esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Future Climate Projections
#### 3.2.3 Extreme Heat Waves  
In the previous section, average HW measures were considered. Here, extremes of HW measures are compared amongst HIST, MED, and LONG. Figure 15 shows the CDF for DUR (a), INT (b), RF (c), Tmax_ave (d), and Tmax_sum (e) for HIST, MED, and LONG.  
Figure 14: (a) NDAYS and (b) Tmax_sum by HW type for HIST, MED, and LONG.  
Figure 15: _Cont._  
There is a marked shift of percentiles toward higher values from HIST to MED and LONG. The exception is for RF, where percentiles decrease in the future (MED and LONG), meaning a reduction of average daily temperature range during HW. This reduces the relief that people may experience during the nights of HW days when the Tmax itself is higher in MED and LONG than in HIST.  
Some extreme HW are identified for the three climates. Although rare by definition, they shed some light on extremely hot conditions that can be experienced, or what may be a mega HW in the future. The criteria used here to identify extreme HW are the longest DUR, highest Tmax ave, and Tmax_max.  
In this study, the statistical analysis of HW was only performed for the warmer extended summer, since these may be relevant to heat stress conditions. However, due to warming, it may be that HW occurring in the future, in the colder months (November to April), may also cause heat stress similar to that experienced in the present climate during summer. Since the HW identification performed by the authors considered all months, a sample of extreme HW simulated in the colder months, together with extreme HW for the extended summer was selected. As mentioned before, the models used in this study may underestimate the Urban Heat Island effect in locally amplifying the magnitude of the HW; therefore, temperatures may be higher than shown.  
Table 2 shows a summary of some HW measures, namely, DUR, Tmax_ave, and Tmax_max, for some of the extremest HWs, in terms of duration/intensity, in each climate, simulated by WRF, for colder months (a) and for the extended summer (b). This list of HW is not exhaustive. These help to anticipate, for example, that HW occurring in the non-extended summer in the future (MED or LONG) may have characteristics similar to those occurring in HIST in the extended summer.  
Figure 15: CDF of (**a**) DUR, (**b**) INT, (**c**) RF, (**d**) Tmax_ave and (**e**) Tmax_sum for HIST, MED, and LONG.  
Some considerations are as follows:  
* In March and November HW with Tmax_ave greater than 29 \({}^{\circ}\)C.
* In October HW with long duration (i.e., 20 days) and Tmax_ave with more than 34 \({}^{\circ}\)C.
* In Summer, HW with long duration (i.e., 10 days) and Tmax_ave in excess of 42 \({}^{\circ}\)C.  
In general, HW with high DUR and Tmax_ave are expected to occur in the future during spring and autumn than those that were typically observed during summer. For the future projections, HW of strong intensity and duration, never observed before, are expected to occur.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 3 Data Collection
### Incident Data  
Traffic incident data used in this study were obtained from the SunGuide database for a period spanning from 1 July 2018, to 30 June 2019, for the I-75 corridor. The SunGuide database recorded a total of 3550 incidents that occurred during this period. The records comprise critical incident information such as incident description and spatial and temporal characteristics. The recorded data includes all types of incidents, such as crashes, roadway debris, disabled vehicles, and fire vehicle involvement.  
Detailed descriptions of incident data categories follow:  
* Incident Description: This consists of general features of an incident, including incident type, number of blocked lanes due to the incident, and incident severity.
* Spatial Characteristics: The location of the incident is documented by recording the travel route as well as incident point longitude and latitude.
* Temporal Characteristics: The incident timeline is recorded, starting with notification time, verification time, and dispatching and response times.  
Additionally, the roadway clearance time is recorded. Notification time is the time at which the TMC was notified. Dispatching and response time indicate the time for the first responder to arrive at the incident scene. Finally, the clearance time records the time when all lanes of the roadway are clear and the last responder has left the site. A detailed description of each incident dataset field is presented in Table 1.
## 2 Experimental
### Adsorption Studies  
Batch adsorption experiments were performed to test the potentiality of the composites on degradation of Acid Red 66 dye. All dyes adsorption studies were carried out in 50 mL dye-containing solution with a concentration of 2 g:L\({}^{-1}\) at 25 \({}^{\circ}\)C. The 100 mg of iron composites were used at pH to 6.8 under vigorous stirring conditions of 150 rpm for 2 h to ensure the extensive adsorption equilibrium. The mixture was separated by magnetization and collected using filtered liquor to measure dye concentration by UV-Vis technique with an Evolution 60S(r) Thermo Scientific equipment (Waltham, MA, USA). For comparison, each adsorption experiment was conducted twice to depict any adsorption behavior of Acid Red 66 dye by iron-based composites.  
