esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 5 Conclusions  
In conclusion, this paper proposed a novel approach to analytically calculate noise distribution after the ZFE. The derived PDF was approximated utilizing series representations to reduce the complexity of calculations. The first approximation showed a close match to the histogram plot of noise distribution. In addition, both PDFs were utilized to implement the LLR equations for the turbo-coded system. It was observed that the proposed LLRs improved the performance of the coded system compared to the Gaussian-based LLR, with a small degradation for the second approximation compared to the first approximation. It was also observed that the complexity of both approximations and their LLRs slightly increased the number of operations compared to Gaussian receivers. The increase in running time of 9.23% results in a 2 dB improvement to the BER of the proposed system, which is regarded as an effective receiver compared to Gaussian-based receiver.  
Conceptualization and methodology, A.J.A.-A.; software, A.J.A.-A. and A.A.-N.; validation, A.A.-N. and M.S.A.; formal analysis, A.J.A.-A., A.A.-N. and M.S.A.; investigation, A.J.A.-A. and M.S.A.; resources, A.A.-N. and M.S.A.; data curation, A.J.A.-A. and A.A.-N.; writing--original draft preparation, A.J.A.-A. and M.S.A.; writing--review and editing; visualization; supervision; and project administration, A.J.A.-A. All authors have read and approved this version.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Results
### Classification of Intention Generation
#### 2.3.1 Determination of parameters  
In the CSA-ELM model, the determination of the CSA parameter is similar to ELM [39]. In the ELM model, the Sigmoid function is selected as the activation function and the number of hidden-layer neurons is 53. In CSA, the spatial dimension where rows search is \(d=53\times 3+53\) (i.e., 53 is the number of neurons in the hidden layer and 3 is the number of neurons in the input layer). \(RMSE/R^{2}\) is adopted as the fitness function. In this way, CSA is used to optimize the weight of the input layer and the bias of the hidden-layer unit in the constructed ELM model of 3-53-2. Following the reference [35], the parameters in the CSA algorithm are determined, including  
accuracy of a model. The precision rate is used to reflect the degree of accuracy in model classification; that is, how many of the samples classified as positive are real positive samples. The recall rate is utilized to reflect the sensitivity of the model; namely, how many correct samples are classified as correct. The precision rate and recall rate are used to deal with positive and negative events, respectively. The accuracy rate is regarded as the stability index of the classifier. The performance evaluation index and calculation formulas are illustrated in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metrics** & **Formula** \\ \hline Precision rate & \(\frac{\text{TP}}{\text{TP+FP}}\) \\ \hline Recall rate & \(\frac{\text{TP}}{\text{TP+FN}}\) \\ \hline Accuracy rate & \(\frac{\text{TP+TN}}{\text{TP+FN+TN}}\) \\ \hline \end{tabular} Note: TP, FN, FP, and TN are the values in the confusion matrix that reflect the model judgment in the classification problem. Among them, TP: true positive, means that the real label is “+”, and the value of the classification label is 1. FN: false negative, means that the real label is “+”, and the value of the classification label is 0. FP: false positive, means that the real label is “-”, and the value of the classification label is 1. TN: true negative, means that the real label is “-”, and the value of the classification label is 1. TN: true negative, means that the real label is “-”, and the value of the classification label is 1. TN: true negative, means that the real label is “-”, and the value of the classification label is 1. 1.  
Every time CSA choses the input weight and bias of the hidden-layer unit in the ELM model, it produces inconsistent results. It is measured based on the physiological data during the flight simulation and the results of each performance index are revealed in Table 5.  
As shown in Table 5, the accuracy rate, precision rate, and recall rate of the CSA-ELM model are all over 80%. This result indicates that the evaluation accuracy and generalization ability of the CSA-ELM model are very high, and it could be used for pilot intention identification.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 5 Analysis of the Fault Diagnosis Algorithm  
### Fault Diagnosis Algorithm  
In this study, faults were diagnosed by classifying the breakage of the USV thruster blade and entanglement of floating object conditions based on the correlations between data. To this end, PCA, whereby the structure and characteristics of data are preserved with minimal loss and dimensionality is reduced, was performed for the visual representation of each fault condition. Furthermore, to simplify the process of fault detection and diagnosis, the entropy method was employed to quantify the visualized PCA results. The proposed fault diagnosis algorithm developed using PCA and entropy is illustrated in Figure 8.  
