esa-sceva/satcom-chunk-collection huggingface dataset:
# A Smart Parking Solution Architecture Based on LoRaWAN and Kubernetes
## 1 Introduction
Our solution uses LoRaWAN which is a low power consumption and long-range protocol. In terms of backend infrastructure, some smart parking solutions use open-source software elements such as Message Queuing Telemetry Transport (MQTT) for exchanging information [12] between sensors and the backend. Others like [13] uses a whole backend platform and only take care about sensors and communication protocols. In the proposed scenario we promote the use of open-source software by designing a cluster architecture to increase availability and scalability. This work reviewed several deployments, proposed and built an architecture based on Libelium smart parking sensors, LoRaWAN and open-source technologies for the backend like a cluster of Kubernetes with MQTT and MongoDB. The proposed architecture has aimed to combine several technologies to develop a robust smart parking system from the perspectives of stability and availability of the service. This combination intends to provide high availability, scalability and portability to a smart parking system. Likewise, this architecture aims to provide a base guide for building this type of solutions by considering the use of specific purpose sensors, adequate low-power and long-range communication protocols and a clustered backend infrastructure. From the availability and stability perspective, other solutions do not provide redundancy over a clustered-environment. They do not use a messaging server (i.e., Rabbit) which can be a bottleneck. In addition, if the main server handling and processing the information fails, data from sensors would be lost. In comparison to other works, in the proposed architecture if the main server in charge of handling the information collected fails, all messages are accumulated in the messaging server for further processing. There is better performance based on the modularity of the system. The main contribution of this work is to provide a base architecture for building smart parking systems by considering all elements that could be easily connected by using several technology components. The paper is structured as follows. Section 2 describes other work reviewed in this work. Section 3, describes a brief analysis of the previous works. Then, the Section 4 explains the proposed smart parking solution. Later, Section 5 presents the results gotten after the implementation of the proposed solution. After, Section 6 discusses strengths of the proposed solution, and finally, the present work is concluded in Section 7.
## 2 Materials and Methods
### Sensors and Geopositioning  
The IoT infrastructure for data collection in the garden of Villa Rvedin Bolasco was implemented via a gateway with two antennas. The gateway for LoRaWAN was a WisGate Edge Max RAK7249 and the 2 antennas are 8 dBi fiberglass antennas. The antennas are omnidirectional, but due to limitations regarding the cultural heritage site, they had to be positioned just outside the top floor of a building and not on the rooftop. To obtain the most coverage, the two antennas were positioned at opposite sides of a corner, thus covering all parts of the garden (see Figure 1).  
To cover the area of the garden, a total of 27 IoT devices were initialized and geopositioned. A list of devices is available in Table 1. with their positions visible in Figure 1. Note the fact that there are not 27 locations because more than one sensor model can be placed in the same spot, e.g., the pyranometers with climate sensors and atmospheric sensors such as in Figure 2.  
The majority of the sensors were placed on a fixed pole at 1.5 m above the ground level at specific locations of interest. The points of interest were near park benches, which are rest areas where young and elderly visitors can sit, talk, and enjoy the garden. The power source was provided by alkaline batteries that guarantee a minimum operating time of 5 months up to 11 years depending on the sensor, except for the Synetica enLink Air-X and Iotsens Sound Level, which require alternating current power supplies.  
Figure 1: Study area (Villa Rvedin Bolasco) with locations of the devices with climate, weather, noise, and air quality sensors and LoRaWAN antennas. Multiple sensors have been installed in the locations labelled 1, 2 and 3. Green circles are the tree canopies.
# Quantum Neural Networks Approach for Water Discharge Forecast
###### Abstract  
Predicting the river discharge is essential for preparing effective measures against flood hazards or managing hydrological droughts. Despite mathematical modeling advancements, most algorithms have failed to capture the extreme values (especially the highest ones). In this article, we proposed a quantum neural networks (QNNs) approach for forecasting the river discharge in three scenarios. The algorithm was applied to the raw data series and the series without aberrant values. Comparisons with the results obtained on the same series by other neural networks (LSTM, BPNN, ELM, CNN-LSTM, SSA-BP, and PSO-ELM) emphasized the best performance of the present approach. The lower error between the recorded values and the predicted ones in the evaluation of maxima compared to the case of the competitors mentioned shows that the algorithm best fits the extremes. The most significant mean standard errors (MSEs) and mean absolute errors (MAEs) were 26.9424 and 4.8914, respectively, and the lowest R\({}^{2}\) was 84.36%, indicating the good performances of the algorithm.  