The adsorption experiments were performed to evaluate the thermodynamic effects and to obtain adsorption isotherms. Also, studied on the amounts of adsorbed dye (mg:g\({}^{-1}\)) were determined from their concentrations, before and after adsorption, by applying Equation (1):  
\[q=\frac{(C_{\alpha\eta}-C_{o})\times V}{m} \tag{1}\]  
where \(C_{o}\) and \(C_{\alpha\eta}\) are the initial and final values of the dye concentration (mg:L\({}^{-1}\)), respectively. The volume (L) of the dye solution is represented by \(V\). Furthermore, \(m\) is the mass of the adsorbents (g).  
The adsorption kinetics were studied at 25 \({}^{\circ}\)C under pH = 6.8 and stirring of 150 rpm. Approximately, 100 mg of the composite adsorbents were dispersed in 50 mL of a 30 mg:L\({}^{-1}\) Acid Red 66 dye solution. The obtained mixture was stirred at 25 \({}^{\circ}\)C and aliquots were withdraw and separated by magnetization. Thereafter, the dye concentrations in the supernatant were determined using a UV-Vis spectrophotometer, at regular time intervals.  
The working adsorption isotherm models such as Langmuir and Freundlich equations were applied as described in the literature [16]. The kinetic adsorption was evaluated using the pseudo-first, pseudo-second order kinetic model and Weber-Morris intraparticle diffusion kinetic models, as described elsewhere [8].
## 5 Discussion
### Comparison of Results with the State-of-the-Art Methods  
In the context of comparing the results of this study with those of state-of-the-art methods, a search for related studies that also used MIT-BIH data was performed while considering the interpatient and intrapatient approaches. With respect to the performance indicators for comparison, it is necessary to analyze each paradigm separately. For the interpatient paradigm, researchers do not usually only use global indicators for comparison purposes, which are as important as the comparison with indicators by class. In this context, Tables 12 and 13 were prepared for the interpatient approach, and the same divisions (DS1 and DS2) were adopted for all studies, albeit with some differences in the organization methodology that do not interfere with the purpose of general comparison. Considering the indicators by class presented in Table 13, the difficulty in classifying the S class is widely recognized in all studies, and the trend in greater overall accuracy implies a lower sensitivity for the N class, as can be observed in [23; 48], with values of 91.3% and 40.3%, respectively. There is a wide variation in focus in the development of studies which should also be considered for comparison, as studies such as those by [15; 23] \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Work**} & **Acc** & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{2}{c}{**F1 Score**} \\  & **\%** & & \% & & \% & & \% & \\ \hline  & **N** & **S** & **V** & **N** & **S** & **V** & **N** & **S** & **V** \\ \hline [PERSON] et al. (2004) [44] & 83.88 & 86.9 & 75.9 & 77.7 & 99.2 & 38.5 & 81.6 & 92.6 & 51.1 & 79.6 \\ [PERSON], [PERSON] (2009) [48] & 91.47 & 91.7 & 88.3 & 89.9 & 98.9 & 39.5 & 71.2 & 95.2 & 54.6 & 79.5 \\ [PERSON], [PERSON] (2011) [46] & 90.62 & 91.8 & 84.8 & 81.9 & 99.5 & 10.9 & 88.4 & 95.5 & 19.3 & 85.1 \\ [PERSON], [PERSON] (2014) [49] & 93.00 & 91.6 & 81.4 & 86.2 & 99.3 & 31.6 & 31.6 & 95.3 & 45.5 & 79.5 \\ [PERSON] et al. (2014) [61] & 86.66 & 88.9 & 79.1 & 85.5 & 99.0 & 36.0 & 92.8 & - & - & - \\ [PERSON] et al. (2017) [22] & 92.38 & 94.0 & 62.0 & 87.3 & 98.0 & 53.0 & 59.4 & 95.9 & 57.1 & 70.7 \\ [PERSON] et al. (2021) [15] & 97.48 & 99.4 & 74.6 & 95.7 & 98.2 & 89.5 & 93.2 & 98.8 & 81.4 & 94.4 \\ [PERSON] et al. (2021) [23] & 80.58 & 79.6 & 91.3 & 87.3 & 99.5 & 40.3 & 93.2 & 88.5 & 56.0 & 90.1 \\ [PERSON] et al. (2022) [50] & 86.12 & 99.8 & 46.1 & 85.2 & 98.6 & 44.9 & 86.6 & 99.2 & 45.5 & 85.9 \\ [PERSON] et al. (2023) [51] & 98.19 & 99.3 & 83.3 & 91.4 & 98.9 & 83.5 & 97.4 & 99.1 & 83.4 & 94.3 \\ CNN-AM-MLII-F1 & 97.29 & 98.3 & 41.1 & 95.6 & 98.9 & 28.0 & 96.2 & 98.6 & 33.3 & 95.9 \\ CNN-AM-MLII-S & 83.89 & 83.3 & 77.5 & 93.8 & 99.6 & 7.6 & 71.4 & 90.7 & 13.9 & 81.1 \\ CNN-AM-MLII + V-F1 & 98.48 & 99.4 & 43.3 & 98.0 & 99.1 & 56.3 & 97.0 & 99.2 & 49.0 & 97.5 \\ CNN-AM-MLII + V-S & 95.47 & 95.8 & 69.3 & 96.6 & 99.4 & 20.2 & 97.2 & 97.6 & 31.3 & 96.