The fault diagnosis algorithm was composed of the following processes: data acquisition, preprocessing, fault detection and fault diagnosis. First, the fault feature data were acquired, then preprocessed. The dimensionality of the data was reduced by applying the PCA technique in the fault detection process. Subsequently, the value was quantified through entropy. The entropy value was compared to the pre-selected threshold, and if it was smaller, it was considered normal. A larger value indicated a fault. When a fault was detected, the algorithm proceeded to the fault diagnosis process, visualized the PCA result and analyzed the symptoms of each fault to perform fault diagnosis.
## 3 Proposed Collaborative Control Algorithm
### Coordinated Voltage and Frequency Regulation Algorithm  
Sections 3.3 and 3.4 describe the independent voltage/frequency regulation process, however, the two independent regulation processes will frequently operate the BESSs to charge and discharge,  
Figure 5: Battery energy storage systems (BESS) frequency regulation block diagram.  
which is not conducive for the long-term use of energy storage equipment. Therefore, a further optimal coordinated regulation algorithm is proposed in this study.  
Figure 6 shows the overall flow chat of the BESS coordinated control algorithm proposed in this paper, which is mainly composed of four parts: (1) The full-time SOC control, it is used to confirm the situation of the BESSs, which includes the quantity of power-adjustable batteries, charging/discharging power of batteries and the SOC of each battery. The information will be used for the selection of BESSs. (2) The independent voltage regulation algorithm, it is used to calculate the power required for voltage regulation of each node at the current time. The sum of the power adjustments of each node is calculated at the end of this step. (3) The independent frequency regulation algorithm, it calculates the required power for the entire distribution frequency regulation. (4) The coordinated voltage and frequency regulation algorithm, it compares the frequency regulating power of the distribution network and the sum of each node's voltage regulating power. If voltage regulating power is larger than the frequency regulating power, the voltage regulating power will be the final regulation power. If frequency regulating power is larger than the voltage regulating power, the frequency regulating power will be the final regulation power. The larger part of the power will be allocated to the nodes with lower sensitivity coefficient according to Section 3.4.  
The coordinated control algorithm consists the following eight steps:  
Figure 6: Battery energy storage systems (BESS) coordination control algorithm flow chat.  
Step 1: Obtain the status of battery from information matrix \(H\) (Formula (6)).  
Step 2-3: Calculate the sensitivity coefficient and voltage regulation power of all nodes (the specific steps are shown in Section 3.2), and then select some nodes (\(x\)) with low sensitivity coefficient for frequency regulation compensation.  
Step 4-5: Calculate the total power for voltage regulation (\(\Delta P_{V}\)) and frequency regulation (\(\Delta P_{f}\)).  
Step 6: Compare the voltage regulation power (\(\Delta P_{V}\)) and the frequency regulation power (\(\Delta P_{f}\)).  
Step 7-8: If \(\Delta P_{f}\leq\Delta P_{V}\), there is no need to complete frequency regulation compensation at this time, therefore \(\Delta P_{fi}=0\). If \(\Delta P_{f}>\Delta P_{V}\), it is necessary to perform frequency regulation compensation in the candidate nodes and the power of compensation is \(\Delta P_{fi}=\left(\Delta P_{f}-\Delta P_{V}\right)/x\).  
