esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Urban Heat Island in China and Its Influences on Building Energy Consumption
## 4 Conclusions  
The characteristics of UHII in 197 main Chinese cities across five building climate zones and their influences on building energy consumption were analyzed.  
It was found that the annual mean UHII in most cities is around 0.5-1 \({}^{\circ}\)C. Based on data from 197 cities, the median daily mean UHII can best represent the annual mean UHII (R\({}^{2}\) = 0.97). The synthetic daily UHII showed a relatively consistent \"U\" shape diurnal profile in typical cities. The synthetic daily UHII decreased first around 6:00 and then began to increase after 16:00. The UHII also presents seasonal variations. The seasonal average UHII is lowest in summer and highest in winter.  
The UHII affects the building energy consumption significantly with different effects across different building climate zones. As the heating demand dominates in SCR and CR cities, most cities in SCR (96.7%) and CR (60.8%) favor the UHI effect, i.e., the presence of UHI reduces the building energy consumption. However, in HSCWR, HSWWR, and MR, the situation inverses due to the importance of cooling load in these three building climate zones. The presence of UHI increases the annual building energy consumption by 69.4%, 80%, and 63.6% in HSCWR, HSWWR, and MR, respectively. Therefore, the background  
Figure 6: Cooling and heating load-related coal consumption simulated with **(a)** urban air temperature and **(b)** rural air temperature. **(c)** The differences between urban coal consumption (\(CC_{u}\)) and rural coal consumption (\(CC_{r}\)) indicate the effect of UHII on coal consumption in a typical residential building. **(d)** The relative change in coal consumption due to UHII, i.e., \((CC_{u}-CC_{r})/CC_{r}\).  
climate should be taken into account when considering the UHI mitigation strategies to reduce building energy consumption.  
Data analysis, Writing and Discussing, S.W.; Data processing, Drawing figures, Modelling, Z.W.; Discussing, Editing and Reviewing, Y.Z.; Project administration, Funding acquisition, Writing, Editing and Reviewing, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Housing and Urban-Rural Development of China grant number K20210466 and The APC was funded by Zhejiang University.  
Not applicable.  
The data are included in the main text.  
The research project of the Ministry of Housing and Urban-Rural Development of China (K20210466) is acknowledged.  
The authors declare no conflict of interest.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Transceiver Design
#### 3.5.3 GMSK Data Rate Selection  
The resampler's properties are responsible for the generated data rate together with the system sampling rate and the number of samples per symbol. Its interpolation and decimation factors control the generated data rate.  
In our design, a sample rate of 500,000 samples/second and 10 samples per symbol for the GMSK modulation are used. The transmitter's resampler parameters are set to a decimation factor of 24 and an interpolation factor of 125, while the receiver's resampler parameters are set to a decimation (\"D\") factor of 125 and an interpolation (\"P\") factor of 24 in order to generate a 9600-bps signal.  
Equation (5) shows the relationship between these factors:  
\[Data\ Rate=P/D\times 1/sps\times Samp\_Rate \tag{5}\]  
where  
* _P_: interpolation factor;
* _D_: decimation factor;
* _sps_: samples per symbol;
* _Samp_Rate_: system sample rate;
## 4 Results and Discussion
### Dataset Description  
The proposed Bi-GRU model's efficacy was validated on the Rossmann and Walmart datasets. The Walmart dataset was released in 2014, and it contains the weekly sales data of 77 departments and 45 stores. The Walmart dataset includes features like unemployment (prevailing unemployment rate), temperature (temperature on a sale day), date (week of sales), CPI (prevailing CPI), fuel prices (fuel cost in a region), store (store number), weekly_sales (sales of a given store), and holiday_flag (whether the week is a non-holiday week, holiday week, or special holiday week). This historical dataset covers sales from5 February 2010 to 1 November 2012, and it is available from [[https://www.kaggle.com/datasets/yasserh/walmart-dataset](https://www.kaggle.com/datasets/yasserh/walmart-dataset)]([https://www.kaggle.com/datasets/yasserh/walmart-dataset](https://www.kaggle.com/datasets/yasserh/walmart-dataset)) (accessed on 12 December 2023).  
