esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussion
### Counting the Number of Drilling Rigs  
To facilitate the measurement of drilling depth, the utilization of YOLOV7-GFCA is incorporated in the proposed method for drilling rig target detection. The movement trajectory of the drilling rig is captured, thereby enabling the calculation of the number of drill pipes driven by the rig and, ultimately, the determination of the drilling depth.  
YOLOV7-GFCA is employed to perform real-time detection on the working video of the drilling rig. The movement trajectory of the drilling rig is captured, and the position coordinate information of the drilling rig is stored in chronological order within a CSV file. Subsequent to this, mean filtering and normalization are applied to the position coordinate information of the drilling rig. The coordinate information of the detection frame vibration, caused by the drilling rig's movement, is filtered. The count of the maximum vertices in the waveform is used to determine the number of drill pipes. Given that the length of each drill pipe used in the coal mine is consistent, and the drilling rig executes a reciprocating motion along a designated straight line, the entry of a drill pipe is inferred. Accordingly, the drilling depth is accurately computed based on the drill pipe count. The filtering process of the drilling rig's position coordinate information is visually depicted in Figure 7. The vertical coordinate corresponds to the drilling rig's coordinate information acquired via YOLOV7-GFCA, while the abscissa denotes the real-time working video frame number of the drilling rig.  
Figure 6: YOLOV7-GFCA model detection results of drilling rigs.  
Figure 7: The process of processing the coordinate signal of the drilling rig: (**a**) the original left signal is obtained by the YOLOv7-GFCA target detection model; (**b**) coordinate signal after mean filtering and normalization processing. The red dot are the number of wave crests, which is also the number of drill pipes.  
In order to verify the application effect of the drill pipe counting method proposed in this paper in the coal mine drill pipe counting task, the manual counting method and the improved algorithm were tested using the complete drilling rig working video recorded in the fully mechanized mining face of the coal mine. See Table 3 for statistical results.  
It is shown in Table 3 that the manual counting method is easily affected by subjective factors. When the number of drill pipes is large, the accuracy may decrease with time, resulting in a low drill pipe counting accuracy, and based on the image detection method, the counting is more stable. Among them, the counting effect based on the YOLOV7-GFCA method is the best, reaching 99.8%, which is higher than the manual counting method and the original algorithm, and the drill pipe counting function can be well realized within the allowable range of error.
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 5 Conclusions  
In D2D streaming environments on 5G gNB, congestion and flow error occur primarily when the network operates large amounts of traffic flows, or the sender for DUE transmits more traffic rates than the receiver for DUE can accept. The congestion and flow delay are responsible for the packet loss and throughput, which degrade the QoS of D2D streaming.  
This paper proposes a new FS-CDA mechanism for an optimal D2D streaming service on 5G gNBs to enhance the QoS between \(DUE_{buffer}(x)\) and \(DUE_{buffer}(x)\). The proposed mechanism is based on the flow trade-off operation between \(DUE_{buffer}(x)\) and \(DUE_{buffer}(x)\). The mechanism considers the flow rates for encoding and decoding and the traffic-flow-sensing operation for underflow and overflow.  
We also considered the flow error and D2D flow control operation due to the transmission rate between \(DUE_{buffer}(x)\) and \(DUE_{buffer}(x)\), and we maintained the D2D streaming strategy and FS-CDA procedure for operating the optimal D2D streaming operation. A congestion control mapping mechanism and D2D streaming strategy was also proposed to control the traffic flows with reference to FV in the buffer cache. The simulation results indicated that the proposed mechanism outperformed the link scheduling, clustering, and rate allocation algorithms.  
This research was supported by National University Development Project at Jeonbuk National University in 2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 12: Average throughput with FV.
## 4 Experimental Evaluation
### Results  
In order to illustrate the performance of the algorithm, in the first experiment the Sybil node is static (it is added at the position 26) and it has the valid ID of a mobile node. In Figure 2(a,b), we can observe from different angles that the reputations of both the Sybil node at the position 26 and the compromised mobile node have been significantly lowered, which means that we have successfully detected and confined the attack. In Figure 2(c), we present the top view, where we can distinguish the path of the compromised mobile node due to its decreased reputation.  
In the following experiments we will gradually increase the number of compromised nodes, increasing as well the number of attacked mobile nodes. In Figure 3 we present the dependence of the average detection rate of the percentage of the malicious nodes.  
