esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 4 Active Device Technology S-Parameter Extraction
### Reflection Coefficient and Matching Conditions  
The load reflection coefficient, \(\Gamma_{opt}\), plays a crucial role in determining the efficiency of power transfer and noise performance of a pHEMT device. This is distinct from the conjugate reflection coefficient of the input return loss, \(S_{11}\). The optimal power transfer occurs when:  
\[\Gamma_{opt}=\frac{Z_{L}-Z_{0}}{Z_{L}+Z_{0}} \tag{30}\]  
Figure 5: Curves of the transconductance for a \(2\times 50\) μm InGaAs pHEMT ([15,18]).  
Figure 6: Curves of the transconductance for a \(4\times 50\) μm InGaAs pHEMT ([15,18]).  
where \(Z_{L}\) is the load impedance and \(Z_{0}\) is the characteristic impedance of the system.  
By carefully selecting the appropriate transistor, the difference between \(\Gamma_{opt}\) and \(S_{11}\) can be minimised, improving the output power while maintaining low noise levels. The noise figure of the amplifier can be expressed as:  
\[F=F_{min}+4R_{n}\frac{|\Gamma_{S}-\Gamma_{opt}|^{2}}{(1-|\Gamma_{opt}|^{2})|1+ \Gamma_{S}|^{2}} \tag{31}\]  
where \(F_{min}\) is the minimum noise figure, \(R_{n}\) is the equivalent noise resistance, and \(\Gamma_{S}\) is the source reflection coefficient.
## 2 Materials and Methods
### Methods  
2.3.1. Box-Behnken Experimental Design and Statistical Analyses  
The investigation was performed on the basis of the Box-Behnken experiment design in the Design-Expert v13. A three-level, three-factor design of experiment (DoE) was used to determine the best combination of subcritical water extraction parameters for _Juglans regia_ L. Three levels were defined as the lowest analyzed value of the process parameter, the average and the highest value. The process temperature (F1), fraction size (F2), and process time (F3) were independent variables (three DoE factors), whereas the dependent variables were the total polyphenols content, the total flavonoids content, antioxidant activity (DPPH), and antioxidant activity (FRAP).  
The experiment consists of 15 combinations, including three center points to estimate the pure error, and was carried out in randomized order. Coded and real values of process parameters of each set of variables according to the design of experiment are presented in Table 1. Coded values represent the minimum value of a variable (coded value \(-\)1), average (coded value 0) and maximum (coded value 1).  
The experimental data were assessed via analysis of variance (ANOVA). The statistical significations of the regression coefficients were verified with an F-test, and \(p\)-values of less than 0.05 were assumed as significant.  
The optimal extraction conditions were estimated through Derringer's desirability prediction tool, aiming at a maximum attainable response for each independent factor. The validity of the developed model was assessed by comparing the experimental with predicted values.  
Figure 1: The raw material for testing after pre-drying process.
## 3 Materials and Methods
### System Reliability  
The system reliability is the probability that the entire system will function successfully at any time. The system performance is dependent on the performance of its sub-systems (or its components). In other words, each component contributes in one way or another to the overall system performance. Consequently, the failure of any part of the system will either partially affect the overall system performance or it will cause a failure to the system. In this study, the focus is on the components of the system that have a major contribution to the system performance, and it is either impossible or not safe to operate without them. During the design stage of any system, it is necessary to decide which components are in series and which should be in parallel. A series system means that the whole system will fail if any components fail. A parallel system fails if all of its components fail. In the truck unloading system, there are two inlet streams, each one consists of an unloading arm and a ball valve connected in series, each one of these two streams are connected in parallel. In other words, if one stream fails, the other one can still be functional. The remaining components are connected in series. For instance, if the positive displacement pump fails to absorb the fluid, then the entire system will fail. A reliability block diagram of an unloading system is shown in Figure 2.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### Basic Formulations  
The process of multi-object tracking can be formulated as follows: given a sequence of \(S\) images \(\mathcal{I}_{0},\mathcal{I}_{1},\cdots\mathcal{I}_{S-1}\), the objective is to generate a list of predictions for each frame \(\mathcal{P}_{0}\), \(\mathcal{P}_{1},\cdots\mathcal{P}_{S-1}\). Each prediction \(\mathcal{P}_{i}\) comprises a list of \(N_{i}\) objects \(\{o_{j}=(c_{j},b_{j},ID_{j})\}_{j=0}^{N_{i}-1}\) represented by their class \(c_{j}\), bounding box \(b_{j}\) and instance ID \(ID_{j}\).
