esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 4 Results
### Test Case 1  
In the first test case, the robot moves between two points in a straight line without obstacles. This case is used to verify the communication system and to generate a control case for other cases with obstacles. Figure 4 shows the frames at \(t=[0;2;3;5]\) s for both the simulation and the real case.  
In the plots shown in Figure 5, the curves of the simulation and the real case perfectly overlap, showing that the robot can follow the given path and reach the final position in the expected time. The absolute error between the two curves (Figure 6) is extremely small and shows a constant trend, with an average of about 0.0025 rad for the position and 0.003 rad/s for the velocity. Regarding the communication between the PC and robot controller, the transmission reached a rate of about 350 Hz, which is higher than the desired minimum value. Compared to the simulation, there is a slowdown due to data exchange and processing; nevertheless, the error generated is negligible, and the robot moves smoothly without sudden changes in velocity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(r_{inf}\,[\mathrm{m}]\) & \(r_{sup}\,[\mathrm{m}]\) & \(v_{inf}\,[\mathrm{m/s}]\) & \(v_{sup}\,[\mathrm{m/s}]\) \\ \hline
0.15 & 0.20 & 0.1 & 0.5 \\ \hline \(\lambda_{max}\) & \(\epsilon\) & \(\theta_{max}\,[\mathrm{rad/s}]\) & \(T\,[\mathrm{s}]\) \\ \hline \(10^{-3}\) & \(10^{-3}\) & \(\pi\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values used for both simulation and real case.  
Figure 4: Test case 1: straight motion between two points, Mode I. Simulated (**top**) and real (**bottom**) motion of the robot.  
Figure 5: Test case 1, Mode I. Joint rotations (**left**) and joint speeds (**right**).  
Figure 6: Test case 1, Mode I. Error between simulation and real system. Joint rotations (**left**) and joint speeds (**right**).
## 3 Results
### Preliminary Seeding Growth Tests  
3.1 The Choice of the Concentration of Algal Filtrate for Foliar and Soil Applications--Effect on Seedlings Length and Dry Weight  
The obtained results showed that there were no phytotoxic effects of algal products on the germination of radish. Table 2 shows the results of the experiments (average length and weight of seedlings) that concerned the selection of the best concentration of the clear and enriched with Zn(II) ions algal filtrate for the final seedling growth tests conducted at optimal conditions. For all the tested products, applied into soil and foliarly, seedlings length was arranged in the same order, taking into account the concentration of filtrate: F 50% > F 100% > F 25% > F 5.0% > C-H\({}_{2}\)O. Many statistically significant differences were observed. The application of algal filtrates at all concentrations stimulated seedlings growth (seedlings length was higher than in the control group). The best concentration for both soil and foliar application was filtrate 50%--clear and enriched with Zn(II) ions. The average seedlings length treated with F 50% was \(\sim\)4 and \(\sim\)2.8 times higher than in the control group for the soil and foliar application respectively. For F 50%-Zn it was respectively \(\sim\)3.2 and \(\sim\)2.5 times higher than in the control group. For both applications, soil and foliar, better results were obtained for clear rather than enriched filtrate used at the same concentration. For example, for soil application in the group treated with F 50%, seedlings length was 13% higher than for F 50%-Zn. For foliar application, the same difference was much higher--in F 50% seedlings length was two times higher than for F 50%-Zn. Other observations concerned the application method of algal filtrate--soil or foliar. Generally, better results were obtained for soil application of clear filtrate; however, the differences were not statistically significant. Only for F 5.0%, the average length of seedlings treated foliarly was 68% higher than for soil application of filtrate (\(p\) 0.00973). Figure 2: Mass spectrum of degree of complexation of metal ions in the tested products: (**a**) ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O solution (C-H\({}_{2}\)O-Zn); (**b**) H 10%; (**c**) H 10%-Zn; (**d**) F 100%; (**e**) F 100%-Zn.
## 4 Case Study
### System Reliability Evaluation
#### 4.2.1 Observed Value  
\(\bullet\)Point estimation  
Suppose that the observed value of system reliability is \(MTBF_{s}{}^{o}\), the amount of CNC grinding machine is \(n_{m}\), the year of failure data collecting is \(y_{r}\), the day of failure data collecting each year is \(d_{r}\), the work time each day is \(h_{r}\), the amount of failures is \(n_{f}\), and the mean maintenance time is \(t_{ma}\), the observed value of system reliability can be calculated by Equation (41).  
\[MTBF_{s}^{o}=\frac{n_{m}\cdot y_{r}\cdot d_{r}\cdot h_{r}-n_{f}\cdot t_{ma}}{n_ {f}} \tag{41}\]  
The observed value of system reliability is 563 h on condition that the work time each day is 24 h and the mean maintenance time is 2 h.  