Figure 3: Detection Rate _vs._ % of Malicious Nodes.  
Figure 2: Reputation Evolution in Time and Space—Different Views.  
As we can observe, with the proposed approach we can detect the attack if up to 80% of the nodes are malicious, and completely confine it (_i.e._, achieve 100% detection rate) if up to 52% of the nodes are malicious. It is also important to mention than in all the experiments the false positive rate was 0%.  
In Figure 4 we show how the time of detection and the complete isolation of the attack depend on the total number of malicious nodes in the network. As we can observe, the attack cannot be isolated if more than 58% of the nodes are malicious, nor it can be detected if more than 80% of the nodes are malicious. On the other hand, if up to 20% of the nodes are malicious, the attack is detected and confined in the same moment, which is due to the fact that the great majority of nodes are still behaving properly and it is not complicated to distinguish the misbehaving ones. As the attack becomes more aggressive, it is harder to detect and isolate all the misbehaving nodes.
## 3 Case study: Social Housing Stock Managed by EVha
### Characterisation of the Public Residential Building Stock in COMUNITAT Valenciana Region
As an advance, most of the blocks studied lay in a N-S orientation of the dwellings, near 70%. Concerning the location of the dwelling blocks, HAPPEN takes into consideration a whole Europe climate area mapping where the Valencian Community is affected by two of them. These are W1S2 and W2S2 related to the characterisation on the type of winters and summers. The Valencian territory is divided in two sections. The coolest of them is in a very small area in NW of Valencia and Western Castellon, corresponding to W2S2 area, and characterised by near continental weather with cold winters and hot summers. The rest of the region lays on the W1S2 area with not very cold winters and hot summers: coastal Castellon province, the rest of Valencian one and the whole Alicante one. This division is one of the points on the general analysis of the intended replication too. There is no comment on the part of the dwelling surface because the usable area for every public social dwelling is 90 m\({}^{2}\) due to the Spanish legislation. Only in a few numbers of flats, the usable area may reach 120 m\({}^{2}\), which is devoted to large family cases. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SFH1**} & \multicolumn{2}{c}{**SFH2**} & \multicolumn{2}{c}{**SFH3**} & \multicolumn{2}{c}{**MFH1**} & \multicolumn{2}{c}{**MFH2**} & \multicolumn{2}{c}{**MFH3**} \\  & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** & **N-S** & **E-W** \\ \hline W1S2 & 92,065.23 & 14,469.53 & 11,996.64 & 15,532.48 & 180.00 & 0.00 & 734,134.71 & 405,714.68 & 677,794.59 & 310,200.14 & 9490.47 & 3940.10 \\ W2S2 & 25,139.37 & 409.99 & 0.00 & 1782.30 & 0.00 & 0.00 & 881.60 & 110.20 & 14,070.20 & 9965.41 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total useful area of each building typology in the public residential building stock (m\({}^{2}\)). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **\textless{}1980** & **1980–2000** & **\textgreater{}2000** \\ \hline
1 & 2 & 3 \\ \hline \hline \end{tabular} \({}^{\star}\) This is the classification for the buildings in Spain.  
\end{table} 
Table 4: Building’s classification according to the year of construction \({}^{\star}\). \begin{table}
\begin{tabular}{c c} \hline \hline  & **\textgreater{}2** & **\textgreater{}3** \\ \hline SFH & MFH \\ \hline \hline \end{tabular} \({}^{\star}\) This is the classification for the buildings in Comunitat Valenciana region are SFH1, SFH2, SFH3, MFH1, MFH2, and MFH3.  
For each group of buildings, with the same classification, orientation, and reference climate, the useful area has been calculated (Table 6).  
\end{table} 
Table 5: Building’s classification according to the number of floors.
## 3 The Proposed Method  
The flowchart of the proposed method is shown in Figure 2. In the step of parameter extraction, the polarimetric parameters of double-bounce and volume scattering power are first extracted using the modified three-component decomposition introduced in Section 2. The power of volume scattering (PV) and the proposed power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are then computed using the double-bounce and volume power. In the step of ROI extraction, the ROIs of ports are extracted by recognizing port water using the proposed polarimetric parameters. Water regions are separated from the land by a thresholding segmentation of the PV parameter first. The suspicious port water pixels are then separated from other water pixels by a thresholding segmentation of the PRDV parameter using the multi-scale thresholding segmentation method. The candidate port water regions are detected by determining all connected regions with a large area from the extracted suspicious port water pixels to obtain the ROIs of ports. Finally, the candidate ROIs are censored by extracting the strong double-bounce scattering region using the PRDV parameter. Ports are detected by examining the area ratio of the strong scattering region to the land in the extracted ROI.