Step 9: Get the total regulation amount \(\Delta P_{i}=\Delta P_{fi}+\Delta P_{Vi}\). And then send regulation commands to each node.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 5 Simulation Results
On the other hand, Figure 4c,d show higher performance gains than Figure 3c,d due to the large channel variation. In Figure 4c, the proposed scheme has more performance gain by 3 dB at BER of \(10^{-3}\) than the gain of Figure 3c. Figure 4d shows improvement for the degradation caused by the outage occurrence at the lower SNR as shown in Figure 3d. \begin{table}
\begin{tabular}{c c} \hline \hline Number of symbols & 64 \\ \hline Modulation & QPSK, 16-QAM \\ \hline Channel & Rayleigh fading \\ \hline MIMO detection & Zero forcing \\ \hline \multirow{4}{*}{Distance rate} & Random distribution \\ \cline{2-3}  & Source to Relay & Relay to Destination \\ \cline{2-3}  & 0 to 1 & 0 to 1 \\ \cline{2-3}  & 0 to 0.5 & 0.5 to 1 \\ \hline Transmit power & Normalization to 1 \\ \hline Number of users & 2 \\ \hline Number of relays & 4, 6, 8 \\ \hline \(N_{t}\) and \(N_{r}\) & 4, 8 \\ \hline \(N_{t}\) & 2 \\ \hline \(N_{m}\) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. The simulation results show that the proposed scheme has an improvement of performance through the diversity gain than other schemes. Even for low SNR, the proposed scheme achieves high performance through an antenna selection with optimal compound channel. Furthermore, diversity gain with a compound channel clearly increases with \(Q\) and SNR. When relay is widely distributed, the BER performance of proposed scheme can be improved. According to distance rate, the conventional schemes have 3 dB performance gain but proposed scheme has about 8\(\sim\)13 dB gain. Through fixing the number of selected antennas, the simulation results show that the proposed scheme is more affected by relay diversity gains than antenna diversity gains. The performance gains can be increased with more relays and antennas. Figure 3: BER performance with 4 antennas
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 3 The Proposed RSG-YOLO Algorithms  
Small targets from the UAV perspective are characterized by complex backgrounds, low resolution, and limited information. The original YOLOv8n model lacks sufficient detection performance in such scenarios, with a higher incidence of false positives and missed detections. Therefore, improvements are made to the YOLOv8n model: RFCAConv is introduced to replace ordinary convolutions; an improved CAMSPD is used for downsampling; a C2 GELAN model, which has richer gradient flow information and is lightweight and efficient, is substituted for C2f in the backbone; a small target detection layer is added, and the large target detection layer is removed. The structure of the improved RSG-YOLO model is shown in Figure 1.
## 2 Materials and Methods  
### Study Site  
The Naniu River Estuary, Guangxi, southern China (Figure 1) receives strong anthropogenic nutrient inputs from agriculture and brackish water shrimp ponds [50] (compare Table 3 in Section 4.3).  
laboratory until analysis within 10 weeks at the Leibniz Center for Tropical Marine Ecology (ZMT) in Bremen, Germany.  
Sediment cores were sampled using 70 cm long transparent Plexiglas liners with an internal diameter of 72 mm (Hydrobios, Kiel, Germany). Within a large mangrove patch, cores were taken from the edge of the forest near a high intertidal creek (Figure 1). Replicate cores were taken within an area of no more than 10 m\({}^{2}\). During sampling we took care to keep an undisturbed, horizontal sediment surface. Cores were sectioned after use and slices were macroscopically examined. No cores contained macrofauna or roots. Thus, the effect of bioturbation is excluded in this study.  
Figure 1: Studied mangrove site in the Nanliu River Estuary. Dark gray areas are mangrove forest, medium gray areas are low intertidal sand banks, the small black dot shows the sediment core sampling site, the concentric symbol shows the tidal sampling site (21\({}^{\circ}\)37’6.85” N, 109\({}^{\circ}\)1’40.15” E), and a small black arrow indicates the mangrove effluent sampling site. Up- and downriver directions are indicated by black arrows. For a more detailed map of the estuary see [50].  
Cores for the extraction of pore water were sampled in triplicate on 3 March and 22 September 2011. On 3 October 2011, after the tropical storm Nesat, one additional core was taken. The coefficient of variability of triplicate pore water concentrations of all nutrients was generally low (<0.2) at any given sediment depth, indicating that one core can be regarded as representative. Immediately after sampling and removal of overlying water, pore water was extracted in 2 cm intervals through pre-drilled holes using Rhizones(Rhizosphere, Wageningen, The Netherlands) attached to clean syringes. No more than 2-5 mL were extracted from each sediment depth, of which _ca._ 1.5 mL were transferred to 2 mL incubation vials. Samples for the analysis of phosphate, sulfate, and silica were acidified with concentrated hydrochloric acid to approximately 1% and stored at 4 \({}^{\circ}\)C; those for analysis of nitrate, nitrite, and ammonium were stored frozen.  