\(\bullet\)Interval estimation  
The interval estimation of system reliability of the CNC grinding machine tool can be calculated by the confidence coefficient, as shown in Equation (42).  
\[[MTBF_{s}{}^{o}\cdot C_{L},MTBF_{s}{}^{o}\cdot C_{L}] \tag{42}\]  
where  
\(MTBF_{s}{}^{o}\) is the observed value of system reliability of the CNC grinding machine,  
\(C_{L}\) is the lower confidence coefficient, and  
\(C_{U}\) is the upper confidence coefficient.  
The interval estimation of system reliability of the CNC grinding machine can be calculated by the confidence coefficient table and the interval is [415,782] h.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 4 Materials and Methods
### Energy Efficient Routing Algorithm Based on AODV for FANETs  
Here, an enhanced routing algorithm is proposed based on the AODV protocol for FANETs. Several parameters influence the routing process in the FANET environment such as threshold values, fitness calculation bandwidth, hop count, energy consumption, and delay. The proposed algorithm focuses on the minimal energy, summation energy, and hop count concepts as cost metrics to find the optimal route in the network due to their direct impact on the routing process. The path selection by consuming the minimum energy and summation energy is calculated using the remaining energy and threshold values. The hop count is calculated using the averaging mode (AVG_MODE).  
The mathematical model of FEE-AODV [27] assumes a route, namely \(Tk=a0,a1,a2,\ldots ad\) such as \(a0\) indicates the source node and ad indicates the destination node. The notation 'h' can be included to evaluate the hop count between the transmitter and receiver node. Algorithm 1 Shows the node selection process for the intermediate nodes, while Algorithm 2 shows the node selection process for the destination node. The notation '\(r(ai)\)' indicates the remaining energy (REM_ENER) with averaging mode (AVG_MODE). If the remaining energy of the network is high, then the route is considered as the best optimal path. The number of hop counts must be as low as possible so that we can also reduce the energy consumption of the network. To calculate the route 'R', the following equations can be given:  
\[\text{REM\_ENER}=(min\;r(ai))/h \tag{1}\]  
\[\text{AVG\_MODE}=(min\;r(ai))/h \tag{2}\]  
Figure 4: The structure of the Proposed CLEA-AODV.  
The below equations help to find the optimal route \(O_{r}\). Considering the minimal remaining energy and maximal threshold value provides the path with low hop count, then there is a possibility of calibrating AVG_MODE and a specified threshold (TH).  
\[Kr=max(AVG\_MODE\ r(ai)-TH) \tag{3}\]  
If not, the above condition applies, the REM_ENER will progress as shown in the below equation  
\[Kr=max(REM\_ENER\ r(ai)-TH) \tag{4}\]  
where '\(A\)' is all routes (sets) and '\(TH\)' is a preset energy threshold.  
```
Begin  Initially find the RREQ's realness with referring source and broadcast id; If RREQ coincides with sequence number and dates of REM, TRE, and RREQ field REM = min(inter_res_ener,REM_rx); TRE = (inter_res_ener+REM_rx); Else  remove RREQ; End End