Q NNN; water discharge; extremes; errors Article
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 5 Conclusions  
Here, we analysed a set of extreme ecological events between 2001 and 2019 in the Mediterranean region and assessed how they are related to soil moisture and land surface temperature. We selected the two most severe episodes from each of the three domains and evaluated GPP over the affected areas in the year of extreme events and the two subsequent years.  
Our results show the influence of the land-atmosphere coupling on Mediterranean vegetation, with soil moisture-temperature feedbacks playing an important role in vegetation productivity, as well as the importance of warm springs in contributing to early soil moisture depletion and the amplification of hot and dry summer impacts on vegetation. High tree cover areas showed generally higher resistance to dry conditions than low tree cover areas and croplands.  
Our work highlights how remote sensing data can be used to detect and monitor ecological extremes, their functional recovery times and the role of land cover in mediating these relationships. Furthermore, the projected increase in the frequency and severity of hot and dry conditions in the Mediterranean emphasizes the need for understanding the dynamics of their impact on vegetation and recovery times in this region.  
**Supplementary Materials:** Table S1, Table S2, Table S3 Pearson correlation coefficients between SM-GPP, LST-GPP and SM_IST anomalies during spring and summer for each land cover-type in 2005 and 2012 over IB; The following are available online at [[https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)]([https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)) 0/rs13194010/s1, Tables S1, S2, S3.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] participated in the conceptual design of the study. All the authors contributed to the interpretation and analysis of the results and the redaction of the manuscript. [PERSON], [PERSON] and [PERSON] defined the datasets and the methodology to be used for the study. [PERSON] made all the calculations, figures and tables and wrote the manuscript. Each of the co-authors performed a thorough revision of the manuscript, provided useful advice on the intellectual content and improved the English language. All authors have read and agreed to the published version of the manuscript.  
Funding: This work was partially supported by national funds through FCT (Fundacao para a Ciencia e Tecnologia, Portugal) through projects IMPECAF (PTDC/CTA-CLI/28902/2017), FireCast (PCIF/GRF/0204/2017) and UIBD/50019/2020-Instituto Dom Luiz. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
Figure 13: As in Figure 4 but over IB during 2012 event (left panels), EM during 2003 event (central panels) and WE during 2006 event (right panels).
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimization of Each Layer in the Base Region
#### 3.2.3 Increased Carrier Injection Efficiency through SCH Tunneling  
In DML, it is difficult to improve the modulation bandwidth because of the transport effect on carriers in QW [55]. To accelerate the carrier transport in DML and reduce the carrier lifetime, a tunnel injection (TI) structure that greatly weakens the carrier transport effect was invented [56]. An energy band diagram of the structure is shown in Figure 9 [57]. In the TI structure, tunneling is the main carrier transport from the emitter to the first QW. Subsequently, the carriers between the wells are transported through a mixture of tunneling and diffusion, which significantly reduces the auger recombination and increases the efficiency of the radiative transition and output light intensity. Overall, the modulation bandwidth of the TI-DML is larger than that of conventional DML.  
Based on their research on TI-DML, [PERSON] et al. predicted that the bandwidth of TI-TL can be significantly better than that of conventional TL [38]. Subsequent studies have demonstrated that the bandwidth of TI-TL is 44 GHz, which is higher than the 29 GHz bandwidth of conventional TL [58].  