On the other hand, the Rossmann dataset was released in 2015, and it has the sales data of 1115 stores. This dataset includes seven features: day of the week, school holiday, state holiday, open promo, customers, sales, and store number. The Rossmann dataset is available from [[https://www.kaggle.com/competitions/rossmann-store-sales/data](https://www.kaggle.com/competitions/rossmann-store-sales/data)]([https://www.kaggle.com/competitions/rossmann-store-sales/data](https://www.kaggle.com/competitions/rossmann-store-sales/data)) (accessed on 12 December 2023). The data statistics of Rossmann and Walmart datasets are mentioned in Table 1.
## 3 Results
### Antiflatoxigenic Effect of Non-AFB1-Producing A. flavus ATCC 9643 on ATCC 22546  
After the co-cultivation of ATCC 9643 with ATCC 22546, AFB1 and AFB2 production was measured using HPLC combined with fluorescence detection. Both AFB1 and AFB2 production were significantly reduced (Figure 6a,b). The production of AFB1 showed 84.1% and 94.6% reductions at ATCC 22546/ATCC 9643 ratios of 1:1 and 1:3, respectively. Also, the production of AFB2 showed 94.3% and 98.9% reductions at ratios of 1:1 and 1:3, respectively. Furthermore, via ATCC 96045-ATCC 22546 co-culturation, ATCC 96045's suppressive effect on AFB1 and AFB2 production was analyzed (Figure 6c,d). Since ATCC 96045 (AF36)'s suppressive effect on ATCC 22546 and aflatoxin production is well studied, we determined that aflatoxin production was increasing the ATCC 22546 ratio to ATCC 96045. After co-cultivation, production of AFB1 was suppressed 100% at both 1:1 and 1:2 ratios (Figure 6c). Our findings suggest that ATCC 9643-ATCC 22546 co-cultivation is a promising means of mitigating AFB1 and AFB2 production.  
Figure 6: Aflatoxin B1 and B2 production by _Aspergillus flavus_ ATCC 22546 after co-cultivation with the non-aflatoxin-producing strains _A. flavus_ ATCC 9643 and _A. flavus_ ATCC 96045. (**a,b**) ATCC 22546: ATCC 9643; (**c,d**) ATCC 22546: ATCC 96045. The experiment was performed in triplicate. Data analysis was conducted using one-way ANOVA, followed by post hoc Tukey’s test. Lowercase letters denote significant differences among groups: a > b (\(p<0.05\)). n.s: non-significant.
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 4 Data-Centric Communications in UAS Scenarios
### Support of Real-Time Conversational Communications  
In the use cases under consideration, the operators of the ground stations (e.g., a firefighter working on fire extinction activities in a given area) may require to establish real-time conversational communications with other ground stations or with an external control center (e.g., to maintain a voice/video call or to exchange images, warnings or other type of messages).  
In TCP/IP, the establishment of real-time conversational communications typically requires the use of a signaling protocol, which may require a set of specific-purpose functional entities for its appropriate operation, as well as a transport protocol to encapsulate the real-time content and exchange it among the involved parties. As an example, the Session Initiation Protocol (SIP) [22] is a well-known and largely adopted signaling protocol for multimedia communications, which can coexist with different transport protocols for data encapsulation (e.g., UDP, TCP, and the Real-time Transport Protocol, RTP [29]). SIP operates with a defined set of functional elements, including SIP registrars and proxies. These entities allow supporting end-user mobility and determining the actual network location of a user and its availability to accept the establishment of a multimedia communication. Multiparty conferences, i.e., real-time conversational communications involving multiple participants, are also supported in SIP by integrating a set of intermediate elements (a conference focus and one or several content mixers) into the signaling and data communications [30]. Consequently, supporting real-time conversational applications over IP networks typically requires the introduction of additional infrastructure components and support services, which should also be deployed over the aerial and/or ground network, thus increasing both the complexity of the deployment and the associated overhead.  
Given the high traffic volume of real-time conversational data (e.g., VoIP data flows), these are typically exchanged offline with regard to DDS: DDS is used to publish all the needed settings and metadata associated with the targeted multimedia session, e.g., including the RTP endpoints to be used for offline streaming transfer.
