esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Terahertz Time-Domain Spectroscopy of High-Permittivity Substrates
### Results
This is attributed to defocusing effects due to the substrate thickness, and considering its high permittivity and operation in reflection mode, which doubles the wave path distance. Indeed, the effect is more noticeable in the case of RO6010.2 LM, which is thicker than the other two samples studied. Nevertheless, in the investigated examples the important information in the reflection spectra is contained in the lower-frequency part, which is very well reproduced by the theoretical model, so relevant correction techniques were not applied [31]. The results revealed different trends for the three materials, despite the fact that their dielectric properties were almost identical at the microwave frequency of 10 GHz, as summarized in Table 1. RO3010 and RO/duroid 6010.2 LM show a significant frequency dispersion with their relative permittivity (loss tangent) approximately in the range of 12-16 (0.02-0.1) and 14-17 (0.02-0.07), respectively, in the investigated spectral window. Such a level of frequency dispersion needs to be taken into account in the design of components as it might significantly impact their performance. On the contrary, the thermose laminate TMM10i shows a significantly lower frequency dispersion (\(10.7\leq\epsilon_{\rm r}\leq 11.4\)) and lower values of loss tangent (\(0.008\leq\tan\delta\leq 0.03\)), both highly desirable properties for high-performance and broadband devices. Overall, the retrieved Lorentz dispersion models accurately reproduce the measurements and constitute a reference for the engineering of novel, low-profile components in emerging applications in the sub-THz spectrum.
# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 6 Discussion

Table 6: Comparison among conventional methods, previously proposed predictive control methods and machine-learning-based methods for water hammering detection. Table 6 shows that predictive control technology-based ML algorithms, mainly regression analysis, can give more accurate results than any other method. The pressure-velocity curve was analyzed in the proposed study and pressure variation under different valve-closing and -opening conditions have been collected. The system has 3 pumps. The variation of the flow rate for valve closing and the opening has been performed and recorded in a sequential manner (Table 3). Figures 6 and 7 show that when one valve of the pump is suddenly closed and opened again, variation is not so steep and a pressure drop has been noted. It is not critical, as other pumps are in running conditions and their valves are open. When, in the second case, two valves are suddenly closed and opened, there is a steep variation in pressure and velocity concerning time. However, in the third case, the exact steep variations are observed due to sudden closing and opening of three valves (Figures 6 and 7). At different times, pressure-head value changes for three valves. The pressure head in this situation goes below the vapor pressure head [27,28]. Therefore, in the third condition, interruption of the operation is more than the other two and fault, and the no-fault condition has been identified (Figure 9). Figure 11 shows the FFT analysis of vibration in a time-frequency pattern at different pressure bars under normal and faulty conditions. Figure 12 shows the regression analysis in the time domain at four different pressure bars to predict weak points. Various algorithms can be used for the prediction of faults in the system, among which SVM and regression analysis are suited for fault prediction in a pumping system. Figure 19 shows the overall accuracy of some conventional methods and ML-based predictive control method. The accuracy of the linear regression algorithm is higher than other ML algorithms. Concerning prediction speed, training time, and accuracy, it is identified that regression analysis is the best-suited method (Figure 20).
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 1 Introduction
From the perspective of multi-disciplinary modeling, a PMSM servo system model for spacecraft was established for the first time, which includes three nonlinear factors: elasticity, backlash, and friction. This model comprehensively characterizes the influence mechanism of nonlinear factors on the PMSM servo system. 2. The backlash features were integrated into the elasticity to form an elastic dead zone model. Besides, considering the effects of impact vibration and environmental temperature on the LuGre friction model, some improvements were made to it. 3. By analyzing the difference between simulation and experimental data, a high-precision PMSM servo system model was formed, which can provide the theoretical basis for control method research and system performance optimization. The remainder of this article is organized as follows. In Section 2, the PMSM servo system is introduced. The details of nonlinear precise modeling of elasticity, clearance, and friction characteristics are proposed in Section 3. Subsequently, the experimental testing and validation are presented in Section 4. And finally, Section 5 concludes this paper.
acquired unstructured data stemming from a wide range of heterogeneous sources are standardized according to predefined protocols [7]. MITRE's Structured Threat Information eXpression (STIX) [8] is an example of a representation used as a common language when sharing CTI; it contains definitions of indicators, events, targets, and tactics used during an attack [9, 10].  
