esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Physical Performance Tests
#### 2.5.2 Performance Tests
* Drop jump [82]: The drop jump test involves jumping after falling from a specified height, beginning in a position with the legs extended and executing a downward movement. The movement must be performed continuously with the hands on the hips and the trunk kept straight. This test is used to evaluate the \"reflex-elastic-explosive\" manifestation and to verify and assess strength [80]. The reliability of the drop jump test has been demonstrated by an intra-class ratio coefficient of 0.70-0.92, a standard error of measurement of 8.5-18.4 milliseconds (ms) and a coefficient of variation of 3.6-6.4% [80]. * Medicine Ball Throwing Test (3 kg) [83] Using the medicine ball throwing test, upper body strength was evaluated [84]. The starting position is without prior impulse, from a standing position, with the legs comfortably apart, the feet in a symmetrical position and the ball held with both hands above and behind the head. In this way, the ball is thrown forward with as much force as possible, so that it falls within the throwing sector. The reliability obtained [85] through a test-retest is r = 0.98, and the validity of the drop jump test was evaluated using the intraclass correlation coefficient (ICC), which yielded a score of 0.98, indicating consistency between the means of each throwing mode (standing, kneeling, sitting, one-handed). The correlation coefficient was also calculated, resulting in a score of r = 0.49, with a significant \(p\)-value of <0.01 [67]. * Agility Test with Change of Direction, Illinois COD Agility Test (IAGT CDD) MicroGate(r) Witty Wireless Training Timer photoelectric cells (Microgate SrI, Bolzano, Italy) were utilized in the study. These cells have a minimum resolution of 0.125 Ms and an event delay of 1 ms. They also incorporate a redundant code with information accuracy check and self-correction, with a pulse transmission accuracy of 0.4 Ms.
* Illinois agility test with change of direction: The Illinois agility test consists of the following steps: To begin the Illinois agility test, assume a face-down position on the starting cone-marker, with your hands placed at shoulder level. When the assistant indicates and activates the stopwatch, the athlete must get up as quickly as possible, and run the entire course drawn by the cones, in the shortest time. The Illinois agility test [86] is widely regarded as a standard agility test [87] thanks to its strong validation and reproducibility [88; 89]. As a result, it has become the go-to test for measuring changes in directional skill, making it a reliable tool for athletes and trainers alike [90]. According to research, the intraclass correlation coefficient and standard error of measurement for the Illinois agility test are 0.96 (95% CI, 0.85-0.98) and 0.19 s, respectively. In addition, the IAGT CDD has been found to be a reliable and valid test, with a correlation coefficient of 0.31 (95% CI, 0.24-0.39) and a significance level of \(p<0.05\)[59]. These findings suggest that the IAGT CDD is a trustworthy tool for assessing agility.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 2 Related Works
### Received Signal Strength Indicator
Therefore, the proposed weighted least-squares method solves the non-linear problem of RSSI-based localization and provides a precise estimation of the target location.
## 3 Results
### Woody Biomass Residues Based Process (WBR-bp)  
Results obtained in the LC analysis of the glucose production process using woody biomass residues as starting material (Figure 6) showed that the pretreatment stage of the starting biomass was the most relevant one, contributing to 50%-70% of the overall impact in all the category indicators. The severe operating conditions required for the pretreatment of wood biomass are the most plausible explanation for the significant impact caused by this stage.  
Together with the pretreatment stage, the conditioning of biomass is almost as relevant in the categories of climate change (CC), fossil depletion (FD), and ozone depletion (OD). In this case, the requirement to operate at low solid concentrations in combination with positive high-temperature conditions leads to an elevated heat duty, being the main responsible for most of these impacts.  
The contribution of the hydrolysis process in the WBR process to the environmental impact of this option for glucose production is almost negligible. This involves a significant difference as compared to the starch-based process (see Figure 4), which is caused by the existence of the conditioning step in the woody biomass residues process. Since the temperature conditions are higher in the conditioning step than in the enzymatic hydrolysis stage, the cellulose concentration being quite similar (\(\sim\)7 wt%), the impacts associated to the energy consumption are transferred from the hydrolysis to the conditioning stage, as it is in this latter where the process stream is warmed up to the operating conditions.  