## 3 Results
### Effects of \(T_{spring}\) Anomalies on Current and Post-season GPP
Results between GOSIF-GPP, NIRv-GPP and FluxSat-GPP were similar, but differed on certain biomes. In general, the forest biomass had a smaller statistical spread around negative correlations, meaning that these negative effects were specific to trees rather than herbaceous vegetation. The other biomes had a wider statistical spread, stretching across both positive and negative impacts, meaning that the overall impact is more neutral. FluxSat-GPP, on average, showed more positive correlations, particularly in summer. ABS and AG stood out from the rest with overall positive impacts, implying that drought propagation from increased \(T_{spring}\) is not promoted to the same extent here, as seen in biomes located further south. Figure 3: Latitudinal distribution of Pearson’s correlations (\(p<0.05\)) between \(T_{spring}\) anomaly to summer (green) and autumn (orange) GPP anomaly, for: (**a**) GOSIF-GPP, (**b**) NIRv-GPP, and (**c**) FluxSat-GPP. The shaded area represents one standard deviation for the pixels with each latitude intervals (0.05°). The solid line represents the mean values for the pixels with each latitude intervals. All the values have been processed using the moving average of every five consecutive latitudes. Transition Patterns for Legacy Effects of \(T_{spring}\) on GPP\({}_{summer}\) and GPP\({}_{autumn}\)  
How legacy effects of \(T_{spring}\) propagated through summer and autumn was investigated based on spatial distribution of transition patterns and their aggregation for different biomes (Figure 5). Insignificant correlations are denoted by /, positive by +, and negative by \(-\). The majority of legacy effects was found either in summer or autumn, meaning that the legacy effects only propagated one season. GOSIF-GPP and NIRv-GPP were dominated by a negative impact in summer (\(-\)/), whereas FluxSat-GPP revealed more pixels of a positive impact in summer (+/) and a negative impact in autumn (\(/-\)). This means that the timing of water-stress induced by the pre-season stimulated vegetation growth may vary between the GPP data sets. Separating the transition patterns based on biomes further reveals the similarity between GOSIF-GPP and NIRv-GPP, that is, the northern bi  
Figure 5: Transition patterns of legacy effects from \(T_{spring}\) anomalies on \(GPP_{summer}\) and \(GPP_{autumn}\) for each biome, for: (**a**) GOSIF-GPP, (**b**) NIRv-GPP, and (**c**) FluxSat-GPP. The labels should be interpreted as follows: \(+-\) means there was a positive significant impact seen in summer and a negative in autumn, \(/-\) means there was an insignificant correlation seen in summer and a significant negative correlation in autumn). There were a total of eight different transition patterns excluding //, which means no significant legacy for both summer and autumn. clear positive impact during summer. However, FluxSat-GPP showed positive impact in wetlands (PW), savannas (SV) and DNF during summer.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 4 Theoretical Analysis
### Estimation of Coverage Holes  
Deploying the sensors randomly will lead to coverage holes, we introduce Delaunay to estimate the coverage holes. \(C_{0}\) is the circumcenter of the \(\Delta S_{1}S_{2}S_{3}\) and the distance \(d(S_{i},C_{0})\) is circumradius \(R\) as shown in Figure 4.  
**Theorem 1**.: _If \(R_{s}>R\) then there does not exist coverage hole between the three sensors, if the \(\Delta S_{1}S_{2}S_{3}\) is an acute triangle and \(R_{s}<R\) then there must exist coverage holes between three sensors [22]._  
Proof of Theorem 1.: We know \(C_{0}\) is the circumcenter of \(S_{i}(1,2,3)\), according to geometry we can get conclusion easily that the distance between \(C_{0}\) and \(S_{i}\) is equal, so if the \(C_{0}\) is covered by sensor \(S_{1}\), then \(C_{0}\) must be covered by \(S_{1}^{\prime}s\) neighbour nodes \(S_{2}\) and \(S_{3}\) as as demonstrated in Figure 5b. It is apparently that the triangle area constructed by \(S_{1}\) and its two neighbours do not exist coverage holes.  
If the \(C_{0}\) is not covered by node S just as shown in Figure 5a, then there must exist coverage holes, we should estimated the uncovered shadow area and worked out how many additional mobile sensors  
Figure 4: Nodes construct triangle.  