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of research on the interpatient paradigm—indicators by class.
# Static Magnetic Fields Effects on Polysaccharides Production by Different Microsalgae Strains
## 2 Materials and Methods
### Static Magnetic Fields Application  
In _A. platensis_ SAG 21.99 photobioreactor assays, SMF was applied with ferrite magnets (150 \(\times\) 50 \(\times\) 10 mm) placed outside the glass bottles. SMF were applied for 1 h d\({}^{-1}\) in the light period or for 24 h. For _Spirulina_ sp. LEB 18 and _C. fusca_ LEB 111 raceway cultivations, 6 magnets were placed for each and during the same periods (24 h d\({}^{-1}\) and 1 h d\({}^{-1}\)). The outdoor assays were performed from March to April, at the end of summer and beginning of fall (Southern hemisphere). The growth results are demonstrated and detailed in previous studies [16; 36].  
For all assays, the magnetic intensity was measured using a Gaussmeter (TLMPHALL-05 k-T0, Cotia, Brazil). To obtain the mean intensity value applied to microalgae, different measurements within the photobioreactor were made, as represented in Figure 2. Magnetic intensity of the magnets was around 30 mT for _A. platensis_ SAG 21.99 cultures and 25 mT  
Figure 1: Scheme of research design experiments.  
for the other strains studied. The magnets used have the following characteristics: barium ferrite, anisotropic and axial bipolar.  
For all conditions, control cultures were conducted with the same operating conditions (temperature, irradiance and initial biomass concentration) than the assays with SMF application except that magnetic field were not applied.
## 3 Results and Discussion
### Effect of Carbonation by SCD on Converter Slag
#### 3.1.4 pH Analysis  
Figure 5 shows the pH measurements of leachate from the converter slag before and after reaction with SCD. The initial pH of leachate from as received converter slag was 11.37. As time passed, the pH slightly decreased, but it was stabilized at around 11.14 after 10 days. The initial pH of leachate from reacted converter slag was measured to be 9.80 due to the carbonation from SCD. The drop in pH was 1.49 compared to the unreacted sample. The pH of leachate from converter slag decreased continuously over time and stabilized at around 9.35 after 10 days of measurement. The result indicates converter slag can be safely utilized for land reclamation material after the reaction with SCD.
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
###### Abstract  
This paper proposes a low-complexity and energy-efficient light emitting diode (LED)-to-LED communication system for Internet of Things (IoT) devices with data rates up to 200 kbps over an error-free transmission distance up to 7 cm. The system is based on off-the-shelf red-green-blue (RGB) LEDs, of which the red sub-LED is employed as photodetector in photovoltaic mode while the green sub-LED is the transmitter. The LED photodetector is characterized in the terms of its noise characteristics and its response to the light intensity. The system performance is then analysed in terms of bandwidth, bit error rate (BER) and the signal to noise ratio (SNR). A matched filter is proposed, which optimises the performance and increases the error-free distance.  
