esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Copper Determination by Flame Atomic Absorption Spectrometry--FAAS  
The results of copper content in PETE-Cu(\({}^{(0)}\)) composite samples determined by the FAAS method are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**PETE-Cu(\({}^{(0)}\) a**} \\ \cline{4-9}  & \multicolumn{2}{c}{**PETE**} & \multicolumn{6}{c}{**PETE-Cu(\({}^{(0)}\)-1 a**} \\ \cline{4-9}  & & & \multicolumn{6}{c}{**PETE-Cu(\({}^{(0)}\)-1 a**} \\ \cline{4-9}  & & & \multicolumn{6}{c}{**PETE-Cu(\({}^{(0)}\)-1.1 c**} & \multicolumn{6}{c}{**PETE-Cu(\({}^{(0)}\)-1.2 c**} \\ \hline
**Element** & **C** & **O** & **C** & **O** & **Cu** & **C** & **O** & **Cu** \\ \hline \% b  & 62.1 & 37.5 & 17.70 & 5.93 & 74.78 & 22.83 & 6.14 & 70.88 \\ \hline Std. deviation & 0.16 & 0.16 & 1.70 & 0.45 & 0.26 & 1.47 & 0.71 & 2.20 \\ \hline \hline \end{tabular}  
* Assignments: PETE-Cu(\({}^{(0)}\))-1—one site a PETE sample copper sputtering deposition: PETE-Cu(\({}^{(0)}\))-1.1—upper site of the sample analysis; PETE-Cu(\({}^{(0)}\))-1.2—lower site of the sample analysis; PETE-Cu(\({}^{(0)}\))-2.2: two sites of a PETE sample copper sputtering deposition; PETE-Cu(\({}^{(0)}\))-2.1—upper site of the sample analysis; PETE-Cu(\({}^{(0)}\))-2.2—lower site of the sample analysis; b: All results in percent by weight [%]. c: Mean value of 3 measurements; d: Mean value of 11 measurements.  
\end{table}
Table 1: The EDS analysis of elemental composition of PETE (C, O) and PETE-Cu(\({}^{(0)}\) (C, O, Cu) composites.  
The results inform about the whole copper concentration in starting PETE knitwear fibers and PETE-Cu\({}^{(0)}\) composites. These revealed that composites are practically null of copper in the starting PETE (0.003: %; _m_: 0.0004) with only 0.7% to 1.4% of copper in PETE-Cu\({}^{(0)}\) composites; in detail there was 0.67% (0.105 molal) of copper in one side of the sputtered knitwear fibers PETE-Cu\({}^{(0)}\)-1 and twice as high a copper concentration in the case of two-sided sputtering mode PETE-Cu\({}^{(0)}\)-2 (140%; 0.221 molal).  
Taking into account the results of copper determination in PETE-Cu\({}^{(0)}\)-2.2 by EDS and in PETE-Cu\({}^{(0)}\)-2 by FAAS it can be assumed that during sputtering of the second side of PETE-Cu\({}^{(0)}\)-2 (PETE-Cu\({}^{(0)}\)-2 \(\rightarrow\) PETE-Cu\({}^{(0)}\)-2.2) the diffusion of copper from the sputtering surface (PETE-Cu\({}^{(0)}\)-2.2) into bulk of polymer occurs, and there is copper diffusion in the polymer, analogous to earlier works on PET metallization [123; 124; 125].
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 5 Optimization Problem and Algorithm  
### Problem Formulation: Throughput Maximization  
The optimal time allocation to maximize the data rate of \(U_{m}\), while guaranteeing the minimum outage requirement \(\varepsilon\) of \(U_{o}\), can be obtained by solving the following problem.  
\[\max_{\lambda} R_{U_{m}}\] (14a) s.t. \[P_{out}(R_{U_{o}}|\eta)\leq\varepsilon \tag{14b}\] \[-1\leq\lambda\leq 1. \tag{14c}\]  
The optimal time allocation for the adaptive parameter \(\lambda\) can be achieved by a one-dimensional searching method, such as a bisection method [34]. Similar to the bisection method, an optimal time allocation algorithm is carried out in two steps. First, it determines an initial time allocation \(\lambda_{i}\) and a search interval \([a,b]\) between \(-1\) and \(1\). Second, with \(\lambda_{i}\) and \([a,b]\) found in the first step, an optimal time allocation \(\lambda_{opt}\) is defined, such that it maximizes \(R_{U_{m}}\) while guaranteeing an outage probability constraint in (14b). Details are in Algorithms 1 and 2 for each step, respectively.  