Transportation of raw materials, based on our calculations, has a minor influence on the waste woody biomass process. However, further investigation should be performed to determine the implications involved in the biomass gathering and transportation, as unlike in the MS-bp case, some types of biomass waste are usually found as a scattered resource, increasing the environmental and logistic costs of its recovery and transportation to the processing plant.  
A detailed analysis of the pretreatment and conditioning steps has been performed because of their large contribution, in all the studied categories, to the environmental impact of the WBR-bp case. Results are included in Sections 3.2.1 and 3.2.2.  
Figure 6: LCA results obtained for the relative contribution of process stages to the evaluated impact categories in glucose production from woody biomass residues.
## 2 Underwater Sensor's Internal Structure  
The internal components of a submerged or acoustically isolated sensor organize include the CPU-on-board control, sensor interface HW, acoustic modem, memory, power supply, and sensor. Every application of an acoustically isolated sensor contains these parts, which comprise most of the main body.  
The primary control is linked to the sensor via a sensor interface circuitry [11]. The CPU or control collects the sensor's data, stores them in memory, analyzes them, and then sends them via an acoustic modem to other sensors is shown in the Figure 3. Occasionally, bottom-mounted instrument frames are designed to authorize unidirectional messages to protect all sensor components from potential trawling gear damage [12].  
Submerged acoustic message channels are centrally influenced by factors such as water heat, common, multi-path, Doppler spread, and sign lessening. Every one of these components causes a high-piece mistake and a deferred change [13]. Accordingly, message connections in UWSNs are particularly blunder-inclined. Additionally, sensor hubs are mostly powerless in cruel, submerged conditions. In contrast, their earthly, submerged systems have a higher hub disappointment rate and parcel misfortune likelihood [14]. UWSNs are generally sent in a three-dimensional space. This situation is different from the two-dimensional sending of most earthly sensor systems. These qualities of submerged  
Figure 2: Underwater Sensor System Architecture.  
sensors raise numerous crisp difficulties and make the current directing conventions for earthbound sensor systems unsatisfactory here. For UWSNs, the directing conventions ought to have the option of dealing with the hub versatility and the unreliable message joins with high vitality proficiencies [15].
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 2 Related Work
[18] proposed an energy efficient clustering with delay reduction in data gathering scheme that focuses on energy efficient routing of data from sensor nodes to base station using multiple mobile sinks. This increased the network lifetime, throughput, and delivery rate. However, the proposed mechanism did not consider the issue of speed control for the mobile sinks and hence the cooperation between mobile sinks are not mentioned. [PERSON] et al. [19] presented a bio-inspired networking strategy to allow the cooperation of static sensors on the ground and multiple mobile sensors in the air, applied to the scenario of large areas surveillance. The strategy can provide efficient communication among the sensor nodes, reduce the number of messages exchanged in the network. However, the proposed mechanism did not consider the issue of speed control for the mobile sinks and hence the cooperation between mobile sinks are not mentioned. [PERSON] et al. (2019) proposed a data collection scheme, called HiCoDG, which adopted multiple mobile sinks to collect and relay data in a cooperative manner. The proposed HiCoDG algorithm aims to find the optimal paths for mobile sinks and global mobile sink, for minimizing the traveling distances of mobile sinks. However, the proposed mechanism did not consider the balance of the number of nodes in the cluster and the distance between the current cluster-head and the next cluster-head. Most of the studies mentioned above emphasize the improvement of the data fresh problem. But most of them did not take into account the velocity control for mobile sinks and the cooperation between the mobile sinks. As a result, the latency of data collection might be increased and the constructed path is not efficient enough. This paper proposes a cooperative data collection algorithm, called CDCA. Compared with the existing studies, the proposed CDCA exhibits several contributions, including achieving the purpose of cooperative data collection, prolonging network lifetime, balancing the workloads of sensors for packet forwarding and maintaining a stable cycle for the rendezvous opportunities between each mobile sink and global sink. The following summarizes the comparisons of related works and the proposed CDCA, in terms of several important parameters which impact the performance of the network lifetime of the given sensor network and cooperation between the mobile sinks. Table 1 compares each related work with this paper in terms of number of mobile sinks, path construction, data collection latency and mobile sink cooperative. It is shown that the proposed mechanism exhibits all good characteristics, as compared with the other six mechanisms.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 3 Results of Predicting the Number of Heatwave-Related Patients