## 5 Results and Analysis
### SCIM-FESS Losses
Stator copper losses at maximum three-phase current is 657 Watts and it decreases to zero when torque from the shaft of IM is removed. Rotor copper losses at maximum three-phase rotor current is 388 Watts and the combined total copper losses of the IM at maximum three-phase current and maximum operating torque is 1.045 kW. As a comparison to the loss model of the SCIM-FESS presented in this research, the performance analysis of a PMSM-FESS for the same system rating (100 kW, 5 kWh and 10-20 krpm) is presented in reference [29]. It is shown that the IM draws high inrush current at the starting but operates normally during the transition periods between charge-discharge states. For the case of PMSM, it starts normally with no major inrush currents but has higher standby losses. In contrary, IM do not use permanent magnets and has not standby electrical losses.
## 2 Antenna Design
### UWB Sensing Mode  
The proposed UWB antenna consists of a wide-slot and a CPW-fed monopole. The wideband characteristics are obtained by utilizing capacitive coupling between the two structures [32]. The circumference of the wide-slot is 1 \(\lambda_{g}\) at 3.1 GHz. Figure 3 shows the simulated reflection coefficient characteristics of the proposed UWB antenna. The \(-10\) dB reflection coefficient bandwidth is from 2.7 GHz to 12 GHz. To understand the operating principle, the surface current distributions of the UWB antenna are analyzed in Figure 4. The frequencies used for the analysis of the proposed antenna in low, mid, and high frequency bands are 3.1 GHz (first resonant frequency of the UWB antenna), 6.52 GHz (second resonant frequency of the NB Antenna #2), and 9.5 GHz (resonant frequency of the NB Antenna #3). The current is excited vertically in all three frequencies within the UWB band. In Figure 4a, 1 \(\lambda_{g}\) resonance is generated at 3.1 GHz. On the other hand, in mid and high-frequency bands (6.52 GHz and 9.5 GHz), the wide-slot is excited by higher order modes and the surface current on the slot becomes weaker.
## 1 Introduction  
Even though more than half a century has already passed since the first demonstration of laser action in ruby crystal, solid-state lasers are still a hot research topic. Their unique versatility has made them irreplaceable tools in many fields. The list of actual or potential applications is continuously being updated at a pace that is set by the rate of advancing research. This virtuous cycle is only made possible by the favorable interplay between continuous research progress, enabling new exciting applications, and the emergence of new needs from industrial markets and fundamental research fields, fostering the study of new materials, the development of new technologies, and the fast maturation of the existent ones.
## 1 Introduction
First, we describe a communication architecture to support the processing of semantic information in quasi-real-time. Second, we combine the multi-agent paradigm with semantic technologies to enable agents discovering logically or physically connected peers to collaborate with them, achieving in this way an effective intelligence distribution. Both the logical infrastructure to enable real-time management of the grid and the distribution of intelligence, operations and, therefore, management, are the crucial requirements for the successful transition from current power networks into smart grids. We illustrate these contributions with a use-case that describes how this collaboration takes place in a real-time scenario and in a non-real-time one. As we will see, the proposed architecture allows a much more dynamic handling of the network. This fact avoids complicated reconfiguration and update process (unlike in the SCADA systems nowadays where all communication and applications must be hard-coded, making updates and changes very difficult and costly). The remainder of the paper is divided as follows. Section 2 presents the smart grid scenario. Section 3 discusses related work. Section 4 details the architecture designed and the compounding parts. Section 5 introduces the use-cases worked out to evaluate our approach and, finally, Section 6 concludes and draws the avenues of future work.