```
1:\(a=-1,b=1\)
2:Calculate \(\lambda_{i}=\frac{(a+b)}{2}\)
3:Check the validity of \(\lambda_{i}\) by (14b)
4:if\(\lambda\) is valid then
5: Save \(\lambda_{i}\) and \([a,b]\)
6: Break
7:else
8:\(b=\lambda_{i}\) if \(R_{(mBS+sBS),UAV}^{1^{st}}>R_{UAV,U_{o}}^{2^{nd}}\)
9:\(a=\lambda_{i}\) otherwise
10:endif
11: Go to line 1
```  
**Algorithm 1** Initialize \(\lambda_{i}\) and \([a,b]\)
## 2 PMSM Modeling  
To simplify the analysis, the following assumptions are made when developing the mathematical model of a PMSM [20]:  
1. Stator core saturation is ignored, assuming the magnetic circuit is linear and inductance parameters remain constant.
2. Core eddy current and hysteresis losses are neglected.
3. The electrical conductivity of the rotor's permanent magnet material is assumed to be zero.
4. There are no damper windings on the rotor.
5. The currents in the stator are three-phase balanced sine waves.  
The mathematical model of the PMSM in the \(d-q\) axis coordinate system is as follows:  
\[\left\{\begin{array}{l}v_{d}=R_{id}+L_{d}\frac{\dot{d}_{d}}{dt}-p\omega_{m}L_ {q}\dot{i}_{q}\\ v_{q}=R_{iq}+L_{q}\frac{\dot{d}_{q}}{dt}+p\omega_{m}L_{d}\dot{i}_{d}+p\omega_{m }\psi_{f}\end{array}\right., \tag{1}\]  
where the symbols represent the following:  
* \(v_{d}\) and \(v_{q}\) represent the voltages along the \(d\)-axis and \(q\)-axis, respectively.
* \(R\) denotes the stator resistance.
* \(i_{d}\) and \(i_{q}\) indicate the currents along the \(d\)-axis and \(q\)-axis, respectively.
* \(L_{d}\) and \(L_{q}\) signify the inductances along the \(d\)-axis and \(q\)-axis, respectively.
* \(p\) is the number of pole pairs.
* \(\omega_{m}\) is the motor's mechanical angular velocity.
* \(\psi_{f}\) is the flux linkage of the permanent magnet.  
For a surface-mounted PMSM with \(L_{d}=L_{q}=L_{s}\), the torque equation \(T_{e}\) is given by  
\[T_{e}=\frac{3}{2}p\,\psi_{f}\,i_{q}\,. \tag{2}\]  
The motion equations of the PMSM [20] can be expressed as follows:  
\[\frac{d\omega_{m}}{dt}=\frac{3p\psi_{f}}{2J}\,i_{q}-\frac{T_{L}}{J}-\frac{B}{ J}\,\omega_{m}, \tag{3}\]  
where \(B\) signifies the viscous friction coefficient, \(J\) represents the moment of inertia, and \(T_{L}\) indicates the load torque. Taking into account the variations present in Equation (3), Equation (4) can be obtained as follows:  
\[\begin{split}\frac{d\omega_{m}}{dt}&=-\bigg{(} \frac{B}{J}+\Delta a\bigg{)}\omega_{m}-\bigg{(}\frac{T_{L}}{J}+\Delta c\bigg{)} +\bigg{(}\frac{3p\psi_{f}}{2J}i_{q}-\Delta b\bigg{)}i_{q}\\ &=a\omega_{m}+bu-d,\end{split} \tag{4}\]  
where \(a=-B/J\), \(b=3p\psi_{f}/2J\), and \(u=i_{q}\) represent the control laws to be designed. The variations in motor parameters are denoted by \(\Delta a,\Delta b\), and \(\Delta c\), while \(d\) represents the disturbances due to parameter and load changes as follows:  
\[d=\Delta a\omega_{m}+\Delta bu+\Delta c+\frac{T_{L}}{J}. \tag{5}\]
# Spectrogram Classification Using Dissimilarity Space
## 4 Clustering
### K-Means  
K-means is a popular clustering algorithm that partitions a set of patterns into \(k\) clusters by assigning each observation to the cluster with the nearest centroid, or mean vector. There are several versions of this algorithm. In this study, the default implementation (with the Euclidean distance metric) in the MATLAB Statistics and Machine Learning Toolbox was applied. The standard k-means algorithm cycles through the following steps:  