```  
**Algorithm 1**Node selection (intermediate nodes).  
A lesser value option may result in a strategy that includes the minimum energy nodes in the route, causing rapid energy exhaustion and frequent link failure. A higher value threshold could result in the less-than-optimal route selection of network performance. As a result, choosing the right threshold value is crucial for performance improvement. To determine the best threshold value, we ran simulations on our proposed approach, adjusting 'TH' from 0% to 100% to evaluate how the lifespan of the network and load distribution.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 4 Multi-Source Image Fusion Algorithm
### The Maximum Sharpness Value Guidance of the Region Overlapping Fusion Algorithm
The pseudo code of the Algorithm 1 flow is as follows:  
```
Input: two source image I = (IA, IB)
1: a multi-channel image I
2: repeat
3: find all local maxima (minima) of each channel image of Ri\(-\)1 by comparing the values of each pixel and its neighbours in the 3 \(\times\) 3 window centered on it
4: obtaining the upper and lower envelope surfaces Emax(x,y) and Emin(x,y) by Equations (1) and (2)
5: get a new intermediate variable L(x,y) by Equation (3)
6: obtain the i-th bimf and residue R
7: i = i + 1
8: until the residue Ri is a constant or a monotonic function, or thenumber of IMFs is more than a given threshold
9: Filter BIMFs and Residual through Equation (7) to get GBIMF and GRes
10: Use Sobel operator to calculate the clarity of GBIMF and GRES
11: Use Equation (19) to select a map with high definition
12: Fusion through overlapping segmentation Output: fused image If
```  
**Algorithm 1** Multi-source image fusion based on BEMD and region sharpness guidance region overlapping algorithm \begin{table}
\begin{tabular}{c c} \hline \hline
**Region** & **Stack Layer** \\ \hline a,h,p,v & 1 \\ b,f,l,o,q,u & 2 \\ c,d,e,r,s,t & 3 \\ j,n & 4 \\ k,l,m & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of overlapping layers in different regions. Figure 7: **(a)** Source image A, (b) source image B, (c) pixel-based fusion strategy, (d) image fusion based on region energy comparison, (e) image fusion based on region sharpness comparison.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 2 Materials and Methods
### Media Preparation  
A minimal medium [10] composed of 9 g/L of Na\({}_{2}\)HPO\({}_{4}\)\(\times\) 12H\({}_{2}\)O, 0.5 g/L of KH\({}_{2}\)PO\({}_{4}\), 1 g/L of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 1 g/L of MgSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 20 mg/L of CaCl\({}_{2}\)\(\times\) 2H\({}_{2}\)O, 10 g/L of NaCl, and 2 mL/L of trace element solution under 20 g/L of fermentable sugar supplementation from 0.3 N HCl DCB hydrolysate was used for _B. megaterium_ ALA2 cultivation in shake flasks. The same medium devoid of NaCl was used for C. _necator_ KCTC 2649 to accumulate PHA in shake flasks. The trace element solution was composed of 0.44 g/L of ZnCl\({}_{2}\)\(\times\) 7H\({}_{2}\)O, 4.98 g/L of FeSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 0.78 g/L of CuSO\({}_{4}\)\(\times\) 5H\({}_{2}\)O, 0.81 g/L of MnSO\({}_{4}\)\(\times\) 4H\({}_{2}\)O, and 0.24 g/L of Na\({}_{2}\)MoO\({}_{4}\)\(\times\) 2H\({}_{2}\)O dissolved in 1 N HCl solution [10].  
A _Halobacterium_ medium composed of 13 g/L of MgCl\({}_{2}\)\(\times\) 6H\({}_{2}\)O, 156 g/L of NaCl, 20 g/L of MgSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 4 g/L of KCl, 1 g/L of CaCl\({}_{2}\)\(\times\) 6H\({}_{2}\)O, 0.5 g/L of NaBr, 0.2 g/L of NaHCO\({}_{3}\), and 5 g/L of yeast extract [17] supplemented with 20 g/L of DCB-hydrolysate-derived sugars was used to assess the ability of _H. mediterranei_ DSM 1411 to co-produce PHAs and carotenoids at both shake flask and fermenter scale.
# OTFS-Based Handover Triggering in UAV Networks
## 6 Mathematical Analysis of the Channel Estimation Error Effect
### MMSE-Based OTFS DD Channel Estimation
In the case that pilot symbols are transmitted separate from data symbols, then (49) only comes from AWGN, i.e., \(\boldsymbol{\Omega}_{go,DT}=\sigma_{g}^{2}\mathbf{I}_{MN}\). Based on the derivation given in [26], the MMSE of \(\boldsymbol{\hat{h}}_{MMSE}\), becomes the following:  