The tunneling properties of TI-TL were also studied extensively by [PERSON] et al. [59]. They noted that the tunneling structure considerably reduced the threshold current and increased the modulation bandwidth compared with conventional MQW-TL structure. This change is more pronounced as the tunneling probability f (i.e., \(0\leq\text{f}\leq 1\)) increases, which characterizes the probability of the charge carriers tunneling into the first QW from the upper base SCH layer. The simulation shows that increasing f increases the modulation bandwidth [59]. When no tunnel structure is added at f = 0, all carriers enter the QW by diffusion, and the bandwidth is 29.01 GHz. As f increases, due to the tunneling effect, the number of carriers entering the QW increases, the transport process is accelerated, the recombination lifetime decreases, and the relaxation frequency and bandwidth increase. In the ideal case (f = 1), all carriers are tunneled into the QW; in this case, the bandwidth reaches a maximum of 56 GHz. Noted that the peak amplitude of the relaxation oscillation increases by approximately 3 dB as f goes from zero to one. In theory, this is not suitable for signal fidelity. Therefore, in the process of transmitting signals using TI-TL, the trade-off between the relaxation peak amplitude and modulation bandwidth must be considered.  
As f increases from zero to one, the second harmonic distortion decreases, and the third-order intermodulation distortion (IMD3) is reduced from \(-27.17\) to \(-39.42\) dBm, while the fifth-order intermodulation distortion (IMD5) is reduced from \(-43.59\) to \(-48.5\) dBm. This proves that the distortion of the signal is suppressed as tunneling occurs [60].
## 3 Current Measurement Techniques
### Snow Property Determination Techniques
#### 3.2.5 Snowpack Models  
The outcomes of the SnowMIP project [44, 45], although now dated, provide us with a good overview of the capabilities and limitations of a wide range of snowpack models created for different purposes. Of these many models, we will briefly consider two (related) models and the capabilities that they can provide.  
Alpine3D [46, 47] can calculate SWE based on [48] at grid points. Alpine3D is driven by meteorological data, using the SNOWPACK model as the core snow-modeling module. The SNOWPACK model is a point-based physical process model that combines micro and macro scale processes to describe the development of the snowpack over time. The combination of SNOWPACK in Alpine3D allows not only spatial coverage of the SNOWPACK model, but also allows the inclusion of further external driving parameters from the terrain (such as reflected radiation and shadowing [22]) as well as boundary layer effects on the snowpack (such as wind redistribution of snow [21, 27]) and is the best estimate of spatially distributed SWE that we currently have access to. It is however, a model, not a measurement, and is still under development, so as well as errors in the input data (erroneous data from meteorological stations and/or due to the resolution of the digital elevation model), it is also subject to parameterization errors.
## 6 Experimental Evaluation
### Scenarios  
To evaluate the proposed FWP model, four experimental scenarios were implemented. By conducting these experiments, we aimed to gain insights into the capabilities and limitations of the FWP model and to validate resource profiling optimization under different scenarios.
# A Petri Net-Based Algorithm for Solving the One-Dimensional Cutting Stock Problem
## 8 Discussion
It should be remarked that, in instances where the FBS-PN-CSP algorithm does not achieve the best solutions, its results were very close to the best ones and in most cases just by one stock above. Therefore, industries handling stock lengths and item types as those of the _hinterding_ and _falkenauer_U_ datasets could obtain good or optimal cutting plans. Even, the FBS-PN-CSP can yield optimal or near-optimal cutting plans for stock lengths like the ones of _hard28_ and _wae_gau1_ datasets in acceptable times. The building industry, the door and window framing industry, and the furniture industry to name a few, could benefit from the FBS-PN-CSP algorithm implementation. These industries usually have several orders, few item types, and not-so-long stocks. However, in industries like the wire industry where the stock length can be extremely long, in the tens of thousands of units, the FBS-PN-CSP could fail to yield cutting plans close to the optimum number of stocks used and in an acceptable time.
## 3 Results and Discussion
### Lipase Inhibitory and Hemoglobin Antiglycation Activities  
The anti-lipase and hemoglobin antiglycation activities of extracts from _S. eleaaggnifolium_ are presented in Table 3. The inhibitory lipase enzyme of the extracts was evaluated at various concentrations (Figure 2A). The reference compound, Orlistat (with an IC\({}_{50}\) of 0.128 \(\pm\) 0.003 mg/mL), demonstrated stronger inhibitory activity compared to SEFE (with an IC\({}_{50}\) of 0.167 \(\pm\) 0.0006 mg/mL). Conversely, SEFR exhibited a higher inhibitory impact against the pancreatic lipase enzyme in comparison to both SEFE and Orlistat, boasting an IC\({}_{50}\) of 0.106 \(\pm\) 0.0008 mg/mL (\(p\) < 0.01). These results are consistent with the results of [PERSON] et al. who tested the anti-lipase activity of _S. eleaaggnifolium_ berries at two stages of ripening; they found that the anti-lipase activity of rip berries is 0.164 \(\pm\) 0.023 mg/mL [9].  
