esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Result and Discussion
### Relationship Between Leakage and Airflow Rate  
The leakage airflow was calculated by substituting the indoor-outdoor pressure difference value due to the change in the supply airflow measured through the experiment into the leakage function Equation (3) derived from the airtightness performance measurement using the air handler. The yellow dots in the Figure 13 represent data points that illustrate the relationship between the pressure difference (\(\Delta\)P) between indoor and outdoor environments, measured in Pascals (Pa), and the leakage flow rate (QLeakage), measured in cubic meters per hour (CMH). The shape of the plotted data points is the air leakage function curve. When the pressure difference is positive, the QL value indicates a leakage airflow from indoors to outdoors owing to the higher static pressure indoors compared  
Figure 11: Pressure difference with changes in supply and return airflow rates.  
Figure 12: Comparison of pressure difference obtained through measurement and prediction in series operation.  
that of the outside air. The relationship between the difference in the supply and return airflow and the leakage volume is shown in Figure 14. The experimental results revealed that if the supply airflow is approximately 2000 CMH larger than the return airflow, the leakage airflow is approximately 3000 CMH. Data points where \(\Delta\)Q (Supply-Return) are around 500 to 1000 CMH, and the corresponding leakage airflow (QLeakage) ranges are from 1000 to 2000 CMH. This points indicates a stable leakage range for moderate differences in supply and return airflow. Here, the leakage remains in a predictable range when the supply-return difference is neither too high nor too low, suggesting that under these conditions, the building envelope maintains a consistent leakage rate. In contrast, if the return airflow is approximately 2000 CMH larger than the supply airflow, the moisture entering the room from the outdoors is approximately 1000 CMH. The knowledge of the difference between the supply and return airflow enables the prediction of the leakage in a building. Lastly, we predicted the pressure difference by utilizing the supply and exhaust airflow difference and substituted it into the leakage function to derive an expression to predict the leakage amount.
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
###### Abstract  
The technical performance of an inflatable solar dryer (ISD) to dry amaranth leaves (_Amaranthus_ spp.) was investigated. To handle the drying of lightweight materials, modification was made by adding an air deflector and trays inside the ISD. Computational performance of the ISD solar energy system was evaluated using MATLAB Simulink. The estimated air mass flow in the inlet of the ISD was 0.75 kg/s. Using computational fluid dynamics (CFD), the uniformity of air distribution in the ISD was evaluated. The solar radiation reported during good drying performance ranged between 510 and 950 W/m\({}^{2}\). In a controlled charging system, a 100 W\({}_{\text{p}}\) PV module typically generated voltage between 10.22 and 18.75 V. Drying conditions at temperatures of 40 \({}^{\circ}\)C or above were typically achieved in the ISD from 12:00 to 16:00. Temperature inside the ISD could reach up to 69.4 \({}^{\circ}\)C during the day and 13.4 \({}^{\circ}\)C during the night. The highest relative humidity of 97.4% was recorded during the night. Opening the ISD while mixing the product could lead to considerable heat loss. Fluctuation of solar radiation and shaded areas in the ISD appeared to be the major factors affecting the drying performance.  
off-grid; photovoltaic; postharvest; renewable energy; Simulink; solar bubble dryer +
Footnote †: journal: Applied Soft. 2019, 9, 3431; doi:10.3390/app9163431
## 2 CRKG in Three Typical Duplex Modes
### Frequency Division Duplex (FDD) Mode  
FDD mode is dominant in existing cellular connections, e.g., LTE, NB-IoT, and it is also an indispensable mode in 5G and beyond [27]. In FDD mode, the uplink and downlink transmissions operate at different carrier frequencies simultaneously. Therefore, CRKG in FDD mode is not affected by temporal dynamics and it can support high mobility and high frequency communications. However, the uplink and downlink sub-bands are separated by the frequency spacing, therefore most of the reciprocal channel parameters used in TDD systems, such as received signal strength, channel gains, envelope and phase, can be quite different in FDD systems, depending on the channel coherence bandwidth. It is thus very challenging to find instantaneous reciprocal channel parameters in FDD mode.  
One possible solution is to employ frequency-invariant parameters, e.g., the multipath angle and delay [28], which unfortunately can hardly be estimated accurately without massive multiple antennas or wide bandwidth. A second way is to establish combinatorial channels with reciprocal channel gains with the aid of an additional reverse channel training phase, termed as loopback based protocols [29; 30; 31]. However, these protocols may complicate the channel sounding process and have potential security threats due to passive eavesdropper capturing entire transmissions [32].  