\[MSE_{\mathcal{H},MMSE}=\text{Tr}\bigg{\{}\Big{(}\boldsymbol{\varphi}_{e}^{ \intercal}\boldsymbol{\Omega}_{go,DT}^{-1}\boldsymbol{\varphi}_{e}+\boldsymbol {\Omega}_{h}^{-1}\Big{)}^{-1}\bigg{\}}, \tag{51}\]  
where Tr means the trace of a matrix. Based on the detailed derivation given in Appendix B, \(MSE_{\mathcal{H},MMSE}\) is lower-bound by the following:  
\[MSE_{\mathcal{H},MMSE}\geq\frac{\left(\text{QP}\right)^{2}}{\left(\frac{ \left(\text{QP}\right)^{2}}{\left(\sigma_{2}^{2}\left(\sum_{i=1}^{QP}\sigma_{hi _{q}}^{2}\right)+\sigma_{g}^{2}\right)}+\sum_{i=1}^{QP}\frac{1}{\sigma_{hi_{q }}^{2}}}, \tag{52}\]To compare \(MSE_{\mathcal{H},MMSE}\) with \(MSE_{\mathcal{H},LS}\) in (42), let us consider the case of separate pilots, i.e., removing the term \(\sigma_{o}^{2}\left(\sum_{i=1}^{QP}\sigma_{hi}^{2}\right)\) from the denominator of (52), equal channel gains, i.e., \(\left(\sum_{i=1}^{QP}\sigma_{hi}^{2}\right)=\text{Q}P\sigma_{h}^{2}\), and pilots symbols are generated from \(\sigma_{e}^{2}=1\), then,  
\[\mathbf{y}_{DD}=\frac{1}{N}\Big{(}\Big{(}\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}}h_ {i_{q}}\mathbf{\Theta}^{i_{q}}\Big{)}(s_{DD}^{\epsilon}+s_{DD}^{\epsilon})+ \widetilde{g}_{DT}\Big{)}, \tag{44}\]0  
In this context, \(\sigma_{e}^{2}=1\) is a typical assumption, and it was assumed in [26] where the power of Tx pilot symbols is normalized. Also, it is a typical assumption in many digital communication systems. This is because many modulation schemes (BPSK, QPSK, QAM) are designed with a normalized average symbol power. This normalization ensures that different modulation schemes can be compared in terms of spectral efficiency and error performance without worrying about different power levels. Moreover, as typically \(\sigma_{h}^{2}\gg\sigma_{g}^{2}\) and \(\text{Q}P\ll MN\) for correct OTFS operation, then the lower bound of \(MSE_{\mathcal{H},MMSE}\) becomes less than \(\sigma_{g}^{2}\) like \(MSE_{\mathcal{H},LS}\). Even when \(\text{Q}P=MN\), the lower bound of \(MSE_{\mathcal{H},MMSE}\) will be less than \(MSE_{\mathcal{H},LS}\). However, this improvement comes at the expense of pre-knowing \(\sigma_{hi}^{2}\), as given in the denominator of (52), which needs long-term pre-training. By combining (42) and (53) into (38), we can say that the channel estimation error has lower effects than AWGN in both LS and MMSE DD channel estimation. Compared to the MSEs of the LS and MMSE OFDM channel estimators deduced in [29], the MSEs of the LS and MMSE OTFS channel estimators are lower than that which belongs to OFDM. This is because the length of the OTFS symbol is typically \(N\)-fold the length of the OFDM symbol, which results in high accurate channel estimation.
## 5 Conclusions  
In this study, we propose SugarRow-YOLO, a target detection model integrating the WTConv module, iRMB attention mechanism, and UIOU loss function to address the challenges of complex occlusion and structural variability in sugarcane elongation target recognition and crop row extraction tasks. Compared to YOLOv5n, YOLOv7-tiny, YOLOv8n, and YOLOv10n, SugarRow-YOLO improves mAP\({}_{50\text{-}95}\) by 3.5%, 4.7%, 3.3% and 3.7%, respectively. Relative to the baseline YOLOv11n model, SugarRow-YOLO achieves improvements of 2.0%, 1.9%, 1.6%, and 4.7% in precision, recall, mAP\({}_{50}\), and mAP\({}_{50\text{-}95}\), respectively. To address the irregularity of plant and row spacing in sugarcane fields, the DBSCAN clustering algorithm is combined with smoothing spline curves for crop row modeling, resulting in an overall recognition accuracy of 96.6%. Although the proposed method performs well, it has not yet been validated in real agricultural environments. Future research will focus on practical deployment and testing, optimizing model lightweight design to improve inference speed, and exploring cross-domain techniques and multi-source data fusion to enhance model generalization, thereby facilitating its application in complex scenarios.  