Semantic web tools such as ontologies and Knowledge Graphs (KGs) appear to be particularly useful for establishing shared vocabularies and practices. In particular, ontologies and KGs are widely used within different and multidisciplinary scientific fields such as medicine [11, 12, 13], criminal justice [14, 15], and sports science [16, 17], among others [18, 19, 20, 21, 22]. Ontologies are also applicable in the cybersecurity domain, where preexisting representations such as STIX can be incorporated, enhanced, and extended with new entities and definitions.  
Ontologies contain important concepts, formally defined as classes, of a specific domain along with their properties, all described within a formal and unambiguous context [23, 24]. In computer science terms, an ontology resembles a carefully constructed tree of classes, relationships, and inheritance of restrictions and properties [23, 25]. Within the cybersecurity landscape, ontological models allow analysts to explore the patterns and connections between different entities and calculate useful indicators and scores for assessing potential threats and vulnerabilities [26].  
On the other hand, KGs usually leverage the structure imposed by the relevant ontology and represent actual data as a set of triples comprising pairs of entities and their relationships [27]. They are often stored as graph databases, and can be easily queried and visualized with various tools such as Neo4j [28, 29].  
In addition to establishing a common semantic framework, ontologies and KGs are capable of automating the process of registering new data and inferring new knowledge that may be difficult to be discovered manually. Especially when combined with Deep Learning (DL) methods for transforming unstructured data to meaningful knowledge, ontologies and KGs become an important asset in the CTI pipeline. The possibility of human error can be greatly mitigated, and analysis becomes more capable of dealing with the exponential generation of CTI data [30].
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.2 Frequent Disconnection of Network  
In VANET, vehicles are moving. This changes the network density and leads to breaking the communication links repeatedly. This phenomenon often occurs in the low-density network areas and in when there are radio obstacles in the network because in these areas, there is a repeated disconnection that causes a high rate of link breakage, high delay in the data transmission, or even data loss. To ensure the quality of the connections, an efficient routing protocol must quickly detect the link failure and find an alternative link [30; 32].
###### Abstract  
Due to the high consumption and incorrect disposal of pharmaceutical active compounds (PhACs), they are recognized as contaminants of emerging concern. Wastewater treatment plants (WHPCs) may be inefficient in removing PhACs, therefore discharging them into surface waters. The removal efficiencies of five WWTPs located in the south of Portugal (Alentejo) were evaluated in 2020. Twenty-six PhACs were analyzed in wastewater influents, effluents, and surface waters, upstream and downstream of the WWTPs by solid-phase extraction (SPE) and ultra-performance liquid chromatography coupled with tandem mass detection (UPLC-MS/MS). The most representative PhACs in influents were acetaminophen, caffeine, naproxen, ibuprofen, and diclofenac with minimum-maximum concentrations of 49-225 ug/L, 26-46 ug/L, 5.9-13 ug/L, 5.2-22 ug/L, and 1.3-2.5 ug/L, respectively. For effluents, it was acetaminophen, caffeine, and diclofenac with minimum-maximum concentrations of 0.054-7.8 ug/L, 0.084-4.8 ug/L, and 0.28-3.3 ug/L, respectively. The highest removal efficiencies were observed for acetaminophen, sulfadiazine, cortisone, testosterone, metoprolol, and propranolol (100%). The lowest removal efficiencies were observed for carbamazepine (2.7%) and diclofenac (\(-\)13.2%). The risk quotient of sulfamethoxazole and diclofenac were higher than 1 for receiving waters, indicating they probably pose high risks to aquatic organisms.  
wastewater; influent; effluent; pharmaceuticals; WWTP; UPLC-MS/MS +
Footnote †: journal: Applied Sciences
## 3 Technical Background
### Algorithm Flow  
The FVIM algorithm can be summarized as follows:  
1. Initialization Phase: Define and randomly initialize the key parameters and agent positions. Set the upper and lower boundaries of the search space.