## 3 Interdependent Behaviors-Based DoS Detection Method
### Traffic Analysis  
We have categorized traffic flowing into a wireless network into several patterns by analyzing the data traffic transmitted by nodes. In this research, data traffic between nodes was analyzed by applying the DEWP (Detecting Early Worm Propagation through Packet Matching) [18] research, which has been presented for DoS detection. This method detects a sudden increase in traffic created by a specific node or an abnormal amount of traffic compared to previous traffic generation. For the reason, we have compared the amount of packets sent to a specific node during defined time and the amount of packets sent out of that node. That is, we used the EWMA (Exponential Weighted Moving Average) algorithm in order to compare the amount of packets sent to a specific node during t-time and the amount of packets sent out of that node.  
The change in traffic analyzed by the EWMA algorithm is as follows:  
\[[\mathrm{K^{\prime}=\alpha\times K^{\prime}+(1-\alpha)\times K}] \tag{1}\]  
where K refers to the number of addresses (sources) of the traffic flowing into a specific node while K' means the average.  
If \(\mathrm{K>K^{\prime}}\quad\times(1+\sigma)\) is satisfied, it means that abnormal traffic has occurred, and the traffic shall be blocked; \(\sigma\) refers to the standard deviation \(\alpha\).  
As to global interdependent behaviors, nodes are extracted by studying patterns and mechanism of malicious behaviors based on the results of tracking behaviors between nodes and traffic analysis as mentioned above. This method has been known to be most effective, for its detection is based on the mechanism of a malicious code. A series of behavior rules are compared with interdependent system requests and the result is used for allowing a service or a system, which is applied to blocking, interconnection, engine control, and so on.  
This method applies rules and can detect new patterns based on the previous mechanism but it requires constant monitoring for infection between nodes due to host-based detection.  
Figure 1 shows the structure of the DoS detection system based on global interdependent behaviors. The _ad-hoc_ sensor network monitors data and traffic information at each BS while BSM, which monitors BS, analyzes all the data related with behaviors and traffic in order to send them to the rule center. Based on the data on the conditions of a node, rules and policies are applied so as to detect an abnormal sensor node and report it to a manager as a precursor symptom. Finally, the node manager confirms the abnormal sensor and removes it from the _ad-hoc_ network.  
In this research, five requirements presented by the requirements of an effective detection system [19] are considered.  
* Multiple detection mechanisms
* Attack Coverage
* Granularity of Attack detection
* Consolidation of alarms
* Response Action
## 3 Results and Discussion
### Advantages and Potential Limitations
Of course, slight differences in the derivation of SWE might occur considering these two relatively new sensors, however, as the sensors are not installed at the exact same place and are located up to 25 m from each other and have a distance (up to 150 m) to the manual measurements. The main differences might originate in different amounts of snow at each location, e.g., due to different wind effects and the different physical principles of the measurements. Regarding the spatially distributed components EO and the hydrological model, it is another big advantage that both can also be potentially used worldwide and are often free of charge as, e.g., the Sentinel data. Of course, remote sensing products might be restricted in temporal and spatial resolution and face, depending on the wavelength and if the systems are active or passive, different limitations as, e.g., cloud cover or foreshortening effects. Therefore, it is often more difficult to apply EO in mountainous terrain. The applied hydrological model PROMET was already tested and validated for various applications for small and large catchments (e.g., [22; 32; 33; 47]) and also globally (e.g., [48]) in different temporal and spatial resolutions. Although there are a few limitations in the model setup like, for example, the difficulty of implementing small-scale features regarding snow variability or the run-off generation in extreme alpine surroundings, the modelled output provides very good results for different scales, also in case of sparse input data as it is the case in Newfoundland. Especially in those remote areas with sparse data, real-time information and forecasts of run-off, fresh water availability, SWE, snow extent and the snowmelt onset can be significantly improved. Up to now, a limitation in our hydrological model setup is the lake ice formation. In general, SWE is calculated from the snow cover on the ground. However, until now, snow accumulation on top of frozen lakes is not implemented, although this SWE also contributes to the run-off after the onset of snow- and ice-melt. Moreover, we aim to describe river ice formation as an additional feature in the model since it can build up to ice dams with a subsequent banking up of water masses. These two model improvements will be implemented in the future, e.g. as suggested in [49; 50]. The big advantage of the entire combined SnowSense service is that it picks up the advantages of all three components to deliver a reliable, assimilated product of snow and hydrology. In case data of one component is missing, the service can still rely on two other pillars and is therefore less vulnerable to data losses or other failures. The service can be used as entire combined system relying on the three pillars in situ measurements, EO, and hydrological modelling, but each of these pillars can also be applied as a modular stand-alone solution if desired. It therefore enhances and combines existing solutions and is, due to its modularity, a customer friendly approach.
# Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS
## 3 Methods  
### Data Distribution Service  
In a distributed system environment, a DDS [32] is data-centric. All the participating AMRs in the DDS domain are allowed to be data publishers and subscribers, as shown in Figure 1. Other participating AMRs can read the data written by any of the AMRs in the same DDS domain. In the DDS, the standard data type is described by a topic name and value pair _topic = <name, value>_. All the data are virtually stored in a global data space. The global data space may be in a distributed set of hardware units or the local memory of individual robots. A topic is transmitted in a multicast manner to share data reliably. To identify which topics are involved, an identifier, e.g., a namespace, is prefixed to the topic name in the following style: _/namespace/topic_. A namespace corresponds to the name of an AMR.  
In this paper, a single centralized traffic management server is applied to set the configurations of AMRs. To this end, the DDS enables the traffic management server to simultaneously send static or user-defined maps as topics to multiple AMRs. In contrast to AMRs that pre-download maps, the participating AMRs in the DDS can share maps via the traffic management. Moreover, AMRs can communicate with each other to share information about their positions or occupied regions. All the AMRs send their position information to the single traffic management server with the DDS topic in real time to avoid collision. A ticket that includes the robot and region information is shared bidirectionally between the AMRs and the traffic management server to control the region occupation.
# Prediction of Damage to the Vehicle Underbody due to Stone Chipping
## 2 Definition and Prediction of Stone Chipping
### Numerical Definition of Stone Chipping Based on Vehicle Geometry
In the situation addressed in this study, the stone escapes tangentially to the tire; however, the elevation angle differs depending on the absolute speed and direction, as in Equation (8), as well as the stone masses. The vertical component of the stone speed, \(V_{abs}k\), is given by Equation (10). \[V_{abs}=(V_{abs}i,\ V_{abs}j,V_{abs}k), \tag{7}\]  
\[V_{abs}i=(V_{\Delta}+V_{\Delta ratio})\cos\alpha\cdot\sin\beta. \tag{8}\]  
\[V_{abs}j=-V_{a}+(V_{\Delta}+V_{\Delta ratio})\cos\alpha\cdot\cos\beta. \tag{9}\]  
\[V_{abs}k=(V_{\Delta}+V_{\Delta ratio})\sin\alpha. \tag{10}\]  
Here, the flight time of the stone after passing below the tire contact surface varies depending on the height, \(H\), of the chassis above ground. The driving speed and gravitational acceleration, \(g\), of the vehicle can be expressed as in Equation (11) by applying \(V_{abs}k\), where \(\alpha\) is the tangential angle of the dropped object at the escape point. As the flight time is short enough, the drag force of air is ignored. \[\frac{1}{2}gt^{2}+V_{abs}kt-(H-R(1-\cos\alpha))=0. \tag{11}\]  
Figure 2:0 Geometry of the tangential angle and target position with respect to the escape point of the dropped object. Using Equations (1)-(11), the chipping of a single random stone can be mathematically proven, and its range can be set to determine the critical zone. However, although it may be meaningful to define chipping for a single random stone in terms of the potential energy, a statistical estimation model is required to identify the impact area.