### Model Application and Visualization  
To apply the validated model, we predetermined the dynamic variables from predictions. Because the time series of variables and result values were the same, the predicted variable values must be used for prediction. With regard to static variables, we employed the latest data as inputs among the information that is updated periodically, which is the same as in model learning. The values of the dynamic population were replaced with dynamic variables using weather forecast data provided by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MAE** & **RMSE** & **RMSLE** & \(R^{2}\) \\ \hline Logistic regression & 5.301 & 12.460 & 0.855 & 0.593 \\ SVM & 5.184 & 8.800 & 0.956 & 0.797 \\ Decision tree & 5.524 & 13.384 & 0.803 & 0.531 \\ Random forest & **3.816** & **8.655** & **0.645** & **0.804** \\ \hline \hline \end{tabular} The bold is the best result among other methods.  
\end{table}
Table 2: Comparisons of performance evaluation.  
Figure 5: Variance importance in the RF model.  
KMA on a weekly basis and a time series forecasting library, called Prophet [47], which is provided by Facebook.  
The performance results and visualization of the model are shown in Figure 6. From the end of May, which was when heatwave management began, the substituted variables were inputted into the model for 4 weeks, and then the predicted values were obtained and compared with the observed values obtained from KDCA on the weekend. The forecasted and observed values for Seoul were compared for 4 weeks, and in the second week of June, the predicted values for each administrative district of South Korea were numerically quantified to visualize the high-risk areas and provide information to heatwave disaster response decision makers. The high-risk areas are shown in dark colors, whereas the lower risk areas are shown in lighter colors. Considering objectivity by region, we used the number of patients and the predicted floating population ratios to calculate the risk. Four weeks of data were applied to real-world situations, resulting in an \(R^{2}\) value of 0.70.
## 4 Estimation of Improvement Depth of Dynamic Compaction
[7] conducted field tests to measure the increase in ground subsidence as the dynamic compaction energy per unit area of compaction increases, as shown in Figure 5. [PERSON] (1993) proposed the relationship between improvement depth of dynamic compaction and ground subsidence, as shown in Table 5. When a 20-ton tamper with the radius of 2.0 m was dropped 12 times at a height of 15 m on sand deposit (compaction energy = 286 ton\(\cdot\)m/m\({}^{2}\)), the ground subsidence was estimated to be about 0.5-1.0 m, as shown in Figure 5. The ground subsidence was estimated to be in the range of 0.3-1.0 m with respect to the improvement depth of 10 m based on the natural sand of zone 1, as shown in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tamper Weight W (ton)**} & \multirow{2}{*}{**Drop Height H (m)**} & \multicolumn{2}{c}{\(DI\) (m)**} \\ \cline{3-4}  & & \(\alpha\) = 0.60 & \(\alpha\) = 0.65 \\ \hline \multirow{3}{*}{20 ton} & 10 & 8.5 & 9.2 \\ \cline{2-4}  & 15 & 10.4 & 11.3 \\ \cline{2-4}  & 20 & 12.0 & 13.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation of improvement depth using Equation (1) for various tamper drop heights. Figure 5: Ground subsidence vs. drop energy per unit area [7]. Figure 6, described by [PERSON] [9], shows the wave propagation due to dynamic compaction. [PERSON] et al. [7] estimated the improvement depth and the degree of improvement from the results of Standard Penetration Test (SPT), CPT, and PressureMeter Test (PMT). The depth of maximum improvement is generally about 1/3 to 1/2 of the influence depth of dynamic compaction. When dynamic compaction is applied, the ground is improved greatly down to a depth of 3-5 m, and the degree of improvement gradually decreases with depth. Almost no ground improvement is expected from the depth of about GL-10 m or below.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 6 Conclusions and Future Work  
In this paper, a UAV-LS system is developed including a dynamic task assignment and trajectory planning algorithm, a software toolkit and hardware design and realization. In particular, the dynamic formation transformation algorithm works in a conditional-triggering manner where local TAPs of connected components are constructed and solved online to achieve minimum flight distance and to reduce the impact of collision avoidance on task assignment. To build such a UAV-LS system, a wireless transmission subsystem was established to transfer data based on the MAVLink protocol between the UAVs and the GCS, and the RTK-GPS technique was utilized to provide centimeter-level positioning accuracy for UAV. Simulations and experiments were conducted to verify the proposed UAV-LS system.  
