esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effective Numerical Aperture  
Effective _NA_ measurements were made using the laser wavelength of 983 nm, where the 6.5 \(\upmu\)m air-clad sapphire fiber produced a round LP\({}_{01}\) beam profile. Measurements were first taken on a 1 m-long SMF-28e patch cable to provide a control for the measurements. Five measurements were made, yielding a measured effective _NA_ of 0.113 and a standard deviation of 0.029 for the SMF-28e fiber, reported fully in Table 1. The published _NA_ of SMF-28e is 0.14, but that is at \(\lambda\) = 1313 nm, which exceeded the detection window of the beam profiler used in this experimentation. At 983 nm, both the LP\({}_{11}\) and LP\({}_{01}\) modes were clearly supported by the SMF-28e fiber, producing a noticeably oblong mode field projection and resulting in less precise beam width measurements due to the asymmetry of the hybrid projection. As the sapphire fiber produced a consistent LP\({}_{01}\) mode, however, these inaccuracies did not apply in sapphire measurements as exemplified by the 0.008 standard deviation recorded in measurements of the 6.5 \(\upmu\)m-diameter sapphire fiber, whose effective _NA_ was measured to be 0.090.  
Figure 8: Altering the input conditions by butt-coupling SMF-28e and 50/125 step-index MMF patch cables did not change the mode properties observed using direct input.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 3 Materials and Methods
### Eligibility Criteria  
Finally, to narrow down the search results from each database, the inclusion and exclusion criteria were predetermined. These criteria help to focus the investigation and evaluate the content of the selected publications. The inclusion criteria for the study required that only peer-reviewed journal articles in English be selected, with a primary focus on agricultural water management in the context of DT technology. Additionally, the studies needed to provide relevant answers to the research questions. The exclusion criteria ruled out conference papers, event and seminar summaries, book reviews, editorials, articles not in English, and publications that were not available in full text.  
The literature search process was evaluated through three stages -- identification, screening and inclusion--as depicted in the PRISMA flow diagram in Figure 3. After filtering the initial metadata, in total, 93 records were identified (40 from Scopus, 19 from ScienceDirect, 29 from WoS, and 5 from IEEE Xplore). Then, duplicates were removed during the identification stage, reducing the total to 48 publications. In the screening stage, titles and abstracts were reviewed,  
Some studies initially appeared relevant because their titles, abstracts, or keywords included terms like 'digital twin', 'water', and'agriculture'. However, after carefully reviewing their titles and abstracts, we found that these articles did not specifically address our main objective, i.e., exploring the use of DT technology for agricultural water management. Instead, these papers covered broader or indirectly related topics, such as flood management, water conservation, indoor farming, livestock management, construction, energy management, and soil characteristics. Although these topics have some indirect links to agriculture or water management, they did not clearly focus on DT applications specifically for managing water resources on farms. Therefore, we excluded these 27 studies at this stage to ensure our review remained targeted and relevant.  
After narrowing down our search to 21 papers for a full-text review, we carefully examined each one to see how well they aligned with our research questions. We focused on three key aspects: whether the study truly involved a DT, the type of application, and its practical relevance to agricultural water management. Some papers described digital simulations or models as DTs. Other studies discussed applications like indoor farming or fish farm design, which, although related to agriculture, do not directly deal with water management on farms. We also excluded articles focused solely on topics such as soil characteristics, yield estimation, general agricultural data collection for DTs, or general reviews that lacked clear, practical field applications specific to water management. After applying these criteria, we excluded 10 studies, leaving a final selection of 11 papers.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 3 Results
### Upper Limb Motor Functions: Long-Term Effects  
For the long-term effects of combined robotic training and tDCS protocols, the analysis of the 10 comparisons from 9 studies showed no significant effect size: \(\mathit{SMD}=-0.075\); \(\mathit{SE}=0.134\); variance = 0.018; 95% CI \(=-0.188\)-0.338; \(Z=-0.559\); \(p=0.576\); Figure 3. In addition, the heterogeneity levels across the individual effect sizes were minor: (a) \(Q\)-value = 11.808 with \(p\) = 0.224 and (b) \(I^{2}\) = 23.782%. Two publication bias tests additionally revealed minimal asymmetry levels of individual effect sizes: (a) a revised funnel plot with three imputed values after executing the trim and fill technique (Figure S2); and (b) [PERSON]'s regression test: \(\beta_{0}\) = 2.010 and \(p\) = 0.075. These findings indicate that the combined tDCS and robotic training protocols showed no significant sustained effects on upper limb rehabilitation post-stroke.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies  
This section discusses the various isolated DC-DC converters for satellite applications. Isolated converters are mainly required for secondary power distribution (as demonstrated in Figure 5e-g) in satellites to interface the satellite bus with a wide variety of payloads. The galvanic isolation between the input and output helps to attain effective power transfer with minimal noise and electromagnetic interference. It can be provided through a coupled inductor or using a transformer. The classification of various isolated DC-DC converters for satellites is shown in Figure 12.  
