esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Coverage Flight Path Planning Algorithm
### Waypoints Graph Generation by Connecting Footprints' Sweeps
However, the BF/Zamboni flight pattern can lead to the excessive path overlapping in the complex scenarios. The proposed approach simplifies the turns to the extent possible for the easier adoption using both types of the UAVs. The proposed approach generates a sparse WG with minimum nodes and edges to reduce the computational complexity of pathfinding process. The complete pseudo-code used to generate a waypoint graph from AOI is given in Algorithm 1. In Algorithm 1, the AOI \(Q\) containing \(N\) number of obstacles, footprint sweeps set \(R\), footprint sweeps visiting sequence set \(S\), and footprint connection gap \(g\) are provided as an input. Waypoint graph \(\Gamma\), where (\(\Gamma=\{W,E\}\)) is obtained as an output. Line 2 implements the finding of appropriate footprint sweep to be connected with footprint sweep \(F_{i}\) out of \(N-1\) candidates based on optimized visiting sequence and sweeps connection gap. Line 3 implements the identification of the relevant endpoint's pairs through which two sweeps \(F_{i}\) and \(F_{j}\) will be connected with each other. Line 4 performs the check for the obstacle(s) existence between two sweeps to form the collision-free connection \(C_{i}\). Lines 5-6 implement the connection formation in the presence of obstacle(s) between two sweeps while avoiding the obstacle with low cost option. Line 8 implements the connection formation if no obstacles exist between two sweeps. Furthermore, the same process continues until the complete waypoints is constructed. Finally, waypoint graph \(\Gamma\) of footprints' sweeps connection is returned as an output (line 11). Figure 7: Pictorial overview of the waypoint graph and Back and forth/Zamboni flight pattern. ```
Input :(1) AOI \(Q\) containing \(N\) number of obstacles, where \(N=\{o_{1},o_{2},o_{3},\ldots,o_{n}\}\). (2) Set \(R\) of footprints' sweeps, where \(R=\{F_{1},F_{2},\ldots,F_{n}\}\) and \(F_{i}=(a_{i},b_{i})\). (3) Sweeps visiting sequence set \(S\), where \(S=\{(F_{1},n_{1}),(F_{2},n_{2}),\ldots,(F_{n},n_{n})\}\). (4) Sweeps connection gap (\(g\)). Output :Way point graph \(\Gamma\) Procedure: for each footprint sweep \(F_{i}\), where \(F_{i}=F_{1}\) to \(F_{n}\in R\) do  Find next footprint sweep \(F_{j}\) considering the \(g\) value and optimized visiting sequence. Identify the relevant endpoint pair using \((a_{i},a_{j},b_{i},b_{j})\) for connection \(C_{i}\) if INTERSECTS(\(C_{i}\),\(o_{i}\)) then  Evaluate and select the appropriate obstacle \(o_{i}\) avoidance option using Equation (7). Connect the footprints' sweeps \(F_{i}\) and \(F_{j}\) with the relevant endpoints. else  Connect the footprints' sweeps \(F_{i}\) and \(F_{j}\) with the relevant endpoints. End if Endfor return\(\Gamma\)
```  
**Algorithm 1**Waypoint graph generation from the AOI located in a 3D map.
## 4 Results and Discussion  
According to the proposed methodology described in Section 2, the case study is analyzed from a multidimensional perspective with the goals of reducing the intense dependence on fossil fuels, increasing the integration of solar solutions and preserving the aquifer to avoid future lower phreatic levels that would require more energy resources and thus relevant economic efforts. Furthermore, PV power plants connected to the grid can give farmers additional benefits through net-metering scenarios and annual energy surpluses.
## 4 Regional Control Strategies Analysis
### Control Strategies for the DQF  
The peak time for cometercy-sweeping of Qingming Festival is 7:00-8:00 during the morning, and the traffic flows from the east to west on the urban network. The model calibration results indicate that the transfer ratio from subregion 3 to subregion 2 in 7:00-8:00 is \(P_{32}=0.6\), and is \(P_{32}=0.18\) during other periods. That is to say, the amount of traffic transfer from subregion 3 to subregion 2 during peak hours is much higher than that in other periods, causing that subregion 2 evolves to congestion. Therefore, the network density should be reduced by decreasing the inflow ratio or increasing the outflow ratio of subregion 2.  
