esa-sceva/satcom-chunk-collection huggingface dataset:
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 4 SDM Communication System Model  
This section describes the model employed to represent the communication system established over the optical channel with multiple spans. The SVD of the channel in (10) can be useful for designing a transmitter based on a precoding matrix combined with a linear receiver, as used in wireless systems [36]. However, this approach becomes unfeasible for long-haul optical communication systems, since the end-to-end channel side information needed to build the transmitter precoding matrix changes faster than the time needed for the system to collect, send, and process that information [33]. Therefore we focus on a SDM system with no channel side information that uses a linear receiver to cope with the channel impairments as shown in Figure 1[37].  
The binary data symbols, \(\underline{s}[n]=[s_{1}[n],s_{2}[n],\ldots,s_{D}[n]]^{T}\), are PAM modulated in parallel for each of the \(i\in\{1,\ldots,D\}\) optical modes using the same transmitter pulse \(P(\omega)\) to get the PAM signals, denoted by the column vector \(\underline{x}(t)=[x_{1}(t),x_{2}(t),\ldots,x_{D}(t)]^{T}\). In Figure 1, \(T\) is the transmitted symbol period and the first block represent \(D\) parallel PAM modulators working at a symbol rate (and, hence, it includes the discrete-time to continuous-time conversion). The transmitted signal is distorted by ISI and crosstalk introduced by the MIMO channel, modeled with the \(\underline{H}_{tot}(\omega)\) matrix described in Section 3. It has been shown that the noise in a MDL-impaired system is additive and spatially white [21]. Therefore, in this work we add before the receiver, as part of the channel, an additive white Gaussian noise (AWGN) vector \(\underline{u}(t)=[n_{1}(t),n_{2}(t),\ldots,n_{D}(t)]^{T}\), which, for a certain mode \(i\), has a variance equal to \(\frac{N_{0}}{2}\).  
The resulting continuous-time signal vector \(\underline{y}(t)=[y_{1}(t),y_{2}(t),\ldots,y_{D}(t)]^{T}\) is processed by a MIMO receiver to obtain the estimation of the transmitted symbols \(\underline{s}[n]\), denoted as \(\underline{s}[n]=[\hat{s}_{1}[n],\hat{s}_{2}[n],\ldots,\hat{s}_{D}[n]]^{T}\). In this work we focus on linear MIMO receivers and so, the estimation part in the receiver is depicted in Figure 1 with a generic linear filter of response \(\underline{Q}(\omega)\), which is followed by a sampler working at the symbol rate. In the following, we propose linear MIMO receiver structures based on the MMSE criterion of an estimated symbol vector.
## 4 Experimental Results and Analysis  
### Experimental Configuration  
In order to train deep learning networks, it is necessary to configure experimental environments in both hardware and software contexts. The hardware devices used in this study included 40 Intel (R) Xeon (R) E5-2630 v4 CPUs with a frequency of 2.2 GHz, 24 DDR4 memory modules that together formed a 128 GB memory space, and 3 Tesla T4 16G graphics cards. In terms of the software environment, the operating system used was Ubuntu 16.04, the main programming language was Python 3.7, the main GPU acceleration software used was CUDA11.6, the deep learning framework used was Pytorch, and the main environment management system used for programming was Anaconda.
# Assessment of the Mass and Surface Area of the Scots Pine (_Pinus sylvestris_ L.) Needles
## 3 Results
### Mass of Needles and the LAI of the Tree Stand  
The values of FMN and SN, calculated from the allometric equations for the 25 research plots covered with the 33-year-old Scots pine stand, are shown in Table 3. The FMN varied between 6458 kg\(\cdot\)ha\({}^{-1}\) and 11,102 kg\(\cdot\)ha\({}^{-1}\). The difference in the mass of needles between the extreme values was nearly two-fold.  
The mean value of the LAI for the Scots pine stand was 4.04 m\({}^{2}\cdot\)m\({}^{-2}\). In the respective research plots, the LAI was in the range of 3.2 to 5.4 m\({}^{2}\cdot\)m\({}^{-2}\) at a stock density of 2500 and 3900 trees\(\cdot\)ha\({}^{-1}\) (Table 3). Based merely on the two research plots, it could be concluded that a higher number of trees results in a higher value of the LAI, but this simple relationship is not true in this case. Individuals growing close to each other have relatively shorter crowns, which results in lower masses of needles and smaller surface areas. On the other hand, in the total area per unit of land, a small assimilation area of a single individual may, to some extent, be compensated by the number of individuals, giving, ultimately, the same or similar value of the LAI. For example, the same LAI of 3.5 m\({}^{2}\cdot\)m\({}^{-2}\) was obtained for different tree densities of 1800, 2000, 2200, and 2400 trees\(\cdot\)ha\({}^{-1}\).  