Alternatively, we can construct the reciprocal channel parameters by taking advantage of the fact that the propagation paths in the uplink and downlink are reciprocal in most FDD systems [33]. The frequency spacing between the uplink and downlink sub-bands in LTE systems is much less than the center frequency. For example, the center frequency of Band 1 (IMT) is 2100 MHz while the duplex spacing is 190 MHz; the center frequency of Band 30 (WCS) used by AT&T in United States is 2300 MHz while the duplex spacing is only 45 MHz [34]. The duplex spacing is generally less than 10% of the center frequency. Literature and field measurements have shown that the uplink and downlink transmissions travel the same propagation paths and experience the same clusters [27; 35]. The frequency spacing leads to the path differences between the uplink and downlink, especially to the phases. The observed channel information would be different due to the superposition of multipath effects. Different channel paths should thus be separated by wideband or massive antennas in order to extract the reciprocal channel parameters. According to the uplink and downlink frequencies, some compensation methods are used to remove or decrease the differences, and extract the reciprocal parameters to generate the secret key. Further studies are still required to model and prototype CRKG schemes, when the frequency spacing is large.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Remote Sensing Data Processing  
The GCFD data primarily utilize remote sensing and meteorological data as predictive factors. Remote sensing variables include the fraction of absorbed photosynthetically active radiation (FAPAR) and leaf area index (LAI), while meteorological variables include 2 m temperature (Ta), surface solar radiation downward (SW_IN), latent heat flux (LE), and sensible heat flux (H). FLUXNET 2015, FLUXNET-CH4, and Drought-2018 were used as site datasets, which include meteorological data and carbon flux variables. The remote sensing data, including FAPAR and LAI, were sourced from the Copernicus Global LandService (CGLS) version 2 dataset, which has a 1 km resolution and covers the period from January 1999 to June 2020. The meteorological variable reanalysis data were sourced from the EAR5-Land dataset. With the above data, machine learning models are employed to predict global-scale NEE, GPP, and Reco.  
Therefore, using GCFD data, the corresponding data for the Ulastai station is extracted and compared with the field-measured data for validation. The suitability of the GCFD data in the Ulastai region was evaluated using Pearson correlation coefficient (\(R\)), root mean square errors (\(RMSEs\)), and \(Bias\) indicators.  
\[R=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline{y} \right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}}\sqrt{\sum_{i =1}^{n}\left(y_{i}-\overline{y}\right)^{2}}} \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(x_{o,i}-x_{m,i} \right)^{2}} \tag{4}\]  
\[Bias=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(x_{o,i}-x_{m,i}\right) \tag{5}\]  
In the equation, \(x_{i}\) represents the measured values and \(y_{i}\) represents the GCFD data. \(\overline{x}\) and \(\overline{y}\) are the average values of the GCFD data and measured values, respectively; and \(x_{o,i}\) and \(x_{m,i}\) represent the measured values and GCFD data at each time step, respectively.  
The specific methodological flow is shown in Figure 4.
# Seismic Behavior of RC Beam Column Joints with 600 MPa High Strength Steel Bars
## 3 Experimental Results and Discussion
### Hysteretic Curves and Skeleton Curves  
Figure 6 shows the hysteretic curves of the six specimens. The curves maintain excellent linear elastic properties, and the hysteresis rings are close to straight lines with low energy consumption before the first cracking. With the cracks appearing and developing, the skeleton curves turn nonlinear, and the slopes of the curves decrease gradually. A noticeable reduction in stiffness can be observed after the yield of beams, and the curves follow a downward trend until the specimens failed, which is caused by the concrete crushing. Compared with BD1, BD2, and BD3, the specimens QD1, QD2, and QD3 exhibit a more significant pinch phenomenon of the hysteresis curve, which shows that the pinch behavior of beam-column joints with high-strength steel bars is commonly more severe. With the same reinforcement ratio, the ductility of the specimen QD1 with higher axial compression is weaker than that of specimen QD2 with a lower axial compression ratio. Because the shear stresses of QD3 and BD3 are relatively small, it can be seen from the hysteresis curve that their ductility is higher, while their energy dissipation capacity is poor.  
Figure 7 compares the skeleton curves of the six specimens. QD2 experiences a longer yield stage than QD1 because of different axial compression ratios, which indicates better deformation capacity. Similarly, BD1 has a similar ultimate load as BD2, but BD1 has a lower ductility than BD2. QD3 has a smaller axial compression ratio and a longitudinal reinforcement ratio, so the yield load and ultimate load are lower than those of QD1 and QD2. However, the ductility of QD3 is more adequate than that of QD1 and QD2. The deformation capacities of all the specimens in the downward direction are found to be inferior to those in the upward direction. Under the same conditions, the yield and ultimate loads of QD1, QD2, and QD3 are less than the ones of BD1, BD2, and BD3, while their performance of ductility is slightly better.  