Conceptualization, G.D. and F.Z.; methodology, G.D. and H.D.; software, G.D. and Z.X.; validation, F.Z., H.D. and Y.L.; formal analysis, G.D. and Y.L.; investigation, F.Z. and Z.X.; resources, Y.L.; data curation, F.Z., H.D. and Z.X.; writing--original draft preparation, G.D. and F.Z.; writing--review and editing, G.D., H.D. and Y.L.; visualization, Z.X.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangxi Science and Technology Major Projects (Guike AA22117005 and Guike AA22117007), the Sub-project of the National Key R&D Program of China (2022 YFD2301104-01).  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YOLO & You Only Look Once WTConv & Wavelet Transform Convolution iRMB & Inverted Residual Mobile Block IOU & Intersection over Union UIIOU & Unified IOU DBSCAN & Density Based Spatial Clustering of Applications with Noise UAV & Unmanned Aerial Vehicle RTK & Real Time Kinematic LSM & Least Squares Method mAP\({}_{50}\) & Mean Average Precison (IOU = 0.5) mAP\({}_{50\text{-}95}\) & Mean Average Precison (IOU from 0.5 to 0.95 in steps of 0.05)
## 3 Channel Statistics
### Definition of Angular Spread Quantifiers  
The method to quantify the angle spread is based on Fourier coefficients of ADP with respect to azimuth AoA, \(p(\theta)\). Trigonometric moments are used for calculating the statistics of directional data. The definition of quantifiers used to gauge the spread in dispersion of energy in angular domain is presented in Table 2. The \(n\)th complex trigonometric moment of angular energy's distribution function, \(p(\theta)\), is \(\bar{R}_{n}\) which is given as in [17], as,  
\[\bar{R}_{n}=\bar{C}_{n}+j\bar{S}_{n}, \tag{1}\]  
where,  
\[\bar{C}_{n}=\frac{1}{P_{o}}\int_{0}^{2\pi}p(\theta)\cos(n\theta)\mathrm{d}\theta, \tag{2}\]  
and  
\[\bar{S}_{n}=\frac{1}{P_{o}}\int_{0}^{2\pi}p(\theta)\cos(n\theta)\mathrm{d}\theta. \tag{3}\]  
The total power of angular energy's distribution function, \(p(\theta)\) is equal to \(P_{o}=\int_{0}^{2\pi}p(\theta)\mathrm{d}\theta\). The trigonometric moments can be modified for the case of discrete data samples, which can be given by,  
\[\bar{C}_{n}=\frac{1}{F_{o}}\sum_{i=1}^{N}f_{i}\cos(n\theta_{i}), \tag{4}\]  
Figure 3: Angular distribution of multipath power with respect to azimuth AoA for different indoor mmWave (28 GHz) radio propagation environments; obtained after performing numerical integration along delay domain, data calibrations, and normalization operations on the measurement results provided in [6]. (**a**) LoS Scenario, Figure 8b of [6]; (**b**) NLoS Scenario 1, Figure 8e of [6]; (**c**) NLoS Scenario 2, Figure 8h of [6]. \[S_{n}=\frac{1}{F_{o}}\sum_{i=1}^{N}f_{i}\cos(n\theta_{i}), \tag{5}\]  
where \(F_{o}=\sum_{i=1}^{N}f_{i}\) for \(i=1,...,N\) and \(f_{i}\) is the number of occurrences in \(\theta_{i}\)th angular bin. In characterization of the SFs, only first and second moments are used; while \(|.|\) and phase\(\{.\}\) represent the magnitude and phase of a complex number, respectively.
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 2 Background and Related Work
### BGP Hijacking and Route Poisoning  
In February 2008, there was a significant incident [12] of routing interference happening as a real-world case. Pakistan Telecom (AS17557) announced an unauthorized IP prefix 208.65.153.0/24 with an unknown reason in the beginning. After that, PCCW Global (AS3491) received such an advertisement and forwarded it to other peering neighbors. As a snowball gathers as it goes on, more and more BGP routers propagated such an incorrect IP prefix to the others in the global network. Before this happened, the IP prefix 208.65.153.0/24 was announced by YouTube (AS36561). As routers preferred to use the shortest path to forward traffic, this policy led routers to hand over the traffic to the poisoning AS path. As a result, it caused a BGP hijacking--traffic that initially went towards YouTube turned to Pakistan Telecom.  
This situation quickly attracted the attention of network administrators. YouTube tried to use a routing mechanism to correct the misleading advertisement. It started to announce the two IP prefixes 208.65.153.128/25 and 208.65.153.0/25. According to the longest prefix match mechanism, in the lookup process, both 208.65.153.128/25 and 208.65.153.0/25 (leading to the correct path) were expected to be priority over 208.65.153.0/24 (leading to the poisoning path) in forwarding table. The mitigation successfully made affected routers change back to the original route again for reaching YouTube. Later, PCCW Global also blocked the misleading announcement made by Pakistan Telecom. The overall hijacking lasted about two hours, while it did make a massive impact on Internet users and content providers.  
From this event, it can be briefly concluded that AS may suffer from few pain points. First, the current BGP advertisement is based on a default trust relationship. A router decides which neighbor can be trusted, while it is unable to determine whether the learned route from its neighbor is poisoned or not. Second, it is hard for a router to ensure that its advertisements are good without implementing a reliable validation mechanism. Once the BGP hijacking happened, the misleading claim may spread out and cause security incidents, finally leading to property loss. Hence, for security reason, it is necessary to strengthen route validation to prevent AS suffering from such circumstance today.