LED photodetector; LED-to-LED communication; BER; matched filter; visible light communication (VLC) +
Footnote †: journal: Sensors
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 3 Background of Middleware
### Publish/Subscribe Middleware
#### 3.1.1 Types of Publish/Subscribe Middleware  
Researchers classify publish/subscribe middleware based on how they route, process, and deliver messages. This section explores the various publish/subscribe middleware types. We develop the taxonomy in Figure 6 from existing taxonomies the literature [16; 28; 29; 30]. Each of these classifications impacts performance, scalability, and use-case suitability. The figure classifies publish/subscribe middleware according to the subscription model, architecture, middleware location, and event messaging technique.  
In terms of the subscription model, middleware can be push-based, delivering messages as soon as they are published, ideal for real-time applications, or pull-based, where subscribers fetch data on demand, which is better for bandwidth efficiency and historical data access [16; 30]. On the system architecture category, middleware can be either brokered, where a central or distributed broker manages message routing and enhances reliability and QoS [28; 29], or brokerless, where devices communicate peer-to-peer (P2P) for low latency and improved fault tolerance, especially suitable for resource-constrained environments like VANETs and IoT networks [16]. Classification of middleware by deployment location also matters: on-premise middleware offers greater control and reduced latency,  
Figure 5: A publish/subscribe-middleware-based network.  
Figure 6: Taxonomy of publish/subscribe middleware.  
while cloud-based solutions enable scalability and accessibility, especially in large-scale or IoT-heavy systems [30; 31]. Lastly, middleware classification by event messaging techniques includes subject-based (by channel, topic, or group), content-based (based on data attributes), and type-based (based on message structure). Each technique affects how subscribers filter and receive information, with content-based middleware offering high relevance but higher processing overhead [28; 29; 32].
## 3 Wheel Flat Recognition and Positioning Algorithm Based on Multisensor Arrays  
### Design of the Algorithm  
Using the established models, we simulated the output signals of the multisensor arrays during a bogie passage. Figure 15a shows the movement of the defected bogie and the configuration of the sensors. There is a wheel flat with a depth of 0.2 mm at the front wheel, whereas the rear wheel is intact. Ten pairs of sensors are mounted on the rail web, marked from \(S_{1}\) to \(S_{10}\). The length of a multisensor array is 5.4 m, which is longer than the circumference of the wheel. Thus, every wheel revolves two times during its passage of the measurement area. This ensures that a flat spot on the wheel hits at the rail tread within this segment no less than two times. Figure 15a shows the output signals of \(S_{4}\). The successive two troughs represent the respective moments when the front and rear wheels arrive at the location of \(S_{4}\). Similarly, Figure 15b,c displays the outputs of \(S_{5}\) and \(S_{6}\). We found that the outputs of different sensors have almost the identical main trend, except for the time delay due to the arrangement of sensors.  
Apparently, the defected front wheel significantly influences the outputs of sensors. There are two obvious impact processes sensed by all the sensors. At 0.21 s, the defected wheel exactly hits the rail above \(S_{5}\). Therefore, the fluctuation amplitude of \(S_{5}\) at this moment is much greater than those of \(S_{4}\) and \(S_{6}\). With the rotation of the wheels, the flat spot on the front wheel hits the rail again between \(S_{9}\) and \(S_{10}\), at 0.34 s. However, this time the fluctuation caused by the impact process occurs just around the second trough of \(S_{6}\) that reflects the arrival of the rear wheel. The reason is that the rear wheel is above \(S_{6}\) at this moment. If we only observe the outputs of \(S_{6}\), it is easy to draw the wrong conclusion that the rear wheel is also damaged.  
Since the in situ condition is much more complex than this case (the only imperfectness is a flat spot on the front wheel), separate analysis can hardly acquire the accurate information. Even if we observe the responses of multiple sensors on the same wheel, it is still difficult to have a comprehensive understanding of the wheel states. Therefore, data fusion of multiple sensors is critical to the better diagnosis of wheel defects. On the basis of the correlations between sensors, we designed a wheel flat recognition and positioning algorithm. As shown in Figure 16, by solving the three key problems of \"when\", \"where\", and \"which wheel\", successively, it accurately accesses the damage states of the wheels.
## 3 Component-Wise Details of LPaMI
### Data Filtering and Migration Phase
#### 3.2.1 Data Migration from HDFS to Relational Database  
We now present details of processing of the raw text data (stored in the HDFS) containing OOIs. The aim of this phase is to clean, filter, format, and normalise the data. A sample of raw text data is shown in Figure 6b that hints at the travel record of a person in terms of visiting different places. Each row contains the name of an OOI along with its timestamp. We also observe that there are few statements with information errors resulting in the non-recognition of entities. This occurs due to classification inaccuracy.