2. Iterations Phase: Update the agents' positions using the four mathematical models, iterating until the stopping conditions are met.
3. Finding Optimal Solution Phase: Identify the best solution by evaluating the fitness of each agent's position and comparing it to the objective function.  
FVIM leverages four leading agents Alpha, Beta, Gamma, and Delta to guide the search process. These four vectors are actual candidate solutions within the population, and they are dynamically updated during each iteration of the FVIM phase. The four vectors (Alpha, Beta, Gamma, and Delta) represent the four best candidate solutions within the population at any given iteration. Specifically:  
* Alpha: The best solution found so far.
* Beta: The second-best solution.
* Gamma: The third-best solution.
* Delta: The fourth-best solution.  
These vectors are actual members of the population and are updated based on their fitness values. Their role is to influence the position updates of all other particles in the population, thereby guiding the search toward promising regions of the solution space.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 5 Performance Evaluation
### Performance Comparison of the Four Protocols
Similarly, for a network with \(N_{m}=10\), the throughput per node will tend to 960 b/s. The SSMA protocol requires nodes to reply with an ACK for each packet received, which results in lower throughput than SS-MAC under the same conditions. Figure 12 also illustrates that the throughput performance of LELLMAC is better than that of IEEE 802.15.4 MAC and SSMA under the same conditions. For LELLMAC, when the offered load approaches 6000 bits/node, the throughput of each node in the network with \(N_{m}=5\) and \(N_{m}=10\) is 1369 bits and 822 bits, respectively, which are 25.1% and 12.4% lower than SS-MAC, respectively. Figure 11: Average packet delay versus packet arrival rate. The above simulation results fully demonstrate that compared with IEEE 802.15.4 MAC, SSMA and LELLMAC, the SS-MAC proposed in this paper can adapt to large-scale cluster tree networks relatively. Moreover, it also performs best in packet delay and throughput under the same conditions.
## 4 Security Threats
### Distribution of Sharing Rules
#### 4.3.5 Rules Altering  
One last attack, closely related to the previous one, is that of unauthorized rules modification. More specifically, an attacker may successfully modify packets containing information regarding the rules enforced in a DSA network to mislead some, or even all, competitors. One point to emphasize here is that such vulnerability can be eliminated by applying special techniques in the network to assure the integrity of packets encapsulating rules-related information.  
To summarize, any attacker attempting to modify the valid set of rules of a DSA network may cause quite similar problems to the latter, irrespective of the nature of the realized attack. To make this statement clearer, it is enough to point out all possible motivations the attacker may have for misbehaving, instead of abiding by the enforced rules. To begin with, the most common such incentivefor the adversary is to increase his utility without having to pay the respective price. If such a target is accomplished, phenomena like unfair resource usage, competitors' satisfaction level degradation, annoyance and complaints from the victims' side, spectrum under-utilization, as well as negative effects on network efficiency, stability, flexibility and adaptation to changes, are highly probable to arise. Either when new fake rules are appended to the set of the existing ones, or some of the existing ones are deleted or modified, or even when a part of them (or even all) are not accessible by network nodes, it will become impossible to fully apply the required spectrum sharing model.
## 3 Proposed Hybrid Optimization Approach
### Evolution Criteria  
The DSCA is designed to evolve while considering a simple sine-cosine rule, where there is a 50% probability of evolving with the sine function and a 50% probability of evolving with the cosine function, as shown in Equation (11) [42]. With this equation, it is possible to create new descendant individuals \(P_{i}^{t+1}\) from \(X_{best}^{t}\).  
\[P_{i}^{t+1}=\begin{cases}round(X_{i}^{t}+r_{2}\sin(r_{3})|r_{4}X_{best}^{t}-X_ {i}^{t}|)&\text{if $r_{1}<0.5$}\\ round(X_{i}^{t}+r_{2}\cos(r_{3})|r_{4}X_{best}^{t}-X_{i}^{t}|)&\text{if $r_{1}\geq 0.5$} \end{cases} \tag{11}\]  
In Equation (11), \(P_{i}^{t+1}\) is the value obtained by the evolutionary method. It is a possible solution that can substitute \(X_{i}^{t}\). Parameter \(r_{1}\) is a constant number from 0 to 1, which ensures the equivalence of the transformation between the sine and cosine trigonometric functions [43]. Parameter \(r_{2}\) is a variable that determines where the resulting new individual should move, i.e., the closest possible to the best solution \(P_{best}\) in the solution space [43], as shown in (12).  