On the day of Qingming Festival, residents went to cemeteries from the urban area. Most of the demand for entering sub-region 2 is rigid, so it is difficult to reduce the total inflow to sub-region 2. In practical control, residents can be reminded to travel early or delay their travel time. By extending the control time, the total demand can be dispersed over time, avoiding concentrated travel in a short period to reduce the inflow ratio to sub-region 2. On the DQF, the control strategies for subregion 2 are designed as follows: (1) Strategy 1: decreasing \(P_{12}\) or \(P_{2A}\), which is to decrease the transfer ratio from subregion 1 to subregion 2 or decrease the external inflow ratio. (2) Strategy 2: increasing \(P_{21}\) or \(P_{2E}\), which is to increase the transfer ratio from subregion 2 to subregion 1 or increase the outflow ratio. The two control strategies limit vehicles entering subregion 2 and guide internal traffic flow away from the subregion, thus reducing the traffic density in subregion 2. The simulations under the two strategies are compared to analyze the impacts of different strategies on the network state.  
Figure 12: The network evolution on the day of Qingming Festival: (**a**) subregions 1 and 2, (**b**) subregions 2 and 3. The blue line is the simulation result of the model, and the arrow indicates the direction of system evolution. The blue scatters are the empirical data. Points A and B represent the initial state and final state of the simulation, respectively.
## 2 Material and Methods
### ORC Plant Modeling  
The hot stream leaving the TES tank with the temperature (\(T_{s,in}\)) feeds an ORC module through a heat recovery system, which is a heat exchanger and is made up of three parts, i.e., the economizer, the evaporator, and the superheater. In the end, the organic medium exits this system at the state of superheated vapor (state 4). The heat input to the \(ORC\) (\(Q_{in}\)) can be defined below:  
\[Q_{ORC}=\dot{m}_{ORC}\cdot(h_{4}-h_{3})=\dot{m}_{s}\cdot c_{p,oil}\cdot(T_{s,in }-T_{s,out}) \tag{14}\]  
The most commonly used heat exchanger type in ORC systems is a shell-and-tube heat exchanger [31]. For the examined configuration, this type is selected for the ORC heat recovery system and the recuperator. The pinch point in the entrance of the evaporator is considered at 10 K [30], while the superheating is assumed at 5 K [28]. Additionally, the organic fluid high pressure (\(P_{high}\)) is considered as a percentage of its critical pressure (\(P_{crit}\)). The fraction of the high pressure to the critical pressure is called parameter (\(\alpha\)) and is described by the following equation [30]:  
\[\alpha=\frac{P_{high}}{P_{crit}} \tag{15}\]  
The electricity output of the expander minus the electricity consumed by the pump defines the \(ORC\) electricity production, which is considered as below [30]:  
\[P_{el,ORC}=\eta_{mg}\cdot\dot{m}_{ORC}\cdot(h_{4}-h_{5})-\frac{\dot{m}_{ORC} \cdot(h_{2}-h_{1})}{\eta_{motor}} \tag{16}\]  
The isentropic efficiency of the feeding pump (\(\eta_{is,pump}\)) is regarded to be equal to 0.8, which is a typical value for small-scale solar ORC applications based on Ref. [32]. Moreover, a screw expander is considered to be installed, as a proper choice for applications greater than 10 kWel[33]. The expander isentropic efficiency is considered at 0.85, a value assumed according to the literature [28; 30]. Additionally, an air-cooled condenser is incorporated, where a temperature difference of 10 K is assumed. For the condenser, the selected heat exchanger type is the fin-and-tube heat exchanger, as a widely used configuration for air-cooled condensers in the literature [34]. Finally, the ORC thermal efficiency is defined as:  
\[\eta_{ORC}=\frac{P_{el,ORC}}{Q_{ORC}} \tag{17}\]  
Taking into account the expected temperature levels of the heat source, MDM is selected to be used as a widely-used and efficient working medium, according to the literature [14; 28; 30]. All the parameters used for the ORC modeling can be found in Table 1[28; 30; 32].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Evaporator pinch point (\(PP\)) & 10 K \\ Recuperator temperature difference (\(\Delta T_{rec}\)) & 5 K \\ Condenser temperature difference & 10 K \\ Superheating (\(\Delta T_{sh}\)) & 5 K \\ Pump isentropic efficiency (\(\eta_{is,pump}\)) & 0.8 \\ Pump motor efficiency (\(\eta_{motor}\)) & 0.8 \\ Expander isentropic efficiency (\(\eta_{is,exp}\)) & 0.85 \\ Electromechanical efficiency (\(\eta_{mg}\)) & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the ORC.