1. Choose \(k\) initial cluster centers (centroids) according to the k-means++ variation detailed below.
2. Compute point-to-cluster-centroid distances of all observations to each centroid.
3. Assign each observation to the cluster with the closest centroid.
4. Compute the average of the observations in each cluster to obtain \(k\) new centroids.
5. Repeat steps 2 through 4 until cluster assignments no longer change (i.e., until the algorithm converges) or until the maximum number of iterations is reached.  
Figure 4: A sample of clusters found from unlabeled data: on the left the original 2D data, on the right clustered data, where different colors denote different clusters.  
The k-means++ variation [44] employs a heuristic to find the initial centroids:  
1. Choose one center uniformly at random from among the data points.
2. For each data point \(x\), compute \(d(x)\), the distance between x and the nearest center that has already been chosen.
3. Choose one new data point at random as a new center, using a weighted probability distribution where a point \(x\) is chosen with probability proportional to \(d(x)^{2}\).
4. Repeat Steps 2 and 3 until \(k\) centers have been chosen.
###### Abstract  
The transition from 5G to 6G wireless systems introduces new challenges at the physical layer, including the need for higher frequency operations, massive MIMO deployment, advanced beamforming techniques, and sustainable energy harvesting mechanisms. A plethora of feature articles, review and white papers, and roadmaps elaborate on the perspectives and research challenges of wireless systems, in general, including both unified physical and cyber space. Hence, this paper presents a comprehensive review of the technological challenges and recent advancements in wireless communication hardware that underpin the development of next-generation networks, particularly 6G. Emphasizing the physical layer, the study explores critical enabling technologies including beamforming, massive MIMO, reconfigurable intelligent surfaces (RIS), millimeter-wave (mmWave) and terahertz (THz) communications, wireless power transfer, and energy harvesting. These technologies are analyzed in terms of their functional roles, implementation challenges, and integration into future wireless infrastructure. Beyond traditional physical layer components, the paper also discusses the role of reconfigurable RF front-ends, innovative antenna architectures, and user-end devices that contribute to the adaptability and efficiency of emerging communication systems. In addition, the inclusion of application-driven paradigms such as digital twins highlights how new use cases are shaping design requirements and pushing the boundaries of hardware capabilities. By linking foundational physical-layer technologies with evolving application demands, this work provides a holistic perspective aimed at guiding future research directions and informing the design of scalable, energy-efficient, and resilient wireless communication platforms for the Future Internet. Specifically, we first try to identify the demands and, in turn, explore existing or emerging technologies that have the potential to meet these needs. Especially, there will be an extended reference about the state-of-the-art antennas for massive MIMO terrestrial and non-terrestrial networks.  
Internet of Things; multifunctional antennas; beamforming networks; passive components; 5G systems +
Footnote †: journal: Future Internet
## 4 Conclusions  
Our research showed some significant differences between 17 barley and 8 oat cultivars produced under organic management system in the phenolic and carotenoid compounds content and mycotoxins contamination in grains. Among the barley cultivars studied, the grains of SU Lolek, Rubaszek and Podarek were richest in polyphenols, including phenolic acids. The grain of Soldo cultivar accumulated the highest content of carotenoids (lutein and \(\beta\)-carotene), but at the same time most of the mycotoxins identified within the study were found in the grains of this cultivar. In the case of oat, the cultivars Harnas, Navigator and Arden stood out from the rest in terms of the highest content of phenolic acids and total polyphenols in the grain, while Pascal and Amant grains were richest in carotenoids. Among all the oat cultivars tested, only Amant grain was free from the studied mycotoxins. These findings, showing a more favorable chemical composition of some cultivars compared to the others, and allowing for initial identification of barley and oat cultivars with the highest quality traits, may be of importance for the organic producers as well as for the consumers, who increasingly search for high quality organic foods. Moreover, these results may be a premise for further research towards developing cereal seed production aimed at organic producers. The next important step of research could also be to define and promote the best storage and processing technologies that would enable the preservation of high quality and safety attributes of the examined organic cereals in food products.