## 4 Further Analysis with Discussions and Implications
### Angular Analysis and the Inferences of Fact
#### 4.2.3 Analysing the Technological Domains Addressed by the Technological Powers
These IPCs reveal the background business logics of the high-tech industries in these countries. Without exception, all of them address the manufacturing and testing technologies for semiconductors as well as those for large dynamo-electric (motor) units. However, as both JPN and KOR have larger variances than GER (see the magnitudes instead of the directions of the arrows), it is reasonable to assume that they are focused on a larger number of other fields at the subclass level for, e.g., the optical, semiconductor and motor technologies. * _Analysing the industrial properties for the countries in group (iii): using TWN as an example_. For the rest of the countries in group (iii), as discussed previously, their high-tech industrial interests are quite diversified. As an example, the market and industry in TWN are studied. For this analytical purpose, some main IPC codes can also be identified along with the direction of the arrow for TWN. These main IPC codes include the following: H05K (printed circuits; casings or construction details of electric apparatuses; manufacture of assemblages of electrical components, e.g., mother boards), G02F (devices or arrangements, the optical operation of which is modified by changing the optical properties of the medium of the devices or arrangements for the control of the intensity, color, phase, polarization or direction of light, e.g., optical logic elements or LCD manufacturing) and H01L (as stated previously). In other words, TWN addresses its technological niches in some hardware manufacturing fields and the manufacturing processes of, e.g., PCB, optical elements and semiconductors. This observation also applies to the other two countries in this outlier group (i.e., CAN and GBR). To provide a short summary, in this section, a novel visualised analysis for the field of patent data mining termed the 'angular-(based) analysis method' is presented in order to unveil critical further knowledge regarding the world's technological powers based on the results from using the correlation matrix analytical method and the PCA modelling discussed in Sections 3.4 and 4.1. This analysis method discovered and contributed several extensive yet insightful points for strategic settings in the studied high-tech industries worldwide. As this method was proven effective in terms of revealing valuable knowledge, further analyses can be conducted by applying this method to the patent databases of various countries in future periodical works.
## 5 Discussion  
First, the proposed method is tested on a toy dataset. The proposed method gives better class separation for six randomly created noisy classes. Furthermore, the effectiveness of the proposed method is shown on two different hyperspectral datasets by applying classification. When the proposed method is evaluated among all linear manifold learning algorithms used, 1.6% improvement is achieved for the Indian Pines dataset and 1.4% for the KSC dataset. Improvements are observed in many classes of both datasets.  
Weight matrices are extracted to observe the changes made by the proposed method on the algorithm for the HSI data. The value increment around diagonals and the value decrement on off-diagonals for weight matrices can be an indicator to get better classification results. However, obtaining a weight matrix is a mid-step and occurs before the construction of the embeddings. Therefore, it does not guarantee that the classification success rates always increase.  
The band information and correlation matrices are extracted using the embeddings for both datasets. In the band analysis, it is shown that the information for Bands 1 and 2 is improved for the \"difficult classes\". Indeed, some bands can be discriminative only for some classes. In this context, the features belonging to some classes that have no information on Bands 11 and 12 are greatly improved with the proposed method. In addition, the correlation matrices for the bands can give information about distinguishability of samples. A remarkable point is that a general decrease in the correlation values for non-intra-class samples is observed. This can, likewise, have a positive effect on classification success, because it shows that the effect of irrelevant class samples is decreased. Furthermore, some correlations of off-diagonal, i.e., correlations belonging to non-intra-class samples lose their significance (greater \(p\)). This can be evaluated as a meaningless relation for non-intra-class samples and supports the supervised approach.