Figure 12: Classification of isolated DC-DC converters for satellite applications.
## 3 Experimental Result Analysis
### Microscopic Damage Characteristics
The results indicate that temperature significantly influences crack development in granite, while different unloading methods also affect crack formation. As temperature increases, the number, length, openness, and connectivity of granite cracks also increase. Additionally, micro-damage intensifies, with specimens in the variable-lower limit cyclic test exhibiting greater rupture than those in the constant-lower limit cyclic test.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 5 Conclusions  
1. In this study, we proposed an object detection model based on GhostNet to improve YoloV4 by replacing the backbone network of YoloV4 with GhostNet network, introducing feature layers adapting to small object detection in the feature fusion layer of the convolutional neural network, and using a combination of 1 \(\times\) 1 convolution and depth-separable convolution to replace part of the normal convolution in the neck network. The improved GhostNet-YoloV4 network has better robustness with fewer weight parameters and improved the detection speed while ensuring the accuracy of kiwi recognition.
2. The performance of the GhostNet-YoloV4 network model was evaluated, and the object detection algorithm was able to complete the recognition of kiwifruit under complex situations such as cloudy sky, shading from branches, and fruits' dense adjacency. The improved network model has a volume of 42.5 Mb, a detection speed of 42 frames/s, and an average accuracy of 93.07%, which meets the operational requirements and facilitates the application on embedded devices.
3. Based on the actual picking environment of the orchard, an image dataset of kiwifruit was produced and the superiority of the model was verified through a pairwise comparison test. Compared with YOLOv4, GhostNet-YoloV4 compresses the network model size and improves the detection of model fine-grained by replacing the backbone network and improving the part-neck network. Using MobileNetV3_YoloV4, SSD, and YoloV4 models for testing respectively, the network model detection speed and model compression volume are better than other models with guaranteed detection accuracy.  
Data curation, J.G.; writing--original draft preparation, J.G.; visualization, X.X., L.W., Y.G. and X.S.; supervision, L.L., J.H. and S.D.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
Key Research and Development Program of Hunan Province, Grant/Award Number: 2021 SK2046; Hunan Agricultural Science and Technology Innovation Program, Grant/Award Number: 2021 CX43; Hunan Agricultural Science and Technology Innovation Program, Grant/Award  
Figure 11: Comparison of recognition results using different models.  
Number: 2022 CX30; Hunan Agricultural Science and Technology Innovation Program, Grant/Award Number: 2022 CX54; Hunan Modern Agricultural Fruit Industry Program, Grant/Award Number: xcnz [2021] No. 37.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 System Model  
We consider a downlink transmission model where \(K\) non-cooperative RISs are deployed to assist the communication from a BS to a high-speed mobile vehicle with a constant speed \(v\) meters/second (m/s). There are no information exchanges between any two non-cooperative RISs; therefore, multi-hop signal reflections [20; 21] are omitted. All the RISs are identical, and each has \(M\times N\) isotropic elements, where \(M\) and \(N\) are the row and column numbers of the RIS. In addition, consider the far-field scenario; the spacing of any two RISs is large enough that the interference can be neglected. The BS and the vehicle are both equipped with a single isotropic antenna with the positions \(P_{\text{BS}}\) and \(P_{\text{V}}(t)\), respectively. Figure 1 shows the proposed system with \(K=2\) RISs; each of them is a uniform planar array that is placed on a rectangular grid spaced \(dx\) and \(dy\) apart in parallel to the \(x-y\) plane in a three-dimensional Cartesian coordinate system. The heights of the \(i\)-th RIS, the BS, and the vehicle are \(h_{\text{RISi}}\), \(h_{\text{BS}}\), and \(h_{\text{V}}\), respectively. The PI (e.g., real-time positions) is sent to the BS through existing positioning techniques (e.g., global positioning system [28]). The RIS phase shift set is then designed and sent to all RISs. A summary of the notations of system model coefficients is provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(P\) & Column vector. & \(\lfloor\cdot\rfloor\) & Floor function. \\ \(\mathcal{U}\) & Uniform distribution. & \(\lceil\cdot\rceil\) & Ceil function. \\ \(\mathcal{CN}\) & Complex Gaussian & \(\mathbb{Z}\) & Sets of integers. \\ \((\cdot)^{\text{T}}\) & Transpose operation. & \(\mathbb{R}\) & Sets of real numbers. \\ \(j\) & Imaginary unit. & \(|\cdot|\) & Absolute operation. \\ \((x\text{ mod }y)\) & Remainder of the & & Euclidean norm \\  & division of \(x\) by \(y\). & \(\|\cdot\|\) & operation. \\ \hline \end{tabular}
\end{table}
Table 2: Notations.  