Presumably, the total surface area of needles also depends on the distance between the trees. For the same stock density of 2800 trees\(\cdot\)ha\({}^{-1}\), the value of the LAI was 4.2 or 5.1 m\({}^{2}\cdot\)m\({}^{-2}\). This is justified: densely growing trees prevent access of sunlight to each other, which leads to an early needle drop. Such differentiation results in different biocentic relationships associated with photosynthesis, carbon fixation, and growth rings.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Formula** & \(\mathbf{\beta_{0}}\) & \(\mathbf{SE_{\beta 0}}\) & \(\mathbf{p}\)-Value & \(\mathbf{CI_{\beta 0}}\) & \(\mathbf{\beta_{1}}\) & \(\mathbf{SE_{\beta 1}}\) & \(\mathbf{p}\)-Value & \(\mathbf{CI_{\beta 1}}\) & \(\mathbf{R_{d}}\) \\ \hline SN = \(\beta_{\text{0 SN}}\) + \(\beta_{\text{1 SN}}\) -DBH & \(-15.6\) & \(8.1\) & \(0.003\) & \(-32.8\) -\(1.5\) & \(2.43\) & \(0.57\) & \(<\)0.001 & 1.2–3.7 & 0.53 \\ FMN = \(\beta_{\text{0 FMN}}\) + \(\beta_{\text{1 FMN}}\) -DBH & \(-4.8\) & \(1.4\) & \(0.003\) & \(-7.7\) —\(1.9\) & \(0.627\) & \(0.098\) & \(<\)0.001 & 0.42–0.83 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Allometric equations for the mass of needles (FMN) and needle surface (SN) determination in Scots pine stands from DBH values.  
Figure 4: The relationship between: (**a**) the breast height diameter (DBH) and the fresh mass of the needles (FMN), (**b**) the breast height (DBH) and the surface area of the needles (SN).
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 2 Preliminaries  
### Number and Text Encryption Methods  
If we understand a 3D scene as a collection of numbers defining its geometry, lighting, appearance, or position (among other characteristics) stored in a data file, we can consider well-established algorithms for encrypting numerical sequences in the literature. These include symmetric encryption, such as the well-known Advanced Encryption Standard (AES) [6,7], which requires a shared secret key for both encryption and decryption. Additionally, asymmetric encryption employs key pairs (public and private), as seen in algorithms like RSA [8], allowing encrypted data to be shared in a way that only the recipient with the private key can decrypt.  
Advanced encryption algorithms are tailored to specific applications, such as stream ciphers like RC4 [9], which are used for encrypting streaming data (e.g., audio or video). Advanced block ciphers, including AES, Twofish, and Serpent, are highly resistant to attacks due to their use of various substitution, permutation, and algebraic techniques. Homomorphic encryption [10] enables computations to be performed on encrypted data without exposing it, eliminating the need for decryption (e.g., Microsoft SEAL [11]). Elliptic Curve Cryptography (ECC) [12] provides high-security encryption while requiring significantly shorter key lengths compared with RSA, making it particularly suitable for resource-constrained devices such as smartphones. Post-quantum cryptographic algorithms [13], such as Kyber [14], NTRU [15], and Dilithium [16], are specifically designed to withstand attacks from quantum computing and are essential for securing data that must remain protected over the long term.
## 2 Materials and Methods
### Characterization, Testing, and Experimentation  
The microstructure of the composite was analyzed by Scanning Electron Microscopy (SEM) (JEOL, JSM-7610 FPlus, Akishima, Japan). The existence of GNP in the composite material was analyzed using X-ray diffraction (XRD) (Bruker D8 Discover, Karlsruhe, Germany). Porosity measurement was carried out using image analysis techniques on SEM micrographs, where image processing software like ImageJ (version 1.54d) converts images into binary formats to distinguish pores from solid regions. The porosity percentage was calculated by determining the total pore area relative to the observed area. Grain size was measured using the linear intercept method (ASTM E112), where parallel lines are drawn over SEM images, and grain boundaries intersected by these lines are counted [26]. The average grain size (d) was then calculated using d = L/N, where L was the total line length and N was the number of interceptions. The compressive strength of the composite and AZ31 alloy was analyzed using a compression test by a universal testing machine (Instron 3366, Norwood, MA, USA). The specimen used for the compression test was prepared using ASTM standard E9 [27]. The length-to-diameter ratio for the compression test was taken as 0.8, as illustrated in ASTM standard E9, and the strain rate is given as 0.0005 s\({}^{-1}\)[26]. Microhardness was accomplished by using a micro-Vickers hardness tester as per ASTM Standard E384 [28]. However, the samples were required to be polished on 400-1500 grit papers along with polished via Al\({}_{2}\)O\({}_{3}\) (alumina powder). Then, the samples were dipped in a 100 mL solution (97% ethanol and 3% nitric acid) for around 10 s before measuring the microhardness of the composite [29]. The seven trails were performed on the polished surface, involving a 1 kgf load with a dwell time of 15 s, and the average value of microhardness was recorded.