## 3 Materials and Methods
### DCP Extension for Hardware Coupling  
In this Section, the additional functionalities and differences for the DCP implementation for hardware coupling are explained in more detail. In general, the implementation is carried out according to the specification, which provides information about the slave state-machine [10]. The schematic structure of the slave state-machine is shown in Figure 6. A proposal for the master's state-machine implementation is described in the Ref. [13].  
Figure 6: Standard DCP slave state machine implementation, based on the Ref. [10].  
Synchronization and Timing  
DCP does not include mechanisms for time synchronization. Regarding synchronization in DCP-coupled test benches, examples from the literature have been given in the Ref. [7], and a recommendation in the DCP specification refers to the Ref. [58]. The challenge within such existing protocol for clock synchronization is the integration in existing systems. Alternatively, the possibility to extend the real-time control unit with the synchronization protocol must be given. For hardware test benches, black box control units are often used. Due to security measures, full access is not granted to the software, and therefore there is no option for additional protocols. Typically, a communication protocol such as UDP/IP or TCP/IP is possible. In this research, synchronization between master and slave was achieved via UDP sequence numbers, added in the data part of a UDP frame. For this purpose, the sequence number is defined and increased in the DCP master with every DCP message to a slave. The slave stores the current number received and sends the number back to the master together with the regular data for coupling. Both the slave and the master check that the sequence numbers are not too far apart (for example, the difference should be \(<\)100). The algorithm can be implemented easily and ensures that there is no, or only a small amount of package loss caused using UDP protocol. The sequence number can also be used to check whether the connection was lost, and is therefore an option to detect failures in hardware coupling and switch to the safe state. In the case of the coupled test benches example, the latency of the communication is very low (\(\leq\)3 ms) due to the spatial proximity of the test benches and the existing VLAN without access via the Internet. If this latency is more significant, as is the case for communication via the Internet, equipping the software, in particular the controls for speed and torque, with predictive behavior is sensible. Using a so-called Smith predictor would be a possibility, as described in the Refs. [59; 60].
## 3 Analysis of Test Results
### Acoustic Emission Characteristics
#### 3.4.2 Impact Characteristics  
As shown in Figure 10 and Table 2, both the maximum impact rate and cumulative impact count decrease with increasing soaking time. For dry sandstone, the maximum impact rate is 316 counts, whereas the maximum impact rates for the other five groups with varying soaking times are 281, 242, 226, 202, and 154 counts, respectively. These rates decrease by 11.08%, 23.42%, 28.48%, 36.08%, and 51.27%, respectively, compared to the dry sandstone. The cumulative impact count for sandstone soaked for 90 days is 18,405 counts, a 56.14% decrease compared to the 41,964 counts for dry sandstone. As shown in Figure 10, the reduction in impact rate and cumulative impact count occurs in three distinct stages: the highest reduction rate occurs from 0-13 days, followed by a slower reduction rate from 13-60 days, and the slowest reduction rate from 60-90 days. Based on the microscopic changes in sandstone after soaking, it is known that water infiltration causes the internal pores of sandstone to gradually develop, enlarge, and interconnect. This weakens or even dislodges the cementation between clay particles within the rock, leading to a reduction in AE signal values in the sandstone.  
Figure 9: Quantitative curves of stress–energy rate/impact rat–cumulative energy–process time.
# GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs
## 5 Conclusions  
We demonstrate that crop yield estimation for grape berries can be obtained using smartphone cameras with fixed-focus small aperture wide angle optical systems by the DL architecture GBCNet, an adaptation of algorithms for crowd counting. Although other factors (see Equations (1) and (2)) have to be considered for an actual yield estimate, the average test error of about 5% for the berry counting model is considered valid for operational application (10%, according to the vine training system). In this study, all data were collected directly in the field and without requiring special cautions or additional constraints, such as a backing board. Notably, the average test error systematically decreases by estimating over more than three pictures from the same parcel. For the Pinot Gris, with a test set of seven images (for a total of about 1300 berries), the percentage MAE is less than 1%.  