# PAM-\(N\)--Fundamental Limits in Chromatic Dispersive-Uncompensated Channels
## 2 General Theory
This work has the goal of identifying and characterizing this maximum failure point. To find the fundamental limit, which is determined by the dispersion, the measurement has to be taken in a noiseless environment. Clearly, any addition of noise will decrease the maximum distance \(\zeta_{\max}\) that the channel can carry information. The system schematic is presented in Figure 1. The system consists of a laser source, which is modulated externally or directly (if the laser is modulated directly at high frequencies, chirp may be introduced, which may worsen the signal's distortion), by an electrical signal. The modulated light is transmitted through the fiber, which consists of a spectral filter, which describes the channel's spectral allocation. It has been shown [16] that in the case of on-off keying (OOK), i.e., PAM-2, the maximum distance \(\zeta_{\max}\) is independent of the shape of the low pass filter (LPF). In these cases, it has been shown that \(\zeta_{\max,N=2}\cong\pi^{-1}\). This result is consistent with the coherent M-ary quadrature amplitude modulation (QAM-M) result [17] (for \(M=4\)):  
\[\frac{\partial A}{\partial\zeta}=i\frac{1}{2}\frac{\partial^{2}A}{\partial \tau^{2}} \tag{2}\]0  
\[\frac{\partial A}{\partial\zeta}=i\frac{1}{2}\frac{\partial^{2}A}{\partial \tau^{2}} \tag{2}\]1 System schematic. However, because there are multiple power levels in the PAM-\(N\) case (rather than only two, as in the OOK one), there are three main differences, which substantially increase the problem's complexity. The first difference is that every level widens differently (unlike the coherent QAM cases), and therefore, the different eyes narrow differently. Second, every level is most affected by different sequences. Third, unlike the OOK case, where \(\zeta_{\rm max}\) is almost unaffected by the shape of the LPF in the PAM-\(N\) scenario, the filter's shape is essential in determining \(\zeta_{\rm max}\).
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Benchmark Evaluation Metrics for Multi-Object Tracking  
This paper uses six evaluation metrics [19] to assess the performance of the tracking algorithm.  
1. FP (False Positive): False alarms indicating that a tracking trajectory was generated when there was no real object.
2. FN (False Negative): Missed detections indicating that a real object was present but no tracking trajectory was generated.
3. IDF1 (IDF1 Score): This combines the precision and recall of identifying objects across frames and measures the tracker's ability to maintain consistent object identities throughout the video sequence.
4. IDs (ID Switches): The number of times the target ID changes, primarily measuring the consistency of the target tracking trajectory.
5. MOTA (Multiple Object Tracking Accuracy): Tracking accuracy, measuring the performance of the tracking algorithm in detecting objects and maintaining tracking trajectories.
6. HOTA (Higher-Order Tracking Accuracy): This is a metric used to evaluate the performance of multi-object tracking. Unlike traditional metrics such as MOTA and IDF1, HOTA aims to comprehensively reflect the tracking algorithm's capabilities in localization and identification. It considers several higher-order factors, such as the accuracy of objects, the consistency of relative positions, and the complexity of maintaining target identities.
## 5 Protocol Performance Evaluation
### Simulation Setup Description
of this standard is the achievement of low energy consumption and secure networking although the rates of transmission are not high. Sensor node modules based on either of these standards can be used in order to perform experiments. However, the existence of hardware dependant particularities in such sensor modules influences the implementation of routing protocols. For instance, the antennas used in the sensor boards have a non perfect omnidirectional radiation pattern. The effect of this issue must be thoroughly investigated in order to bridge the discrepancies between the spherical radio model adopted in simulation tests and the physical reality of radio signals. An analytical research work on this topic has been performed by [PERSON] _et al_. [27].
