna at the cloud layer. The DatAna instance at the cloud layer is responsible for relaying the SDM-compliant inference results it collects from all the layers to the DFB by publishing them to the appropriate DFB Kafka topics.
# _remote sensing_
## 3 Results  
### Broadcast Ephemerides  
Different from the D1 navigation message with 16 orbit parameters, the number of orbit parameters of the B-CNAV (B-CNAV1 and B-CNAV2) for BDS-3 MEO satellites was 18. B-CNAV1 and B-CNAV2 have the same orbit parameters as B-CNAV. The difference between the two navigation messages is reflected in the Kepler orbit parameters' semi-major axis (A) and the mean motion (n). The square root of the semi-major axis (\(\sqrt{\text{A}}\)) is used to describe A in the D1 navigation message, but the semi-major axis difference at the reference time (\(\Delta\)A) and the change rate in the semi-major axis (A) are used in B-CNAV navigation messages. Similarly, the mean motion difference from the computed value (\(\Delta\)n) is used to describe n in D1 navigation, but the mean motion difference from the computed value at reference time (\(\Delta\)n\({}_{0}\)) and the rate of the mean motion difference from the computed value at reference time (\(\Delta\)n\({}_{0}\)) are used in the B-CNAV navigation message. The new orbit parameters describe the change in satellite orbit in more detail [3; 4; 5; 32; 33]. The broadcast ephemeris produced by IGS and the broadcast ephemeris produced by the Test and Assessment Research Center of China Satellite Navigation Office (TARC) were used to study the evolution of SISRE and the comparison between B-CNAV and D1 navigation messages.  
The IGS product only contains 16-parameter orbit information. The TARC product contains the 18-parameter orbit information of B-CNAV. The broadcast ephemeris of BDS was one epoch per hour to record orbit and clock offset information [6; 7; 21; 35]. In this study, in order to analyze the difference between two types of navigation messages (D1 and B-CNAV), we compared them at the same epochs using the ephemeris products from the Center for Orbit Determination in Europe (COD) as the reference values. Figure 1 shows the integrity of the ephemeris products. It can be observed that the ephemeris product of IGS and COD was complete, and the ephemeris product of TARC was partially missing in 2022. For TARC, the ephemeris product was missing with respect to six days (the DOY was 134, 135, 274, 275, 288, 289) of data.  
Figure 1: Missing broadcast ephemeris of the international GNSS service (IGS), the Test and Assessment Research Center of China Satellite Navigation Office (TARC), and the missing BDS ephemeris products obtained from the Center for Orbit Determination in Europe (COD) from DOY 001 to 365, 2022.
## 3 Results
### The Relationship between Canopy SIF and GPP in Multi-Timescales  
The relationship between SIF and GPP in 30 min and daily scales showed that they had a positive correlation under different weather conditions (Figure 3). On the 30 min scale, SIF and GPP had a significant correlation for all days, but the correlation coefficient decreased slightly by 0.106 on sunny days, and increased slightly by 0.109 on cloudy days. On sunny days, the slope between SIF and GPP also changed with the change of DOY and had obvious stratification due to DOY, while this phenomenon was not obvious and points were evenly distributed in all stages of the growing season on cloudy days. The points on cloudy days were more concentrated, so the correlation coefficient was higher than for sunny days, increasing by 0.215 for the 30 min scale. In addition, incoming radiation also impacted on the slope of the linear regression of SIF and GPP, impacts that were larger for cloudy conditions than for sunny days in the 30 min scale.  
The correlation between SIF and GPP deteriorates when aggregating the 30 min averages to daily averages. Among them, the correlation coefficient decreased significantly under all day and sunny conditions, and there was no significant correlation between SIF and GPP on the daily scale under both conditions. Although the linear correlation between SIF and GPP was weak on sunny days in the growing season, from a single-day scale analysis, the two had a strong linear relationship, and the fitting coefficients were mostly above 0.5 (Figure 4). As the values were aggregated, some linear relationship details on the diurnal scale became obscured. SIF and GPP on cloudy days had a significant correlation, however, with the correlation coefficient decreased by 0.134 compared to the 30 min scale. On the daily scale, we can still see the stratification in slopes caused by DOY under sunny conditions and points more concentrated in cloudy days.  
Figure 1: Seasonal patterns of SIF (**a**), GPP (**b**), PAR (**c**), VPD and SM at 20 cm depth (**d**), and NDVI (**e**) derived from Auto-SIF1 system measurements. Dots and shade represent daily means values and standard deviations that were calculated from all 30 min data in the day, respectively.  
Figure 3: Relationships between SIF and GPP at 30 min (**–c**) and daily (**–f**) timescales, **(a)** and **(d)** for all days, (**b**) and (**e**) for sunny days, (**c**) and (**f**) for cloudy days. The black straight line represents the linear regression lines for 30 min and daily data, respectively. R represents the Pearson correlation coefficient. The color scale represents the day of year (DOY). ** for \(p\) < 0.01.**  
Figure 2: Diurnal variations in canopy solar-induced fluorescence under different sky conditions in 2019. DOY represents the day of the year. (**a**), (**b**) and (**c**) represent SIF on a sunny, cloudy, and rainy day (DOY 188, DOY 190, and DOY 191), sunny day (**a**) and cloudy day (**b**) were divided by clearness index. The gray line represents PAR in 30 min scale in the same day.