The in vitro haemoglobin glycosylation inhibition assessment indicated a significant decrease in glycosylation as a function of concentration (Figure 2B). _S. eleaaggnifolium_ extracts demonstrated significant antiglycation activity, with 3.990 \(\pm\) 0.236 mg/mL of SEFE and 3.997 \(\pm\) 0.140 mg/mL of SEFR. Gallic acid also demonstrated significant antiglycation activity, with a value of 3.008 \(\pm\) 0.030 mg/mL. _Solanum nigrum_, which is characterized by the presence of molecules with anti-glycation properties such as solamargine and solasonine [22], which are also found in our plant, is another species with an anti-glycation  
Figure 1: HPLC-DAD chromatogram of _S. eleaaggnifolium_ extracts at 254 nm (fruits (SEFR) and leaves (SEFE)) and identified with the standards (catechin (1), vanillin (2), naringin (3), quercetin 3-O-\(\beta\)-D-glucoside (4), rutin (5), cinnamic acid (6), p-cumaric acid (7), quercetin (8), trans-3-hydroxycinnamic acid (9), kaempfer (10), and flavone (11)).  
\begin{table}
\begin{tabular}{l l l l l} \hline  & **SEFE** & **SEFR** & **Orlistat** & **Gallic Acid** \\ \hline Lipase inhibitory & & & & \\ IC\({}_{50}\) (mg/mL) & 0.167 \(\pm\) 0.0006 & 0.106 \(\pm\) 0.0008 & 0.128 \(\pm\) 0.003 & - \\ Antiglycation & 3.990 \(\pm\) 0.236 & 3.997 \(\pm\) 0.140 & - & 3.008 \(\pm\) 0.03 \\ IC\({}_{50}\) (mg/mL) & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Determination of anti-lipase and hemoglobin antiglycation activities of the leaf extracts (SEFE) and fruit extracts (SEFR) of _S. eleaaggnifolium_. Results are shown as mean \(\pm\) SEM (n = 3).  
effect. The extract of _S. nigrum_ inhibited the formation of advanced glycation end products (AGEs) [36].
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna  
In our computational study, we used the optimization algorithm described in the previous section to optimize the AoO of the Vienna-based electric car sharing provider ELOOP [13] for a variety of future expansion strategies. In their free-floating car sharing system, customers are allowed to drop off their rented cars anywhere within this area, which is also referred to as _home zone_ by the company. Trips to locations outside of the home zone are possible, but customers need to return the car to the home zone to end the rental.
## 3 Results and Discussion  
### Relationship Between Deformation and RGB Values  
Figure 6 shows the intensities of R, G, and B values with respect to the applied displacement. The R, G, and B values were normalized as the ratios of the intensities changed by deformation to the initial intensities. The changes in R, G, and B values varied depending on the displacement level.  
Figure 5: Image processing workflow and ViT architecture. The 6 channels input into the Vision Transformer in this study include the R, G, B, H, S, and V channels.  
In stage I (0-1 mm), R, G, and B showed discoloration characteristics. The B values were the most sensitive to the initial displacement, showing a relatively steep slope. The R and G values were similar during this stage and showed a slight decrease. R and G showed different behaviors from those at stage II (1-3 mm). R increased continuously until the end of this stage, whereas the G values remained constant up to 1.5 mm, followed by a sharp decrease up to 2 mm. Subsequently, this decrease slowed down. Within this displacement range, the decrease in B slowed, and B increased from 2 mm; however, overall, it showed low-sensitivity discoloration. In stage III (3-4 mm), the R value increased. In contrast, G showed an increasing trend from 3.5 mm, which was similar to the trend observed from 0.8 to 1.5 mm in stage I and II. However, B showed an increasing trend until the end of the experiment at 4 mm and showed a slope similar to that of R. Owing to these characteristics, RGB can be appropriately defined as a polynomial function (dotted lines in Figure 6) within the 0-4 mm displacement range. These relationships are consistent with those reported previously [6]. R and B exhibited R\({}^{2}\) values of over 0.91 in relation to displacement, while G showed an R\({}^{2}\) value of 0.84.  