###### Abstract  
High-acidity fruit wines, such as sea buckthorn wine, are valued for their nutritional benefits but often suffer from excessive tartness and limited aroma complexity, which restrict their consumer acceptance. The application of non-Saccharomyces yeasts with acid tolerance and flavor-enhancing potential offers a promising strategy to address these challenges. In this study, a highly acid-tolerant yeast strain, _Pichia kudriavzevii_ GAAS-JG-1, was isolated from a naturally fermented apricot system and systematically characterized in terms of its taxonomy, physiological properties, and fermentation potential. The experimental results demonstrated that _Pichia kudriavzevii_ GAAS-JG-1 maintained robust growth activity (OD\({}_{600}\) = 1.18 \(\pm\) 0.09) even under extremely acidic conditions (pH 2.0). Furthermore, the strain exhibited a strong tolerance to high ethanol concentrations (16%), elevated sugar levels (350 g/L), and substantial sulfur dioxide exposure (500 mg/L). Optimal growth was observed at 35 \({}^{\circ}\)C (OD\({}_{600}\) = 2.21 \(\pm\) 0.02). When co-fermented with _Saccharomyces cerevisiae_ in sea buckthorn wine, the ethyl acetate content increased significantly from 303.71 ug/L to 4453.12 ug/L, while the ethyl propionate levels rose from 5.18 ug/L to 87.75 ug/L. Notably, _Pichia kudriavzevii_ GAAS-JG-1 also produced novel flavor compounds such as methyl acetate and ethyl 3-methylhiopropionate, which were absent in the single-strain fermentation. These findings highlight the potential of _Pichia kudriavzevii_ GAAS-JG-1 as a valuable non-_Saccharomyces_ yeast resource with promising applications in the fermentation of high-acidity specialty fruit wines.  
Pichia kudriavzevii; environmental tolerance; co-fermentation; aroma compounds; sea buckthorn wine
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Parameter Criticality Assessment
#### 3.2.2 Design of Experiments
Overall, if the two driving modes are combined, the full factorial approach would require 180 simulations per iteration, whereas the proposed method performs only 24 simulations. This results in an overall reduction in the simulation count by approximately 86.7%, highlighting the substantial computational savings achieved through this approach. Based on the review of the aforementioned papers, it can be concluded that most studies do not explicitly incorporate higher-order interactions among the design factors in their analysis and instead focus primarily on first-order effects of individual variables. Furthermore, in cases where no dedicated sampling method for capturing non-linearities, such as LHS or CCD, is employed, the identification of non-linear behavior is deferred to the FEM simulations conducted prior to the surrogate model construction.
## 3 Results  
### ESF Feed Delivery Experiment  
The ESF ration feeding depended on both the speed of feed delivery and the rotation angle of the auger. The speed of feed delivery refers to the mass of feed delivered by a revolution of the motor. The rotation angle of the auger was electronically calculated to feed ratios of different sizes. In the gestation unit, the feed was transported via a feed line and chain, with longer paths resulting in increased abrasion. In order to investigate the effect of abrasion on feeding performance and the stability of the system, the speed of feed delivery was measured using rations of different sizes. The trials were conducted on a sample feed line with 60 consecutive ESFs. The tools included electronic scales (EHA28, Guangdong Xiangshan Weighing Instrument Group Co., Ltd., Zhongshan, China) and containers.  
For the speed measurement, a PDA was used to control the 60 ESFs to rotate augers sequentially, and the mass of feed delivered was measured. The error from 30 revolutions was smaller than that from one revolution, so the former was chosen for the speed measurement. The results are shown in Figure 8. The data were essentially centered between 1350 g and 1450 g, with a standard deviation of 21.60, a mean value of 1395 g, and a coefficient of variation of 1.55%. Fifty-seven of the feeders had similar delivery speeds, whereas feeder numbers 5, 31, and 59 deviated further, which could be due to variations in the auger, motor, plastic box, or even the installation environment. The data indicated that there was no obvious relationship between feed abrasion and delivery speed on a feed line, and 60 ESFs had good feeding uniformity.  