Therefore, the position column vector of the \((m,n)\)-th element on the \(i\)-th RIS is  
\[\mathbf{P}_{in,in}=\bigg{[}\omega(m,dx,M)+\Delta_{i}^{x},\omega(n,dy,N)+h_{\text{ RISi}},\Delta_{i}^{z}\bigg{]}^{\text{T}}, \tag{1}\]  
where \(i=1,\ldots,K\) and \(\omega(m,dx,M)=dx(m-0.5((M+1)\text{ mod }2))\), \(m\in\Omega(M)\) with  
\[\Omega(\sqrt{M})=\{\big{(}(\sqrt{M}+1)\text{ mod }2)\big{)}-\lfloor\sqrt{M}/2 \rfloor,\ldots,\lfloor\sqrt{M}/2\rfloor\}, \tag{2}\]  
and \(\omega(n,dy,\sqrt{N})\) is obtained in the same way as \(\omega(m,dx,\sqrt{M})\). The \(x\) and \(z\) coordinates of the center for the \(i\)-th RIS are \(\Delta_{i}^{x}\in\mathbb{R}\) and \(\Delta_{i}^{z}\in\mathbb{R}\), respectively. It is noteworthy that we assume the reflecting sides of all RISs always orient to the vehicle [21].
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 4 Design of the Space-Based Time System for Navigation Constellations  
The space-based time system is constructed and maintained by a constellation clock group in the navigation constellation, which includes a configured high-performance atomic clock, inter-satellite measurement, and data transmission links. The middle- and high-orbit laser and microwave inter-satellite links are responsible for broadcasting the satellite inter-clock offset parameters, achieving time synchronization within the constellation.  
Currently, the most widely used on-board time synchronization and autonomous orbit determination method is the distributed Kalman filter algorithm. However, it leads to a rapid increase in the time difference between the constellation and the ground, resulting not only in the decreased accuracy of the timing service but also indirectly affecting the accuracy of autonomous orbit determination, making it difficult to exceed 60 days. Two main reasons contribute to this problem.  
1. The constellation timekeeping function is not designed, making it challenging to maintain constellation time accuracy for an extended period.
2. As the autonomous operation time of satellites approaches 60 days, the time scale error of constellation maintenance becomes less able to meet the needs of autonomous orbit determination, leading to a rapid deterioration in orbit determination precision.  
Therefore, research on the timekeeping function of the navigation constellation is necessary. Considering the advantages of the timekeeping accuracy of the constellation clock group, the time-frequency performance of the constellation during autonomous operation should meet the quality of service (QoS) requirements of autonomous orbit determination and timing services.  
To establish and maintain the high-precision space-based time system of the navigation system, this paper proposes the following:  
1. Satellite clock stability evaluation technology based on inter-satellite link measurement;
2. The establishment of the satellite-ground joint integrated atomic time;
3. Space-based integrated atomic time that can support special circumstances based on the ALGOS algorithm.  
This comprehensive space-based time system can provide a higher-stability frequency reference and system time for the satellite navigation system, thereby further increasing the QoS in smart city applications.