Further research will investigate, in the same setup, the problem of estimating clusters' weight, thus considering a correcting factor for non-visible berries.  
Figure 7: Mean Average Error (MAE) (%) achieved by GBCNet in both cross validation (CV) (5 for CR1 3 for CR2) and test mode by image (I) and overall (O) for all the 8 grape varieties in the two datasets CR1, CR2. All varieties with white background belong to CR1, while Teroldego, in gray background, is CR2. Results are formally reported as mean \(\pm\) sd, where sd may be larger than mean.  
**Author Contributions:** Conceptualization, L.C. and M.C.; methodology, L.C. and M.C.; software, L.C.; validation, L.C. and M.C.; formal analysis, M.C.; investigation, L.C. and M.C.; resources, M.C.; data curation, L.C.; writing--original draft preparation, C.F. and G.J.; writing--review and editing, G.J., M.C. and C.F.; visualization, L.C.; supervision, C.F.; project administration, C.F.; funding acquisition, C.F. and G.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Research project partially funded by CAVIT s.c., Trento. L. C. is supported by \"[PERSON]\" scholarship, GARR Consortium.  
**Acknowledgments:** Authors thank [PERSON] and CAVIT s.c. for providing data and scientific support throughout all phases of the research.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Use-Case-Based Categorisation of Technologies
### Use Case Examples and Derived Requirements
#### 3.2.2 Tracking and Tracing  
Tracking and Tracing applications provide a certain connectivity between machines and towards stationary central points and/or enable traceability of personnel and equipment. The term traceability is defined as \"... the ability to trace the history, application or location of an object\" [36]. Tracking itself goes a step further. The collected data can be connected to specific machines or workers and is (decentrally) recorded and (centrally) stored. This provides a temporal and spatial information density that can yield new insights when analysed.  
Such network can be installed at points of interests such as intersections, entry/exit points, infrastructure areas, loading/dumping points, on mobile machines and infrastructure. This sort of \"network\" does not provide full connectivity or coverage, but very local connectivity that can transmit information to central units when connected to a central infrastructure at all. These can provide actual transmission of data sets or provide information towards the mobile machine and driver or towards a central control unit. Examples of use cases for this category include:  
* Transmission of locations (tracing for vehicle or central control)
* Transmission/reading of ore pick-up/drop-off when passing
* Pick-up/drop-off of collected sensed data from sensors or machines at a local scale
* Passing of sensor data in direct surroundings
* Detection of approaching vehicle or worker
* Tracing of worker and equipment towards central units (when passing)  
In conjunction with these usages, the following data must be transmitted:  
* Position or area
* Quality, quantity and distribution information of materials
* Various machine performance or environmental parameters
* Information of existence of unit/worker within certain range or area
* Collision perceptions
* Other collected datapoints  
A wireless technology must fulfil the following requirements in order to be appropriate and function effectively for the use cases mentioned above. The requirements are as varying as the use cases:  
* High (Mbit/s) or very low data throughput (Kbits/s)
* Coverage within single or precisely distributed defined connectivity area(s)
* Robust (i.e., transmission detection)
* Quick connection setup and transmission
* Robust and sufficient for safety-relevant applications, incl. compliance
* Battery lifetime of remote sender/receiver
## Materials and Methods
### Restricted Maximum Likelihood (REML) Method in Single-Locus Screening Stage
#### 2.2.2 Equation Transformation and Update Covariance  
Transform Equation (1) by left-multiplying \(\mathbf{Q^{T}}\)[32] and generate the following model  