\[r_{2}=a-t\frac{a}{t_{max}} \tag{12}\]  
In Equation (12), \(t\) is the current iteration, \(t_{max}\) is the maximum number of iterations, and \(a\) is a constant used for this article, with a value of 2, as recommended in [43]. \(r_{3}\) is a random number between 0 and \(2\pi\) that determines how far or how close the new potential solution moves with regard to the current best solution [43]. Finally, \(r_{4}\) is a random number between 0 and 1.
## 2 The Proposed CPV System  
The proposed CPV system includes an array of CPV units and an additional low-cost solar cell. Each CPV unit consists of a Fresnel lens as the POE, a plano-concave lens as the SOE, and an MJ solar cell. A silicon (Si) solar cell surrounds plano-concave lenses of the CPV units to capture diffuse solar radiation. Each CPV unit highly concentrates and uniformly distributes direct solar radiation on its MJ solar cell through its POE and SOE, whereas the Si solar cell captures diffuse solar radiation.  
The design and layout of the proposed system are shown in Figures 1 and 2, respectively.  
Figure 1: Design of the proposed concentrator photovoltaic (CPV) system. Si, silicon.
# Fault Diagnosis of a Reconfigurable Crawling-Rolling Robot Based on Support Vector Machines
## 5 Experimental Setup  
The experiment conducted in this paper uses statistical features that provide critical information needed to classify between faulty and non-faulty locomotion modes of our Scorpio robot. The nine most commonly used gaits in our Scorpio robot were chosen based on a set of pre-defined urban reconnaissance use cases. The identified gaits were tested across three different speeds identified for the same set of use cases. The IMU data is obtained for all nine gaits and all three speeds. When the program first starts running, it takes some readings to establish a baseline orientation, during which it expects both the roll and the pitch of the sensors to be zero. After performing the startup routine, the program constantly takes readings from the gyro, accelerometer, and magnetometer, and calculates an estimate of the robot's data. The speed is varied by changing the degree of rotation per period in the servo motor. We used a total of eight statistical features, listed in Section 3, to perform the fault classification. Scatter plots illustrating the statistical features obtained from the IMU data, for backward locomotion gait at a speed of three degrees per period, are shown in Figure 6a, and curve left locomotion at a speed of six degrees per period is shown in Figure 6b.  
Figure 6: _Cont._Figure 6: _Cont_.  
The plot in Figure 6 illustrates the sample training sets used for fault classification. The plot denotes a clear separation between the faulty and non-faulty classes. The SVM classifier, when trained with the training data obtained from each gait, provides a useful fault classification for the corresponding locomotion gait. For calibration purposes, and due to the diverse nature of the locomotion gaits considered in this study, the feature sampling of IMU data were done separately from each locomotion gait. For example, in the backwards locomotion gait shown in Figure 6a, the IMU data were sampled in a period between 5s and 10 s, whereas for the curve left locomotion gait, the IMU data were sampled in a period between 7s and 20 s. We ensured that the IMU data, sampled for both faulty and non-faulty classes, were collected during the same period, which accounts for the calibration. In all gaits, except rolling, the faulty IMU data, used as training and testing classes, was obtained by deactivating the following motors: C\({}_{2}\) (Back-Left Cox), C\({}_{3}\) (Back-Right Cox), C\({}_{4}\) (Front-Right Cox). To consider the rolling motion, the fault data is obtained by simulating a disturbance in two of the fem motors, F\({}_{1}\) (Front-Left Fem) and F\({}_{4}\) (Front-Right Fem). Each disturbance is sufficiently large enough to prevent rolling motion. These faults were particularly considered for this study due their high degree of occurrence within the considered use cases. Future work is set to expand the scope of the faults considered for the study. The non-faulty IMU data are obtained when all motors, across the nine locomotion modes, are working properly.