## 1 Introduction  
China has some of the most serious, intense and large-scale geological disasters in the world [1]. The Hengduan Mountains area of the Yangtze River Basin is located in the second and third ladder transition zone of China on the eastern edge of the Qinghai-Tibet Plateau and belongs to several monsoon confluence areas. It has a variety of climate types and extremely complex climatic conditions. Sensitive to the effects of global climate change, the area is a high-incidence area for extreme weather and climate events [2]. Systematic assessment of geological hazards and comprehensive risk evaluation of extreme climatic conditions can provide important references for the effective development of disaster prevention and mitigation work [3]. The demand for the refined evaluation of geological disaster risks in key areas and the accuracy of evaluation results have improved in recent years [4; 5; 6]. The current risk evaluation methods mainly include qualitative evaluation methods (expert scoring method [3], fuzzy comprehensive evaluation method [4; 5; 6; 7], semi-quantitative evaluation method (information value method [7; 8], entropy value method [8; 9], frequency ratio method, logistic regression model [10; 11], artificial intelligence model [10; 11], weight of evidence method [12; 13; 14], coefficient of determination method [15; 16; 17; 18], analytic hierarchy process [19; 20; 21; 22], quantitative evaluation methods (infinite slope model [23; 24; 25], 3D limit equilibrium method, slope hydrological model [26; 27; 25], etc.  
Climate change has a profound impact on major water conservation projects in the Yangtze River Basin. The Jinshajiang River is an important section of the Yangtze River Basin, and in recent years, extreme weather events have been accompanied by frequent geological hazards, seriously affecting construction and people's well-being [28]. The downstream section of the Jinshajiang River was selected as a typical area to evaluate the vulnerability of typical landslides to geological hazards, taking into account the formation mechanism of geological hazards and extreme rainfall trends [29; 30]. In order to adapt to the impact of climate change and mitigate the risk of geological hazards, there is an urgent need to carry out an assessment of climate change and geological hazard risk and to formulate a targeted disaster reduction strategy for climate change adaptation in the Yangtze River Basin [31].  
The purpose of this study is to investigate the relationship between geological hazards and their influencing factors (topography, geology, human engineering activities, rainfall) in the study area, determine the main controlling factors and influencing factors for the occurrence of disasters and use future rainfall trends to predict the occurrence of geological disasters.
# Post-Quantum Authentication in the MQTT Protocol
## 3 Results
### Results for KEM Authentication
#### 3.2.2 Bandwidth Used  
To measure the memory bandwidth required by the signature-based and KEM-based MQTT protocols, the number and size of packets sent back and forth were monitored by writing the name and size of each packet sent and received on the client to a log file. The number of packets is the key factor when comparing bandwidth required by protocols, as more packets means more overhead at each lower layer in the network architecture. However, one protocol may have an advantage if the size of its packets is smaller. As is evident in Figures 2 and 3, both signature-based and KEM-based MQTT protocols send the same number of packets. Since both protocols have a similar number of packets, this means they will require similar amounts of bandwidth. Therefore, any advantage seen will be due to the packet size.  
As shown in Figure 8, the KEM-based MQTT protocol has the largest total number of bytes to send at 13,524 B, compared to 7,732 B in the signature-based MQTT protocol. This is due to the shared secret \(ss_{e}\) being sent both ways in the handshake of the KEM-based protocol.  
The KEM-based protocol also has the largest individual packet to send, at 3619 B for the BrokerHello message, compared to 2883 B for the BrokerHello message in the signature-based protocol. Given that the number of packets sent is equal, the bandwidth advantage of the signature-based version is minimal, with only a difference of approximately 1.5 KB.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 2 Motivation
### Requirements of a New Architecture  
Due to the use of handovers and other salient features of femtocells, the following requirements have to be considered when designing a handover-based interference management architecture.  
* _Mintimization of handovers:_ Current OFDMA-based cellular systems (e.g., 3 GPP LTE and 5G NR) support hard handovers only. Thus, a user's handover accompanies a temporary interruption of his ongoing voice or data service as well as a signaling overhead. To minimize users' performance degradation, handovers should be triggered only when service deterioration for users would otherwise be inevitable or if a significant reduction of the interference to macro- and femtocell users is expected. Moreover, any solution algorithm should be able to reach a desired system condition with the minimum number of handovers. Ping-pong handovers must be avoided as well.
* _Low computation complexity:_ This is an important design requirement due to the expected large number of users to be supported by a single BS in forthcoming cellular networks (resulting from the increased bandwidth) and multiple femtocells to be deployed within a macrocell. The Next Generation Mobile Networks (NGMN), an alliance of cellular operators, specifies that a 5G network should support up to 150 thousand concurrent user sessions per square kilometer [3]. To meet such a high connection density, the number of femtocells to be installed within a macrocell coverage area could be up to several thousands in the future. Consequently, the search space of a handover-decision algorithm will be widened significantly.
* _No special hardware:_ For cost-efficiency, femto BSs and user devices should not be required to have special hardware.