## 3 Development of ICRT
### Estimation of Issue-Based Similarity (IBS)  
Issue-based similarity (IBS) between two articles, \(a_{1}\) and \(a_{2}\), is based on how they cite a certain number of references with similar titles. It is the fundamental basis of clustering. A larger IBS indicates that the distance (dissimilarity) between \(a_{1}\) and \(a_{2}\) is smaller. ICRT identifies the set of references in \(a_{1}\) that are the most similar to those references in \(a_{2}\). This set of references is \(MostSimRef(a_{1}\), \(a_{2})\) defined in Equation (5), where \(Ref(a)\) is the set of references in article \(a\), and \(RefSim(r_{1}\), \(a_{2})\) measures how a reference \(r_{1}\) in \(a_{1}\) is similar to the references in \(a_{2}\) (see Equation (6), where \(Title(r)\) is the set of terms in reference \(r\), and \(IRF(t)\) is the inverse reference frequency of term \(t\) defined in Equation (7) to indicate how rare \(t\) is in the references).  
\[MostSimRef(a_{1},a_{2})=\left\{\begin{array}{l}\{r_{1}|r_{1}\in Ref(a_{1}), \text{ and }RefSim(r_{1},a_{2})\text{ is top }20\%\text{ in }Ref(a_{1})\},\\ if\ |Ref(a_{1})|\geq 5;\\ \{r_{1}|r_{1}\in Ref(a_{1}),\text{ and }RefSim(r_{1},a_{2})\text{ is the largest in }a_{1}\},\\ \text{otherwise.}\end{array}\right. \tag{5}\]  
\[RefSim(r_{1},a_{2})=Max_{r_{2}\in Ref(a_{2})}\frac{\sum_{t\in Title(r_{1})\cap Title(r_{2})} IRF(t)}{\sum_{t\in Title(r_{1})\cup Title(r_{2})}IRF(t)} \tag{6}\]  
Figure 2: Main steps of ICRT: (**a**) estimating IBS and identifying IIT between each pair of articles; (**b**) clustering the IITs, and (**c**) producing clusters of articles accordingly.  
\[IRF(t)\ =\ Log_{2}\frac{\text{Total number of references}}{\text{Number of references mentioning }t} \tag{7}\]  
With _MostSimRef_ defined above, ICRT can identify which references in \(a_{1}\) are the most similar to the references in \(a_{2}\), and vice versa. These references can collectively indicate how \(a_{1}\) and \(a_{2}\) discuss similar issues; hence, issue-based similarity (IBS) between \(a_{1}\) and \(a_{2}\) can be defined in Equation (8), where _Rareness(\(r\))_ measures how rarely a reference \(r\) appears in articles (see Equation (9)). The basic idea is that two articles are similar in discussing certain issues if they share similar and rare references.  
\[IBS(a_{1},a_{2})\ =\ \frac{\sum\limits_{\tau_{1}\in\text{MostSimRef}(\tau_{1},a_{2}) \times\text{Rareness}(\tau_{1})+}\sum\limits_{\tau_{2}\in\text{MostSimRef}(\tau_ {2},a_{1})\times\text{Rareness}(\tau_{2})\atop\tau_{1}\in\text{MostSimRef}( \tau_{1},a_{2})}\sum\limits_{\tau_{2}\in\text{MostSimRef}(\tau_{2},a_{1}) \times\text{Rareness}(\tau_{2})}}{\sum\limits_{\tau_{2}\in\text{MostSimRef}( \tau_{2},a_{1})\times\text{Rareness}(\tau_{2})}} \tag{8}\]  
\[Rareness(r)\ =\ Log_{2}\frac{\text{Total number of articles}}{\text{Number of articles with }r\text{ as a reference}} \tag{9}\]
## 3 Setup and Measurement Campaign
### CNR Inspection and Jammer Position Determination
\begin{table}
\begin{tabular}{c c c c} \hline
**DOY** & **Series** & **Receiver** & **Used Satellites** \\ \hline \multirow{8}{*}{262} & \multirow{8}{*}{1} & \multirow{8}{*}{Xiaomi 11T} & \multirow{8}{*}{E04 E05 E09 E34 E36 G01 G03 G04 G17 G19 G21 G22 G31 G22 G31} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \multirow{8}{*}{263} & \multirow{8}{*}{2} & \multirow{8}{*}{Xiaomi Mi8} & \multirow{8}{*}{C09 E11 C27 C33 E04 E05 E09 E34 G01 G03 G08 G17 G19 G21 G22 G33 R07 R24} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline
**DOY** & **Series** & **Receiver** & **Used Satellites** \\ \hline \multirow{8}{*}{262} & \multirow{8}{*}{1} & \multirow{8}{*}{Xiaomi 11T} & \multirow{8}{*}{E04 E05 E09 E34 E36 G01 G03 G04 G17 G19 G21 G22 G31 G22 G31 G22 G31} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \multirow{8}{*}{263} & \multirow{8}{*}{Xiaomi Mi8} & \multirow{8}{*}{C09 E11 C27 C33 E04 E05 E09 E34 G01 G03 G08 G17 G19 G21 G22 G31 G22 G31 G22 G31} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline
**DOY** & **Series** & **Receiver** & **Used Satellites** \\ \hline \multirow{8}{*}{262} & \multirow{8}{*}{1} & \multirow{8}{*}{Xiaomi 11T} & \multirow{8}{*}{E04 E10 E11 E12 E19 G01 G03 G08 G17 G21} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \end{tabular}
\begin{tabular}{c c} \hline