With the increase of the displacement, the corresponding load is taken as failure load when the load decreases to 85% of the maximum load. Table 4 illustrates that the yield load and ultimate load of BD1 and BD2 are higher than those of QD1 and QD2. This is mainly attributed to the higher axial compression ratios. It can be seen that the strength of reinforcement increases with the reinforcement ratios. Furthermore, the ratio of yielding strength to the ultimate strength under upward loading is higher than that under downward loading. In addition, the ratio increases with the decrease of the reinforcement ratio.  
Figure 6: Hysteretic curves for six specimens. (**a**) QD1; (**b**) QD2; (**c**) QD3; (**d**) BD1; (**e**) BD2; (**f**) BD3.  
Figure 7: Skeleton curves.
# Effect of Ultrasonic Vibration on the Surface Adhesive Characteristic of Iced Aluminum Alloy Plate
## 1 Introduction
Currently, practical research methods to achieve this aim include simulation and experimental methods. The simulation method aims to calculate the vibration mode of a research object and the distribution of shear stresses at the adhesive interface. However, the distributing state of shear stress at the adhesive interface is seldom researched. Additionally, the experimental method is commonly used to measure the time length of deicing. In these experiments, most research objects are small lumpy ices on the substrate surfaces, and the time length of ice removal is selected as one of the key measuring parameters. However, in many real icing conditions, such as the icing conditions of wind turbines, the structure of the ice is not composed of independent lumpy ices but a whole continuous ice cover on the whole surface. This kind of ice is seldom selected as a research object, and the adhesive forces of the ice under different conditions, such as excitation frequency and the size of piezoelectric ceramic, are seldom measured. In our previous research, it was found that the thickness of the ice covering the whole aluminum alloy plate surface decreases under ultrasonic vibration, which has an anti-icing effect, but the ice is not removed at all [21]. Therefore, the adhesive force of the ice and the distributing state of shear stress at the adhesive interface, excited by ultrasonic vibration, need to be researched in depth. In this paper, studies are carried out on the effect of ultrasonic variations on the surface adhesive characteristic of ice covering on aluminum alloy plates, under excitations of ultrasonic vibration, using simulation and experiment. The effect of ultrasonic vibration on the adhesive force of ice is measured and analyzed quantitatively. The location and size of an ultrasonic excitation source (PZT patch) are calculated by ANSYS. Then, the distribution law of shear stress at the interface between ice and substrate plate is simulated. In this way, the optimum distribution law is determined. Based on theoretical research findings, the adhesive force of the ice under the excitation of ultrasonic vibration is measured by a self-developed experimental system, and the adhesive shear strength of the ice is calculated by a self-established analytical model. All the research findings in this paper lay the theoretical and experimental foundations for exploring the ultrasonic deicing method in depth.
# Extending Battery Lifetime by Avoiding High SOC
## 1 Introduction  
Electrified vehicles (EVs) are today becoming more and more common. Despite the significant decreases in the cost of the battery, the battery itself is still the dominating cost in the electric propulsion system. It is thus of very high importance that the battery in the vehicle degrades as little as possible during the life of the vehicle.  
It is well known that high temperature has a negative impact on battery ageing [1; 2; 3; 4; 5]. Another factor that affects the ageing is the Depth of Discharge (DOD), where a larger DOD increases the ageing [6; 7]. High charge and discharge rates accelerate the ageing [6; 7; 8]. For the investigated test cell, current rates over 3C are considered as high and rapidly deteriorates the cell if used for longer than a few seconds.  
In vehicle applications, the discussion has been very focused on the driving range of the vehicle. A study by [PERSON] [9] on drive patterns showed that the most common trip is 30-50 km long and that the majority of trips were even shorter.  
The driving range of EVs today are from 100 km [10] and up to 499 km [11], meaning that the battery in most occasions only will be used in a small DOD before it is charged again. It is therefore of high interest to investigate the effects this user pattern can have on the vehicle battery.  
Only few studies concerning small DODs can be found in the literature, and even fewer are considering the ageing effect of the placement of the DOD in the State of Charge (SOC) window. The ageing as a function of SOC levels for LiFePO4 (LFP) battery cells was investigated in [1; 2] and it was found that high and low SOC levels are substantially degrading the cell. Similar results were presented for an NMC cell [3]. However, extensive testing on large commercial cells in small DODs at different SOC levels have not been documented to this date.  