Figure 3: DT Repaired.  
\(N_{m}\) should healed the coverage hole. Assuming that each sensor's location is known, then \(\theta_{(1,2,3)}\), \(S_{\Delta s_{1}s_{2}s_{3}}\) and the sector area \(S_{\theta_{i}}\) can be calculated as follow:  
\[S_{\theta_{i}}=\sum_{i=1}^{3}\frac{\theta_{i}}{2}R_{s}^{2} \tag{1}\]  
the area of \(S_{\Delta s_{1}s_{2}s_{3}}\):  
\[S_{\Delta s_{1}s_{2}s_{3}}=\frac{1}{2}d(s_{1},s_{2})d(s_{1},s_{3})\sin(\theta_ {1}) \tag{2}\]  
According to the (1) and (2), \(S_{uncovered}\) can be derived as follow:  
\[S_{uncovered}=S_{\Delta s_{1}s_{2}s_{3}}-S_{\theta_{i}} \tag{3}\]  
From (1)-(3) we can estimate the number of additional mobile sensors \(N_{m}\) which is needed to heal the coverage holes, define \(\rho=\frac{S_{uncovered}}{\pi R_{s}^{2}}\) then  
\[\rho= \frac{S_{\Delta s_{1}s_{2}s_{3}}-S_{\theta_{i}}}{\pi R_{s}^{2}} \tag{4}\] \[= \frac{\frac{1}{2}d(s_{1},s_{2})d(s_{1},s_{3})\sin(\theta_{1})- \sum_{i=1}^{3}\frac{\theta_{i}}{2}R_{s}^{2}}{\pi R_{s}^{2}} \tag{5}\]  
Input parameter \(\mu\) (\(0<\mu<1\)) to round the number of assisted sensor.  
\[N_{m}=\begin{cases}[\rho]+1,&\rho-[\rho]\geq\mu\\,&\text{else}\\ \end{cases} \tag{6}\]
# S-Band Doppler Wave Radar System
## 4 Results Comparison and Analysis  
### Experiment Introduction  
An experiment using MORSE was conducted from 19 March 2015 to 30 March 2015, in Zhelang, Guangdong. The location and the orientation of antennas are demonstrated in Figure 8. A cold front occurred during the experiment, which resulted in strong winds and rain. The wind scale began to increase on March 19 and reached its maximum on approximately March 25. Afterwards, the wind gradually ceased and the temperature rose. Rain fell on March 22, March 23 and March 26. The azimuth angle covered by antennas is 0\({}^{\circ}\) to 180\({}^{\circ}\). The installation position of the antennas is 20 m above the sea level. The No.1 and No.6 antennas were partly sheltered; therefore, the data acquisition rate was low. The data from 18:00 March 23 to 08:00 March 24 were missing because of a power failure caused by exterior reasons.  
The radar waveform parameters are listed as follows: operating frequency is 2.85 GHz; frequency sweep period is 4112 us; frequency sweep bandwidth is 20 MHz; number of echo sample points is 512; Doppler sample period is 4112 us, range resolution is 7.5 m. The data processing parameters are listed as follows: number of first FFT(FT1) points is 512; number of second FFT(FT2) points is 128; water depth used in algorithm is 15 m.  
Figure 7: Data processing approach of MORSE, from echoes of every antenna to the final results.  
A buoy mounting Waverider MKIII was deployed 450 m from the MORSE system. Waverider MKIII is developed by Datewell in Netherlands. The specifics of the Waverider MKIII are as follows: wave height range 0-20 m with a resolution of 0.01 m, wave period 1.6-30 s, wave direction 0-360\({}^{\circ}\) with a resolution of 1.4\({}^{\circ}\). We compared the data of the range cell near the buoy acquired by MORSE with the data acquired by buoy. The buoy generated a dataset every half hour, and these data represented the average of the data in the previous 20 min before the dataset was generated. However, MORSE generated a dataset every 3 min. Hence, we averaged the latter six of ten datasets acquired by MORSE every 30 min to get a similar time resolution with the buoys.
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
###### Abstract  
The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3 rd Generation Partnership Project (3 GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors' knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.  