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 3 Results
### Cluster Analysis
#### 3.1.3 Estimating the Electricity Generation Potential of Each Cluster  
According to the electrical potential of the different township-based clusters in Table 2, the total energy potential into the national grid from rice straw within the 43 townships is 279.14 MW. High-High Clusters have 51.88 MW, while High-Low Clusters have 25.73 MW. Low-Low Clusters have 3.84 MW. Although some of the townships do not exit as cluster patterns, Kungyangon, Thongwa, and Hmawbi have the potential of 27.43 MW, 36.67 MW,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Township Name** & **Cluster Type** & **Rice Straw Generation (ton)** & **Electricity Potential (MW)** \\ \hline
**Kyauktan** & Not Significant & 262,814.46 & 25.30 \\ \hline
**Htantabin** & Not Significant & 355,211.87 & 34.19 \\ \hline
**Kawhmu** & High-High Cluster & 261,696.52 & 25.19 \\ \hline
**Kayan** & High-High Cluster & 277,279.32 & 26.69 \\ \hline
**Dala** & High-Low Cluster & 110,021.13 & 10.59 \\ \hline
**Thanlyin** & High-Low Cluster & 157,252.94 & 15.14 \\ \hline
**Dagon Myothit (East)** & Low-Low Cluster & 22,458.52 & 2.16 \\ \hline
**Dagon Myothit (Seikkan)** & Low-Low Cluster & 15,501.94 & 1.49 \\ \hline
**Mingaladon** & Low-Low Cluster & 1207.23 & 0.12 \\ \hline
**Kyeemyindaing** & Low-Low Cluster & 3637.47 & 0.35 \\ \hline
**Seikggikanaungto** & Low-Low Cluster & 701.33 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Township-based clusters for rice straw.  
and 28.17 MW, respectively. Applying the electric potential from different clusters, the decision maker can decide on the strategic plan for bioenergy promotion.
## 2 Background  
### The QCA Basics  
The main component of QCA technology is the QCA cell. Each QCA cell contains two electrons, which, based on the coulombic interactions created between the two electrons [9], create two logical values, \"0\" and \"1\" [13]. Every QCA cell contains four holes, in which two electrons are trapped inside. By placing two electrons in four holes, due to the coulombic interactions between the electrons, two electrons are always positioned diagonally [14]. As a result, two stable states are created, and the electrons are diagonally placed inside the cavities to keep the maximum distance from each other and to establish the coulombic interaction law. By placing electrons in the diameter, two structures are created, which we attribute to the polar structures \(+1\) and \(-1\) with the logical values \"1\" and \"0\", respectively; the poles \(1+\) and \(1-\) are the same poles of the square cells shown in Figure 1[15].  
The clocked QCA maintains four clocking phases (switch, hold, release, and relax) to transmit a signal [8], as shown in Figure 2. During the switch phase, the interdot barriers become lower, and the data propagation happens through electron tunneling. The potential barrier is raised gradually, and the cell becomes polarized according to the modes of the input drives. At the end of this phase, the barriers are high enough to prevent electronic tunneling. During the hold phase, the barriers remain high, and the cell retains the polarization state. In this state, the cell maintains a steady state and transmits data to the neighboring cells. Indeed, the outputs can be used as inputs to the next stage. During the release phase, the barriers gradually decrease, and the QCA cells start to lose their polarization by lowering the barriers. During the relaxation state, cell barriers stay on the lowest level and in an unpolarized state [9; 10].  
The most simple gate in a logic circuit is the inverter gate (NOT gate). Figure 3 shows these gates [16]. In QCA logic circuit design, the majority gate (MV) is the most commonly used logic gate. This gate has three inputs and one output cell. The majority vote of the inputs determines the output. As a result, the majority of the logical values of the inputs specify the polarization and the value of the output cell [14; 16]. Figure 4 shows different samples of this gate.  
Figure 1: The QCA cell.  
Figure 2: Clock phases in QCA.  
By stabilizing one of the inputs of the majority gate and valuing the logical \"0\" (polarization \(-1\)), the AND gate is constructed [16,17]. Figure 5 shows the AND gate of two inputs. The OR gate is also created by fixing one of the inputs of the majority gate and value logical \"1\" (polarization +1) [16,17]. Figure 6 also shows the OR gate of two inputs.