In future work, efforts will be devoted to cooperative formation transformation techniques. Flight distance balancing issues will also be considered, i.e., the difference between the flight distance of each UAV is minimized to avoid battery energy imbalance.  
Methodology, G.T. and Y.C.; software, G.T. and J.Z.; investigation, G.T. and Y.C.; writing--original draft preparation, G.T. and Y.C.; writing--review and editing, Y.C. and J.H.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China under Grants 61603094.  
**Institutional Review Board Statement:** Not applicable.  
Figure 16: Trajectories of six quadrotors planned by the proposed formation transformation algorithm.  
Figure 17: The minimum distance of each quadrotor from the others.
## 4 Discussion  
### Validity of the Preliminary Assessment  
A direct comparison was done between the values obtained for _RiskT_ and _UTCI_ condensed in Table 3, to understand how different the collected samples of the proposed risk criteria and the established, but simplified _UTCI_ were.  
_UTCI_ presented a higher risk frequency overall with an absolute algorithmic difference of 0.22% when comparing yearly data, 1.09% during summer and mid-season, or 2.48% during summer. Yet, _RiskT_ presented an absolute 1.37% more hours of potential thermal stress risk during mid-seasons and 0.66% during winter when compared to _UTCI_.  
Figure 6: Conjoint risk frequency distribution on a yearly basis for 2019’s data extracted from weather and air quality station Milano v. Juvara.  
\begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{2}{c}{**RiskT and AQI**} \\
**Season** & **\# of Hours** & **Hours at Risk** & **Ratio** \\ \hline Whole year & 8760 & 485 & 5.54\% \\ Summer and mid & 4392 & 480 & 10.93\% \\ summer & 2208 & 435 & 9.96\% \\ Mid & 2184 & 45 & 1.02\% \\ Winter & 4368 & 5 & 0.11\% \\ \hline \end{tabular}
\end{table}
Table 5: Summary results of the allocated multi-hazard risk, when using _AQI_ \(>\) 100 as the criterion, differentiated by seasons.  
The absolute algorithmic difference between risk and no risk hours ranged between 19 and 108 h, being least for the 8760 yearly period (i.e., 19) and highest for the 2208 h in summer. This was considered non-negligible; thus, the classification accuracy was computed, assuming as true values those computed for \(UTCI\), using Equation (2), as 91.56%; from: 360 False Positives (_FPs_), 379 False Negatives (_FNs_), 964 True Positives (_TPs_), and 7057 True Negatives (_TNs_).  
These results were expected as the assumption of \(MRT\approx t_{db-air}\) did not truly express the condition of a person outdoors, and _RiskT_ was configured in a way in which it gave a large significance to the presence of intense \(I_{tot}\) (i.e., \(I_{weight}=0.3\)). However, with an accuracy over 90% on a large sample (>8000 measurements), it could be assumed that the approach is feasible for preliminary analyses.