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 2 Methodology  
This section covers a detailed explanation of the methodology and is depicted in Figure 2a,b. The indoor positioning system is developed by calculating the distance of the remote object using UWB technology and WiFi for wireless communication. We built the hardware gadget using the micro-controller as a master to control all the UWB and WiFi communication.  
Figure 1: TDOA scheme.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System
### aPad
#### 2.2.3 Sensors  
For localisation and positioning, the aPad uses an IMU and a u-blox C94-M8P GPS module with the capability of using the Real-time kinematic (RTK) positioning technique. This technique can greatly enhance the precision of satellite-based position data using signals from a reference station positioned at a known location. In the absence of the reference station, it acts like a classical GPS unit.  
As part of its docking system, the aPad uses both the infra-red and the RGB camera capabilities of the Microsoft Kinect sensor. The Kinect sensor is mounted on the top of the aPad in a sealed vertical tube set upon a servo motor powered pan mechanism. Using the pan mechanism, the Kinect can turn up to 270 degrees, thus offering view on all four aPad docking stations. This setup is shown in Figure 10.  
Figure 9: X-shaped thruster configuration present on the aPad, enabling omnidirectional motion in the horizontal plane, [14].  
The aPad has a voltage and current sensor on its power management board which provides information about the vehicle's battery state.
## 1 Introduction
In this study, a feature only sensitive to plate thickness is proposed through electromagnetic waves theory. The electromagnetic waves theory has been used for PECT analysis in some researches. [PERSON] [8] propose features, such as the peak value, TZC, and the LOI, by considering the wave produced by the square-wave pulse was a plane wave. However, the model [8] was imprecise, because the wave produced by the square-wave pulse could be regarded as a plane wave only if the transmission coil radius or plate thickness was sufficiently large [17; 18]. Then, Dodd-Deeds model was used to obtain the analytical solution of the PECT problem [19; 20]. [PERSON] et al. [21] reinterpreted the model using the reflection-transmission theory, which provided it a clearly physical meaning. According to the previous studies, the reflection and transmission of electromagnetic waves in the ferromagnetic plates covered with insulations and claddings are further studied, and by decoupling the analytical solution, a feature which is only sensitive to the thickness is introduced in this study. The rest of this paper is organized as follows: Part II describes the modeling of the insulated ferromagnetic plate, and the theoretical analysis for decoupling the interference caused by claddings, insulations, and lift-offs. In Part III, the similarity measurement based feature is presented, and in part IV, the validity of the feature is proved by experimental study. In part V, the performances of the feature are examined under various conditions. Finally, a brief conclusion is provided in part V.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 2 Theoretical Analysis  
At present, in the more commonly used analysis process, although the higher-order modes and the coupling effects between modes have a certain degree of influence on the structural buffeting response, the fundamental frequency still dominates [30]. Therefore, the coupling between modes in different directions is usually ignored without affecting the calculation accuracy. This paper will analyze the problem without considering the modal coupling effects. In addition, since the theoretical principles of the vertical, lateral and torsional buffeting responses are consistent, to simplify the derivation and demonstration process, this paper takes the vertical buffeting response as an example to study.
###### Abstract  
In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.  
blockchain; smart contract; data immutability; datastore; user access control; role-based authentication; gas consumption; IPFS
## 5 Position and Opportunities for Further Research
### Limitations  
There are several limitations of the work presented in this paper. The first limitation is concerned with the definition of CPSs and the CPS eco-system presented in Figure 1. As discussed in Section 1, there exist several definitions of CPSs. Our definition conforms to the well-known definitions of CPSs by [PERSON] and [PERSON] [1, 2] and by the International Conference on CPSs (ICPPS). The second limitation is concerned with the magnitude of the gaps identified in the state of the art. We have not performed a systematic literature review [67, 68]; instead, we have investigated the design-time support for specific properties of time-critical CPSs, including timing predictability, functional safety and security. Another limitation is the consideration of timing predictability verification only at the design-time and not at the run-time. Finally, we have answered the posed research questions after investigating the state-of-the-art solutions. However, we have not thoroughly investigated the commercial/proprietary solutions and tools, especially those that do not expose the underlying models, techniques and methods.