# Wireless Sensor Networks for Big Data Systems
## 4 Open Issues
### Network Architecture  
In terms of WSN for big data systems, mobile sink [61] and clustering [62] are important research challenges in collecting and aggregating the data. However, these schemes need to be extended to significantly reduce energy consumption and provide real-time communications due to a large amount of data. Examples include [63; 64] that propose a data aggregation algorithm based on principal component analysis (PCA) to achieve minimal energy consumption and delay, which can be executed in the cluster head (CH).  
In addition, as new applications emerge, typical WSN is evolved to support new types of sensor networks: urban sensor networks, flying sensor networks, wireless underground sensor networks, and wireless underwater sensor networks, etc. To handle big data in these new networks, the current network architecture that usually employs either mobile sink or clustering need to be extended or modified to collect the sensed data efficiently in several areas. For example, mobility patterns in these networks are completely different from typical WSN: connectivity between source and destination is not always guaranteed. Thus, feasible mobility prediction and clustering algorithms should be developed according to the additional constraints of each network. Additionally, clustering algorithms need to be revised in the network environment, while considering connectivity and mobility. Moreover, regardless of the type of the network, energy efficiency is the most critical issue to be addressed. Therefore, network architecture and communication protocols need to meet the above requirements. Finally, deploymentissues of controlling the topology in new types of WSN should be mentioned. In addition, overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Thus, a data fusion algorithm needs to be supported, as studied in [65]. The authors introduce data fusion model based on an improved radial basis function (RBF) network. Additionally, different data packets set different transmission intervals and properties according to the dynamics of topology under the clustering architecture.
## 5 Model Simulation Analysis  
### Evolution Path Simulation  
Conducting simulation experiments allows for a more intuitive analysis of the strategy choices of the three subjects in a certain initial state. This study set two initial parameters corresponding to stable evolution conditions that only satisfy \(\rm E_{5}\) and simultaneously satisfy \(\rm E_{4}\) and \(\rm E_{5}\). Respectively, array 1 is as follows: \(R_{\rm c}=150\); \(C_{\rm co}=70\); \(C_{\rm cf}=30\); \(C_{\rm cfs}=20\); \(C_{\rm cfr}=30\); \(F_{\rm c}=20\); \(C_{\rm p}=20\); \(F_{\rm p}=25\); \(M_{\rm p}=15\); \(C_{\rm g}=15\); \(T_{\rm g}=40\), and array 2 is as follows: \(R_{\rm c}=150\); \(C_{\rm co}=120\); \(C_{\rm cf}=20\); \(C_{\rm cfs}=10\); \(C_{\rm cfr}=50\); \(F_{\rm c}=20\); \(C_{\rm p}=10\); \(F_{\rm p}=20\); \(M_{\rm p}=10\); \(C_{\rm g}=15\); \(T_{\rm g}=40\). Substitute the arrays into the game model and use MATLAB R2018a software to obtain the evolution path, as shown in Figure 5.  
The simulation results show that (1) is consistent with lemma 7; \(\rm E_{5}\) (1, 1, 0) is the only deterministic evolutionary strategy, and \(\rm E_{4}\) (0, 0, 1) is a potential ESS; (2) The evolution of the system is affected by multiple variables, such as the revenue, speculative costs,  
Figure 5: The evolution result of the replicator dynamic system. (**a**) Array 1 evolves 50 times; (**b**) Array 2 evolves 50 times.  
collusive costs, rewards, fines, penalties, and others. When the initial values of these variables change, the evolution results of the tripartite game will also change.  
The simulation analysis of the above different initial parameters verifies the effectiveness of qualitative analysis and further demonstrates that the numerical relationship between variables affects the evolutionary stability strategy. In order to avoid collusion detection of digital works and the influx of pirated NFT digital works into the market and infringement of others' rights, regulators should strengthen information technology construction, conduct in-depth inspections of the interests of digital content creators and NFT digital publishing platforms, and ensure that the sum of fines and rewards for all parties exceeds the sum of speculative profits for all parties.