For the ration feeding, six experimental groups were established: 1.6 kg, 2.0 kg, 2.4 kg, 2.8 kg, 3.2 kg, and 3.6 kg. In each experimental group, 60 ESFs were controlled to deliver feed quantitatively, and the weight of feed actually delivered was measured separately. The results in Figure 9 show that the relative error of the feeding system consistently remained within \(\pm\)2.94% and the coefficient of variation was less than 1.84% at different expected feed weights. In terms of overall feeding performance, the feeding system was shown to have a low relative error, a low coefficient of variation, high accuracy, and strong stability, which met the requirements for the accurate control of feed allowances for sows.  
Figure 8: Weight of feed delivered by 60 consecutive ESFs with 30 revolutions of the augers.  
Figure 9: Relationship between (**a**) the relative error and the expected feed weight and (**b**) the coefficient of variation and the expected feed weight.
## 2 RF-MEMS Phase Shifter: Design Concept and Simulations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Transverse SPST** & **Longitudinal SPST** \\ \hline Frequency range, GHz & 0–40 & 0–40 \\ Actuation voltage, V & 28 & 38 \\ Isolation, dB & \(-\)30 @ 15 GHz & \(-\)30 @ 10 GHz \\ Insertion loss, dB & \(-\)0.8 @ 40 GHz & \(-\)0.12 @ 40 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main simulated radio frequency (RF) characteristics for the transverse and longitudinal Single Pole Single Three (SPST) RF-MEMS switches previously shown in Figure 2.  
Figure 2: Schematic of the transverse (a) and longitudinal (b) Micro-Electro-Mechanical Systems (MEMS) membrane integrated into the Coplanar Waveguide (CPW) transmission line and employed in the K and Ka band phase shifters designs, respectively. On the other hand, the device working at 29.3 GHz has simulated losses better than \(-2.4\) dB for most configurations, despite at \(-90\) and \(+90\) phase shifts, \(-2.6\) dB losses are observed at the maximum limit of 2 GHz frequency range (Ka band phase shifter; see Figure 5). As conclusive consideration of this section, it must be highlighted that most of the losses are due to microswitches, as it will be discussed more in technical details later in this paper, especially for what concerns the non-idealities exhibited by the tested physical samples.
# 5G V2X System-Level Architecture of 5 GCAR Project
## 4 Detailed Analysis of Selected Features
### V2X Service Negotiation
* Spatial/time constraints, e.g., relevance area (e.g., a certain message should be transferred before the vehicle leaves/enters the relevance area, or the application is happening in a certain location, for instance for a lane merge); message transfer deadline; application duration time (the time interval which the application is expected to last). * Vehicle information, for each vehicle involved in the application, which could be, for example:
* Location (e.g., the current location of the vehicle). * Speed. * Planned trajectory (e.g., waypoints and associated timestamps). According to the V2X service descriptor, the V2X service negotiation maps some relevant network function(s) that receive the descriptor (or part of it). If the V2X service descriptor asked for a request, the V2X service negotiation provides feedback to the service considering the information receiving from the relevant network function(s) contacted upon reception of the service descriptor. The content of the feedback varies depending on the V2X service descriptor, for example:  
* A feedback answering a request for information about the QoS the network is expected to support in a certain location. * A feedback on whether the network expects that the message transfer described in the V2X service descriptor will be completed by the deadline. * A feedback on whether the network expects to fulfil the desired QoS for the whole application duration time indicated in the descriptor. An example of V2X service negotiation applied to a file transfer service (e.g., HD map update) is shown in Figure 7. In this example, the V2X service informs the network that a certain amount of data should be transferred towards a certain vehicle within a certain latency budget, also providing information about the expected trajectory of the associated vehicle within the area of relevance of the transfer. The network maps the information provided with the descriptor to internal network information, such as for instance network topology, expected load, and provides a feedback to the service containing information on when to start/pause the delivery in order to support an efficient file delivery within the transfer deadline. Figure 6: Flow diagram and information exchanged for the vehicle-to-everything (V2X) service negotiation.
# Mapping Wild Leek through the Forest Canopy Using a UAV
###### Abstract  
Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species.  
unmanned aerial vehicle (UAV); wild leek; Structure from Motion (SfM) 2018 10.3390/rs10010070