Consequently, in the initial displacement of stage I, B showed the dominant discoloration characteristic, whereas G showed the main discoloration in stage II. The discoloration of R showed remarkable discoloration characteristics at displacements of 1 mm or more, including stages II and III; that is, a large displacement. This is owing to the change in the reflection characteristics according to the deformation of the internal structure of the mechanochromic sensor. This trend aligns with the wavelength shift shown in Figure 2, where the color changed sequentially from blue to green to red as the strain increased. Color B, which was sensitive during the initial displacement in stage I, reappeared prominently in stage III due to the periodicity of discoloration governed by Bragg's diffraction equation (\(\lambda=2\)d\(\cdot\)sin\(\theta\)). This observation is consistent with previous observation [16], which reported that the color of the sensor returned to its initial B beyond a strain of 50% (4 mm displacement). However, the color in this second cycle was deteriorated. Therefore, the discoloration of the sensor in the first cycle is effective for predicting deformation, while the reliability of using discoloration in the second cycle remains questionable.  
Figure 6: Changes in RGB depending on displacement.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### The Comprehensive Optimization of the Rotor's Key Parameters
#### 3.3.2 Torque Performances of the Final Machine  
Figure 10 shows the design and optimization process of the IPM motor, which is divided into three steps: Firstly, the initial key motor parameters including the \(w_{rib}\), \(w_{b}\), and \(h_{rib}\) were obtained in step 1, aiming at acceptable torque and power that were close to the requirements. The torque and torque ripple were 910 Nm and 14.4% when step 1 was finished. Secondly, the rotor pole surface offset was employed, and optimized along with the split ratio (obtained by \(D_{rot}\)/\(D_{so}\)), \(w_{m}\), \(h_{m}\), and \(R_{2}\) by multi-objective genetic algorithm carried out on JMAG. Figure 11 shows the influences of the \(D_{ro}\), \(w_{m}\), \(h_{m}\) on the torque performances. The optimized torque was 902 Nm, which was lower than the result in step 1, since the efficiency was considered in step 2. Meanwhile the torque ripple was reduced to 8.7%. Finally, the deep optimizations of the three key rotor parameters, \(w_{rib}\), \(w_{b}\), and \(h_{rib}\) were carried out, aiming at low torque ripple and high mechanical strength. Figure 12 compares the torque output performances due to different optimization methods mentioned in the previous section. Table 3 shows the comparison of the initial and final parameters. The average torque, torque ripple, and \(k_{rip}\) of the original machine before optimization was 910 Nm, 183 Nm, and 14.4%, respectively. When the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & \(h_{rib}\) & \(w_{rib}\) & \(w_{b}\) \\ \hline Average torque & High influence & medium & medium \\ Torque ripple & medium & High influence & medium \\ \hline \hline \end{tabular}
\end{table}
Table 2: The influence of the three key parameters on torque performances.  
Figure 10: Demonstration of the design and optimization process.  
Figure 9: Analysis of key factors affecting output torque. (**a**) Output torque. (**b**) Torque ripple.  
eccentric rotor pole arc was employed, the average torque and torque ripple was 902 Nm and 8.77%, respectively. Then the rotor key parameters were furthermore optimized based on the eccentric rotor pole model. Finally, after a comprehensive optimization of the rotor topology and parameters, the torque ripple was reduced to 46.5 Nm with \(k_{rip}\) = 3.84%, which was much better than the original model, at the cost of a slight drop in average torque output.  
Figure 11: Influences of the \(D_{n}\), \(w_{m}\), and \(h_{m}\) on the torque performances. (**a**) Influences of the rotor’s outer diameter, \(D_{ro}\). (**b**) Influences of the magnet width \(w_{m}\). (**c**) Influences of the magnet height \(h_{m}\).