## 2 Methodology
### Approach for Travel Time Measurement  
#### 2.2.1 Train Travel Time Measurement  
For the measurement of train travel time, a model is developed based on [PERSON]'s approach to measuring intercity travel time by train [31]. The total travel time by train between two cities consists of five parts (Figure 1) and it can be calculated as follows:  
\[T_{ij}^{rail}=T_{iI}+T_{wI}+T_{IJ}+T_{wJ}+T_{lj} \tag{3}\]  
where \(T_{ij}^{rail}\) is the total travel time from city \(i\) to city \(j\); \(T_{iI}\) and \(T_{lj}\) stand for the travel time using intracity public transport from the origin to the optimal train station and from the optimal to the destination, respectively; \(T_{IJ}\) is the travel time by train between two stations; \(T_{wI}\) is the transfer waiting time at the train station \(I\), including the time of entering the station, security check, and waiting for the train; and \(T_{jw}\) is the waiting time at the train station \(J\), including the time of exiting the station and queueing for public transport.  
\(T_{IJ}\) and \(T_{wI}\) were retrieved from the National Railway Customer Service Website ([[https://www.12306.cn](https://www.12306.cn)]([https://www.12306.cn](https://www.12306.cn)), accessed on 1 April 2023). \(T_{iI}\), \(T_{wJ}\), and \(T_{lj}\) are the components of intracity travel, derived from the simulated query module of public transportation on Amap ([[https://ditu.amap.com](https://ditu.amap.com)]([https://ditu.amap.com](https://ditu.amap.com)), accessed on 1 April 2023). Amap is able to automatically calculate the time spent on intracity transport using public transportation. It will include real-time data on walking and transfers during this process. The API built in this study can be used to export these data. This is a typical advantage of utilizing online mapping technologies. It is recommended to arrive at the station at least 30 min prior to departure to allow for ticket verification by the China Railway Customer Service Center.  
However, due to the online mapping technology, the train travel mode encompasses both high-speed rail and regular train services, making it difficult for us to obtain real-time navigation data separately for each. In China, most domestic online mapping software applications face this limitation. Consequently, the train travel data utilized in this study encompasses both HSRs and regular train journeys.  
Figure 1: Schematic diagram for calculating intercity travel time.
## 6 Discussion  
The presented results demonstrate that it is possible to significantly influence the performance of the legacy heating control system by algorithms that only adjust the set point of temperature of central heating circuit supply water. Details regarding the optimization algorithms' implementation are outside the scope of this article, but the performance metric clearly shows differences in their effectiveness.  
The main implication of the presented results for legacy central heating installations is that optimization algorithms using data from external sources (like wireless temperature sensors) can be seamlessly added, even if the legacy controller has no proper communication interfaces to establish direct communication. Existing communication protocols like TCP, or more specifically, MQTT, can be used to exchange the data with an external processing unit that carries out the necessary computations and is equipped with appropriate communication interfaces.  
The split between the legacy controller specialized in orchestrating valves, pumps, etc., and the optimization unit responsible for adjusting the set point of the temperature of central heating circuit supply water is clearly defined. Another advantage of that solution is that software engineers can operate on a platform of their choice (in our case it was an edge router with Raspberry Pi Compute Module, shown in Figure 4), instead of learning the nuances of the legacy platform designed to perform much simpler operations without the capability of running, for example, Python or Matlab code. More advanced solutions involving artificial intelligence and machine learning can also be introduced in a similar way. In particular, we want to apply a reinforcement learning training method by rewarding central heating control strategies that minimize \(E_{1}\), \(E_{2}\), and \(E_{3}\) indices. The learning agent should be able to find improved adjustments of the TCR08 set point by learning through trial and error.  
The main disadvantage of the proposed solution is that it requires some modifications of the legacy controller control logic. The effect of scale is hard to achieve, as every modification of the existing CH installation requires spending time and resources on learning the legacy controller. For old installations, source codes might not be available, making modifications painful and time-consuming. Deeper analysis is required to learn when performance improvement, time and material costs, and system complexity justify modifications of the existing system instead of simply replacing it. The proposed architecture is very scalable--it can be used in any central heating system with a local controller capable of running injected programming code to communicate with an external optimization algorithm. Thus, it can be used in a single-family or a multi-family building as well as in an office. Algorithms can run either on local machines or in the cloud, which further increases system scalability and reliability.