For sediment incubation six cores were taken on 16 March and 3 October 2011. Overlying water was removed to avoid sediment resuspension and cores were transported to the lab within 2 h.
## 2 Materials and Methods  
### Study Area  
Spain and Germany were selected as study areas (Figure 1) for different reasons. The first reason is because several studies have observed significant drought trends in southern and central Europe [41; 42]. Second, due to the importance of cereal grain production in Spain and Germany, they were among the top 5 producing countries in Europe in 2022 [43]. Finally, the last reason is due to the different characteristics hindering crop productivity which allow for a comparison of impacts under a wide range of conditions, from the limited water of Spain to the mainly limited energy of Germany [44; 45].  
In Spain, the regions of Castilla y Leon (CL) and Castilla-La Mancha (CM) were selected as the areas to be studied (Figure 1) since they are the main cereal-producing regions, accounting for approximately 60% of cereal production [46]. The CL and CM regions consist of 9 and 5 provinces, respectively, and are characterized by a semiarid Mediterranean climate, with cold winters and hot summers, average temperatures ranging between 10 \({}^{\circ}\)C and 15 \({}^{\circ}\)C and total annual rainfall ranging between 350 mm and 600 mm (Csb climate according to Koppen-Geiger classification [47]). The same approach was used in Germany, where the regions of Bayern (BY), Niedersachsen (NS) and Nordrhein-Westfalen (NW) were selected (Figure 1) as the regions with the highest cereal production, accounting for almost 41% of total production [48]. BY, NS and NW are composed of 7, 4 and 5 districts, respectively, characterized by a temperate continental climate, with cold winters and mild summers, average temperatures around 9 \({}^{\circ}\)C and total annual rainfall around 900 mm (Cfb and Dfb according to Koppen-Geiger classification [47]).  
Figure 1: Study areas. Regions of Germany (right, top) and Spain (right, bottom) selected for study (shaded orange): Nordrhein-Westfalen (NW), Niedersachsen (NS), Bayern (BY), Castilla y Leon (CL) and Castilla–La Mancha (CM).
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation  
At present, KGs are roughly divided into two categories: the general knowledge graph and the domain knowledge graph (DKG). As a specific domain of KG research, the power DKG undertakes the mission of shifting from \"data-driven power automation\" to a \"knowledge-driven smart grid\", which has important theoretical value and engineering significance for the power industry.  
In the following, we will introduce the power DKG from several perspectives: the overall construction process of power DKGs, the typical application scenarios of KGs, and the application of KGs in other aspects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Start Date** & **Main Knowledge Source** & **Scale (Entity/Concept/Relation/Fact)** \\ \hline WordNet [81] & 1985 & Expert Knowledge & 155,287/117,659/18/- \\ YAGO [82] & 2007 & WordNet + Wikipedia & 4,595,906/488,469/77/- \(\approx\) 40 M \\ Dbpedia [83] & 2007 & Wikipedia + Expert Knowledge & 17,315,785/754/2843/79,030,098 \\ Freebase [84] & 2008 & Wikipedia + Domain Knowledge + Swarm Intelligence & 58,726,427/220/39,151/3,197,653,841 \\ NELL [85] & 2010 & WordNet + Wikipedia(Multilingual) & 9,671,518/6,117,108/1,307,706,673/- \\ Wikidata [86] & 2012 & Freebase + Swarm Intelligence & 45,766,755/-/-/- \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some common large-scale knowledge bases.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 3 The Proposed Model
### Model Construction
#### Feature vector construction  
For each trustor \(\leftrightarrow\) trustRelation edge or trustRelation \(\leftrightarrow\) trustee edge, we create a feature vector \(F_{\text{TAUX}}^{\text{u2 TT}}(\text{E}_{u\leftrightarrow t})\) or \(F_{\text{TAUX}}^{\text{T2 uT}}(\text{E}_{t\leftrightarrow u})\), respectively. Each feature in this category is a _label-label feature_.