\[\mathbf{y_{Q}}=\mathbf{F_{Q}}\mathbf{b}+\mathbf{X_{Q}}\beta+\mathbf{Q^{T}}\mathbf{u}+\mathbf{Q^{T}}\mathbf{\epsilon} \tag{3}\]  
where \(\mathbf{y_{Q}}=\mathbf{Q^{T}}\mathbf{y}\), \(\mathbf{F_{Q}}=\mathbf{Q^{T}}\mathbf{F}\), and \(\mathbf{X_{Q}}=\mathbf{Q^{T}}\mathbf{X}\). After transformation, the covariance of \(y\) becomes  
\[\begin{split} Cov(\mathbf{y_{Q}})=&\mathbf{X_{Q}}\mathbf{X_{Q} ^{T}}^{2}+\mathbf{Q^{T}}\mathbf{\Sigma_{u}}\sigma_{u}^{2}\mathbf{Q}+\mathbf{Q^{T}}\mathbf{I_{n}} \sigma_{n}^{2}\mathbf{Q}\\ =&\mathbf{X_{Q}}\mathbf{X_{Q}^{T}}^{2}+\mathbf{Q^{T}}\mathbf{Q}\mathbf{ \Lambda}\mathbf{Q^{T}}\mathbf{Q}\omega_{i}^{2}+\mathbf{Q^{T}}\mathbf{I_{n}}\mathbf{Q}\sigma_{n}^{ 2}\\ =&\mathbf{X_{Q}}\mathbf{X_{Q}^{T}}\sigma_{g}^{2}+\mathbf{ \Lambda}\sigma_{u}^{2}+\mathbf{I_{n}}\sigma_{n}^{2}\\ =&\mathbf{\left(\mathbf{X_{Q}}\mathbf{X_{Q}^{T}}\omega_{g}+ \mathbf{\Lambda}\hat{\omega}_{u}+\mathbf{I_{n}}\right)}\sigma_{n}^{2}\end{split} \tag{4}\]  
Let \(\mathbf{V_{0}}=\mathbf{\Lambda}\hat{\omega}_{u}+\mathbf{I_{n}}\) and \(\mathbf{V}=\mathbf{X_{Q}}\mathbf{X_{Q}^{T}}\omega_{g}+\mathbf{V_{0}}\); clearly, diagonal matrix \(\mathbf{V_{0}}\) is estimated. To determine whether a marker has an effect, hypothesis testing needs to be conducted. To estimate the marker effect \(\beta\), its variance ratio \(\omega_{g}\) needs to be obtained firstly, so that the estimation of each \(\omega_{g}\) is the most interesting issue for each corresponding marker.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 5 Multi-Well System Connectivity Analysis
### Multi-Injector-One-Producer  
In order to achieve a better displacement effect and expand the sweep area during waterflood development, the well configuration adjustment between injectors and producers is often in high demand. Therefore, a four-injector-one-producer model was set up, in which the same five-point well pattern was applied with a central well converted into the production well.  
Figure 19 shows the inter-well connectivity coefficients in a four-injector-one-producer system. The BHP of the four injectors remains the same throughout the run. It is observed that the inter-well connectivity coefficients in 1 January 2022-1 January 2027 are proportional to the permeability of the permeable channel, with the same pattern as that of the four-producer-one-injector system (Figure 17). However, from 1 January 2027 to 1 June  
Figure 17: Oil saturation distribution in (**a**) 4-producer–1-injector and (**b**) 4-injector–1-producer models.  
Figure 18: Time-lag coefficient in a multi-producer–one-injector model.  
2027, the inter-well connectivity coefficients are inversely proportional to the permeability of the permeable channel: Injector 1 and Injector 2 are connected to the central producer with lower permeability, but they correspond to higher inter-well connectivity coefficients. This abnormal phenomenon might be related to the configuration of the well pattern and the intangible fluid passages.  
Similarly, the time-lag coefficient also shows a reversal trend. Injector 4 has the channel of highest permeability, but its time-lag coefficient reverses to the maximum after 1 January 2027, as shown in Figure 20.  
The analysis shows that for a four-producer-one-injector system, there are more producers than injectors, and increasing injection pressure differential may not cause significant pressure changes because the pressure fluctuations are mainly concentrated  
Figure 19: Inter-well connectivity coefficients in a multi-injector–one-producer model. The arrows indicate that the connectivity coefficients of injector 3 and injector 4 are reversed, despite their relatively high permeability channels.  
Figure 20: Time-lag coefficients in a multi-injector-one-producer model.  
around the single injector, while the BHP of the producer decreases as the result of continued production. Well group configuration determines the spatial distribution of water injected, which is more likely to form a strong pressure gradient in the reservoir and alter the fluid flow path, forming a new ineffective circulation channel. This may be the reason for the low connectivity coefficient of high permeability channels in the late simulation period.