**DOY** & **Series** & **Receiver** & **Used Satellites** \\ \hline \multirow{8}{*}{262} & \multirow{8}{*}{1} & \multirow{8}{*}{Xiaomi 11T} & \multirow{8}{*}{E04 E10 E11 E2 E19 G01 G03 G08 G17 G21} \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 10: Used combinations of satellite-receiver for the calculation of the time of approach (C stands for BeiDou, E for Galileo, G for GPS, and R for GLONASS satellites on their respective frequency bands B1, E1, L1, and G1).
## 3 Theoretical Analysis of Blockchain Identity Authentication
### Identity Authentication Based on Zero-Knowledge Proof  
In 1989, MIT researcher [PERSON] and others proposed zero-knowledge proof [24], suggesting that the prover can prove that his statement is correct without disclosing any relevant information to the verifier. As revealed in Figure 5, zero-knowledge proof is adopted in a scenario where the group determines the illegal nodes, which can assure that group members complete the authentication without revealing their ID.  
Traditional identity authentication schemes may encounter attacks such as replay and impersonation during the authentication process, while zero-knowledge proofs can verify their identity without revealing their keys. Meanwhile, the verifier fails to obtain any valuable information about the keys. Given this feature, a new identity authentication scheme based on zero-knowledge proof is proposed in this paper in accordance with the authentication proposed in the literature [25; 26; 27]. The security of this scheme relies on the intractability of discrete logarithms. The scheme is expressed as:  
\[x=Ina^{y}+ev\;mod\;p \tag{1}\]  
where \(p\) and \(q\) are two prime numbers, q denotes a prime factor of \(p-1\), a represents a factor of order \(q\), and \(a\
eq 1\). Client H announces the authentication system parameters (\(p\), \(q\), \(a\)) for each node, and the upload node of each group registers its unique identity F for each node of the group. The upload node utilizes the hash function to generate a private key  
Figure 4: Flow chart of the SHA256 algorithm.  
Figure 5: Zero-knowledge proof model.  
following its identity. Additionally, N = hash(F1 | K), where K signifies the master key of the upload node.  
As demonstrated in Figure 6, the specific process of the identity authentication protocol is detailed as follows.  
Step 1: Node A requests to join the network, sends its own identity FA, uploads the node to generate a public key v = Ina'k mod \(p\) (including the private key k = hash (F_A | I K)), and makes v public.  
Step 2: Node A selects a random number r smaller than \(q\), calculates it x = Ina'r mod \(p\), and makes x public. This process can be completed before both parties authenticate, saving authentication overhead.  
Step 3: Suppose node B receives x and initiates a challenge to node A, and sends a random number e smaller than \(q\) to node A.  
Step 4: Node A calculates y = (r - ke) mod \(q\) and sends y to node B.  
Step 5: Node B verifies x = Ina'y + evmod \(p\) and whether it is established. If it is established, the authentication of node A is successful; otherwise, the access of node A is rejected, and an authentication failure message is sent to the client.