2. The other category of auxiliary edge features will be attached to edges between trustRelation nodes that are involved in the motif structure explained previously. Similarly to trust propagation features, features in this category follow the concept of propagative trust, i.e., direct trust propagation and transposed trust propagation, and grant values of each of them with either 0 for direct trust or 1 for transposed trust. However, different from the trust propagation features which are node features, they are edge features trying to \"filter out similarly behaving trustRelation nodes\".  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Feature** & \multicolumn{3}{c}{**Sequenced “Labels” of Nodes in Motif**} \\ \cline{3-5}
**Type** & **Name** & \(\text{T}_{A\to B}\) / \(\text{T}_{1}\) & \(\text{T}_{A\to C}\) / \(\text{T}_{2}\) & \(\text{T}_{C\to B}\) / \(\text{T}_{3}\) \\ \hline \multirow{6}{*}{Direct Trust} & d000 & N & N & N \\  & d001 & N & N & Y \\  & d010 & N & Y & N \\  & d011 & N & Y & Y \\  & d100 & Y & N & N \\  & d101 & Y & Y & N \\  & d111 & Y & Y & Y \\  & d111 & Y & Y & Y \\ \hline \hline \multirow{6}{*}{Transposed Trust} & \multicolumn{3}{c}{\(\text{T}_{A\to B}\) / \(\text{T}_{1}\)} & \(\text{T}_{A\to D}\) / \(\text{T}_{2}\) & \(\text{T}_{B\to D}\) / \(\text{T}_{3}\) \\ \cline{1-1} \cline{2-5}  & t000 & N & N & N \\  & t001 & N & N & Y \\  & t010 & N & Y & N \\ \cline{1-1}  & t011 & Y & Y & Y \\ \hline \hline \end{tabular} Refer to Figure 4 for \(\text{T}_{A\to B}\), \(\text{T}_{A\to C}\), \(\text{T}_{C\to B}\), and \(\text{T}_{B\to D}\). For a trustRelation node: “Y” indicates an existing trust and “N” a nonexistent one.  
\end{table}
Table 5: Trust propagation features used in the proposed model.  
Feature vector constructionFor each trustRelation \(\leftrightarrow\) trustRelation edge, we create a feature vector \(F^{\text{T}}_{\text{TPALUX}}(\text{E}_{t\leftrightarrow t^{\prime}})\). Each feature in this category is a _label-label-observation feature_.  
The current implementation of the proposed model does not support inference on cliques yet, which will be discussed below. And thus, it hinders the employment of the second category of auxiliary features (_label-label-observation features_) on edges between trustRelation nodes, which require the prerequisite of the existence of trustRelation-trustRelation edges. However, we use the same inference method for pairwise graph structure approximately for cliques, and we'd like to leave the inference on cliques as future work to complete.
## 4 Conclusions  
In this paper, we studied the effect of Juncus fiber content and Portland cement stabilization on the physical, mechanical, thermal and durability properties of compressed earth blocks. According to the experimental analysis, the following conclusions could be drawn:  
* The apparent density of the CEB containing 0.2% JF was reduced by approximately 11% in comparison to the CEB composite without fibers, regardless of the Portland cement percentage.
* The impact of JF on the ultrasonic pulse velocity was favorable to promote sound insulation characteristics and damping vibration, owing to the reduction in its value by up to 40% for 4% cement stabilization reinforced with 0.2% fiber content.  
Figure 20: CEBs after being tested for erosion resistance.  
* Decreasing the JF content and increasing the cement percentage decreased the total water absorption and capillarity coefficient of the CEBs.
* The incorporation of JF from 0 to 0.2% results in a moderate decrease in the compressive strength of CEBs. Meanwhile, increasing the Portland cement content leads to an increase in the wet and dry compressive strength.
* Better findings for the thermal conductivity were highlighted for the CEB with a 0.2% fiber content and stabilized with 4% cement.
* The durability criteria were respected based on the results of the experimental tests of resistance to erosion and abrasion.  
In conclusion, these results lead us to conclude that the combination of Juncus fibers as a reinforcement and Portland cement as a stabilizer allows us to obtain a CEB composite with good properties, which can provide safe, durable, and comfortable building materials.  
R.S.: Conceptualization, Investigation, Visualisation, Methodology, Testing, Writing original draft, Writing--Review and editing. H.K.: Conceptualization, Supervision, Validation, Writing--Review and Editing. M.B.: Conceptualization, Supervision, Validation, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors gratefully acknowledge Cadi Ayyad University and CAC of Cadi Ayyad University for their support.  
The authors declare no conflict of interest.