MCS; reinforcement learning; Q-learning Article
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 4 Algorithm of OTPP
### Deployment of Beacon Points in OTPP
Therefore, two beacon points should be added to completely cover the entirety of the basic square, as shown in Figure 7a. **Lemma 6**. : _When a basic square contains zero- and one-covered areas, a particular layout will enable six beacon points to completely cover the entirety of the basic square._  
Proof. : Lemma 4 indicates that the addition of three beacon points to each sub-square will cover the entirety of a basic square. However, this placement method does not consider the area that can be covered by the beacon point and only places three beacon points in each sub-square. Therefore, if we consider \(CR\geq\sqrt{(5/2)}d\), then, placing a beacon point in each of _m\({}_{4}\)_, _m\({}_{5}\)_, and _m\({}_{6}\)_ will cover _reg\({}_{1}\)_, _reg\({}_{3}\)_, _A\({}_{1}\)_, _A\({}_{2}\)_, _A\({}_{7}\)_, and _A\({}_{8}\)_. In addition, the unknown nodes in these regions will receive at least three beacon packets. Similarly, adding a beacon point to each of _m\({}_{1}\)-_m\({}_{3}\)_ will cover _reg\({}_{2}\)_, _reg\({}_{4}\)_, and _A\({}_{3}\)-_A\({}_{6}\)_; moreover, the unknown nodes in these regions will also receive at least three beacon packets. Therefore, the addition of six beacon points will enable complete coverage over the entirety of the basic square, as shown in Figure 7b. We can further calculate the total number of beacon points required for OTPP, \(N_{BP}\), as described by Lemma 7:  
**Lemma 7**. : _In OTPP, the entire basic square contains only three-covered areas; the total number of beacon points, \(N_{BP}\), required is five. The basic square contains two-covered areas; the total number of beacon points required issix; the basic square contains zero-covered and one-covered areas, and the total number of beacon points required is ten._  
\[N_{BP}=\left\{ \begin{array}{ll}5,&\text{when three-covered}\\ 6,&\text{when two-covered}\\ 10,&\text{when one-covered and zero-covered}\end{array} \right. \tag{1}\]  
Proof. : In an obstacle-free environment, the total number of beacon points, \(N_{BP}\), required for OTPP is five (\(c_{0}\), \(c_{1}\), \(c_{2}\), \(c_{3}\), \(c_{4}\)). When the entire basic square contains two-covered areas, which require adding two beacon points, the total maximum number of beacon points required is six (see Figure 7a). When the basic square contains zero-covered and one-covered areas, which require adding six beacon points, the total maximum number of beacon points required is ten (see Figure 7b).
# Energy Regeneration Hydraulic System via a Relief Valve with Energy Regeneration Unit
###### Abstract  
Relief valves are widely used in industrial machinery. Due to the outlet of the relief valve being connected to the tank, the pressure drop of the relief valve is frequently equal to the inlet pressure. Accordingly, the energy loss of the relief valve is very high in some cases and this will worsen with an increase in the rated pressure of the hydraulic system. In order to overcome the disadvantage of overflow energy loss in a relief valve, a hydraulic energy regeneration unit (HERU) is connected to the outlet of the relief valve to decrease the pressure drop between the inlet and outlet of the relief valve. The overflow loss, which is characterized by the pressure drop, can be reduced accordingly. The approach is to convert the overflow energy loss in hydraulic form and allow for release when needed. The configuration and working principle of the relief valve with HERU is introduced in this present study. The mathematical model is established to obtain the factors influencing the stability of the relief valve. The working pressure of the hydraulic accumulator (HA) is explored. Furthermore, the control process of the operating state of the HA is scheduled to decide whether to regenerate the energy via the HERU. The software AMESim is utilized to analyze the performance and characteristics of the relief valve with HERU. Following this, the test rig is built and used to verify the effectiveness of the proposed relief valve with HERU. The experimental results show that the relief valve with the HERU connected to its outlet can still achieve better pressure-regulating characteristics. The energy regeneration efficiency saved by the HA is up to 83.6%, with a higher pre-charge pressure of the HA. This indicates that the proposed structure of the relief valve with HERU can achieve a better performance and higher regeneration efficiency.  
pilot relief valve; energy regeneration unit; pressure fluctuation; regeneration efficiency Article