The purpose of this article is to investigate the ageing dependency on SOC, using an extensive test series on large commercial cells as foundation. An empirical model is used to quantify this impact insynthetic drive cycles as well as more realistic drive cycles and user cases. Furthermore, a final objective is to establish the impact of calendar ageing, i.e., to plan the charging so that a more favourable SOC level can be used for the battery while the vehicle is parked.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 4 Validation of the Solution
### Functional Validation Using Real Devices  
For the functional validation, real devices are used to measure the temperature in different places of a particular house using multiple sensors (two Raspberry Pis model 3B with a BME280 sensor attached and one Raspberry Pi Zero with a DHT22 sensor attached). In the background, some random temperatures are also sent from JMeter load test application. When testing the slice with the IoT gateway deployed in the Raspberry Pi Kubernetes cluster (slice 1), all these devices are connected to this IoT gateway, and when testing the other slice (slice 2), all of them are connected to the IoT gateway deployed in Google Kubernetes Engine (GKE) [89].  
Figure 6 shows the measured values stored in the InfluxDB instance deployed together with the Edge X gateway in the Raspberry Pi cluster (slice 1) and Figure 7 shows the ones obtained from the GKE cluster (slice 2), both for around two minutes. As it can be observed, temperature sent from real sensors is steady, while the temperature received from JMeter has fluctuations. There is no difference in the behavior when deploying the gateway in one location or the other, except for the measured values that were obtained at different times.  
The measured values obtained in the InfluxDB instance deployed with the Mainflux server in the GKE cluster (slice 1) are not included since the information measured is exactly the same as the one measured in the IoT gateway (Figures 6 and 7).  
Figure 6: Temperature generated by BME280 and DHT22 sensors together with emulated background traffic measured at the IoT gateway deployed in Raspberry Pi Kubernetes cluster.
## 4 Introducing Samza
### Samza Streams, Partitions, and Jobs  
Samza processes the data as organized in _streams_. In particular, a Samza stream is a collection of immutable messages, typically organized by categories, where each message belonging to the stream is modeled as a <key, value> pair. A stream can have multiple producers writing its data and as many consumers reading its data. In particular, the data can come from infinite sources (e.g., Kafka topics) or from finite sources (e.g., a set of HDFS files). A stream is then separated into multiple _partitions_ to make its manipulation more scalable: Each partition is, therefore, an ordered sequence of records. When a message is written to the stream, it is stored in one of its partitions and identified by a specific offset. Samza allows to adopt various systems to realize stream abstraction; for example, a topic coming from Kafka or even a sequence of updates to the tables of a database. A _job_ is code that consumes a set of input streams. To make the system throughput as scalable as possible, jobs are separated into smaller units called _tasks_.  
Samza provides scalability to applications by logically dividing them into multiple tasks: A task is the logical unit of parallelism of the application. Each task manipulates data from one or more partitions for each input stream. Since there is no specific order between partitions, tasks can operate on them independently of each other. In the event that a task is running on a failed machine, it is restarted on another machine, always processing the same partition. Since there is no order among the messages belonging to various partitions, the tasks can process entirely independent of each other without having to share state.  
Just as a task is the logical unit of parallelism of the application, a _container_ is its physical unit: Each worker can be thought of as a JVM that executes one or more tasks. An application that makes use of Samza typically has multiple containers distributed among the various hosts. Each application also includes a Coordinator that manages the assignment of tasks between the various containers. The coordinator also monitors the life cycle of each container and redistributes the tasks among the remaining ones in case of failure. The coordinator itself is pluggable, making Samza able to support various deployment solutions.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 2 Data and Methods
### Data Records  
This experiment includes seven radar records collected at different look geometries and strongly varying winds (Figure 2a). During the initial period, the wind was westerly, with speed varying from 10 to 20 m s\({}^{-1}\). This was followed by an abrupt switch to strong offshore northerly wind with speed increasing up to 33 m s\({}^{-1}\). Currents at 10 m depth were below 0.2 m s\({}^{-1}\) during the entire experiment (Figure 2c).  
During the initial measurement period starting at 04:00 UTC on 24 October 2018 (Records #1-4, Figure 2a), the wind wave spectrum had a single peak corresponding to predominantly westerly winds. The significant wave height (SWH) varied from 0.8 to 1 m with a rather stable wave peak frequency of about 0.2 Hz. At these conditions, four 30 min radar records were collected at low incidence angle, \(\theta=20^{\circ}\), and different azimuths.  
After 23:00 UTC on 24 October 2018, the wind direction started turning northerly with wind speed increasing to 30 m s\({}^{-1}\). This wind transition period was fully captured by the 9.5-h long Record #5, also collected at \(\theta=20^{\circ}\). As a result of wind direction change, the surface wave spectrum became bi-modal, with a residual gradually decaying 0.2-Hz wave system propagating from the west and a 0.5-Hz peak corresponding to \(\approx\)1-km fetch-limited wind waves propagating from the north (Figure 2b).  
On the morning of 25 October 2018, the radar look azimuth was changed to upwind (Record #6). Next (Record #7), the incidence angle was increased to \(\theta=45^{\circ}\) (also upwind) to capture the radar backscattering during the northerly wind gradual decay period.