###### Abstract  
Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices' accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method's superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings.  
breast cancer; computer-aided diagnosis (CAD); Breast Imaging Reporting and Data System (BI-RADS); receiver operating characteristic (ROC); tumor classification; support vector machine (SVM)
## 7 Evaluation Results
### Number of Copies  
We measured the number of copies of each bundle generated for each bundle in the network. The first generated copy was excluded from this number. As shown in Figure 23, the number of copies is equal to the number of relays between nodes minus the number of discards.  
Figure 22: Bundle arrival rate.  
The percentages of the reduction in the numbers of copies required by different routing methods in comparison to the number of copies required by \"epidemic routing\" are shown in Figure 24. The circular covered area model, the vectorial covered area model and all routing methods, except \"spray and wait routing\", showed a dramatic reduction in the number of copies in comparison to the epidemic routing. \"Motion vector routing\" generated the smallest number of copies. This was because, with this routing, the bundle arrival rate was low with the consequence that there were few needs to propagate bundles within destination areas. If we do not count the copies generated during the propagation of bundles within destination areas, our proposed routing, \"location-based routing\" and \"motion vector routing\" generated no copies, and thus, only a single bundle existed in the network. This was the reason why differences between all routing methods, other than \"spray and wait routing\", were small.
# Strength Development of Metakaolin-Based Alkalial-Activated Cement
## 3 Results and Discussion
### Strength Development with BFS as Co-Binder  
For comparison, some samples were also prepared with 10% GGBFS as a co-binder instead of MKF using otherwise the exact same slurry compositions. Several studies with low-reactive fly ash as a precursor during alkali activation have shown that the addition of increasing amounts of BFS as a co-binder can both decrease setting time and increase compressive strength [38; 39; 40]. BFS is a high-calcium aluminosilicate precursor that forms a C-A-S-H gel during alkali activation [5; 6], and it has been shown that this initial C-A-S-H gel can act as nucleation sites for subsequent N-A-S-H gel formation [39; 40]. Consequently, BFS should function as a co-binder for the low-reactive MKB as well.  
Figure 10 shows the obtained compressive strength after 1, 7, and 28 days curing time for MKB-based cement samples with 10% GGBFS of varying KOH concentrations and Si/Al ratios of 1.2 and 1.6. The same significant strength increases from 1 day to 7 days to 28 days curing time was seen as that observed when MKF was used as the co-binder (Figures 8 and 9). Consequently, GGBFS also acted well as a co-binder providing some early strength within 1 day, probably due to C-A-S-H gel formation, which led to significant subsequent strength increases due to potential K-A-S-H gel formation by the later MKB dissolution. It should be noted that it was not verified experimentally in this study that this precipitated initial binder phase was indeed C-A-S-H, but due to the high Ca content of the BFS precursors, this is a credible assumption.  
Moreover, although the highest obtained strength with GGBFS as a co-binder was similar to what was observed with MKF as a co-binder, a general trend of somewhat higher compressive strengths at 28 days with BFS as the co-binder was seen for most of the samples. This slightly increased strength may be caused by the presence of Ca\({}^{2+}\) ions in the slurry, as it has been shown that Ca\({}^{2+}\) ions in solution can improve the dissolution of quartz [33], thereby increasing the amount of dissolved Si from MKB that can contribute to the C-A-S-H or K-A-S-H formation. Consequently, the addition of BFS as a co-binder resulted in both early strength development and increased later strength development.  
Figure 9: Compressive strength of MKB-based cement samples with 10% MKF and varying KOH concentrations. Si/Al ratio = 1.2 (**left**) and Si/Al ratio = 1.6 (**right**) in the initial mix design.
# Parametric Testing of Metasurface Stirrers for Metasurface Reverberation Chambers
## 1 Introduction
### Simulation Parameters Set Up  
The simulated RC was 1.2 m long, 0.8 m wide, and 1.2 m high, with a transmitting antenna as shown in Figure 1. The dimensions of the antenna and the simulation of the reflection coefficient in free space are also depicted in Figure 1 (the orientation of the antenna had no effect on simulation results, as it had been corroborated from independent results (not shown)). In our case, the distance of the boundary of test volume to the wall of the RC was \(\lambda\) = 0.375 m, where \(\lambda\) was the wavelength of the LUF (800 MHz, in our case, where the number of modes of the RC was above 100). It was at least half a meter from all other metallic surfaces and it remained the same for all the simulated configurations, for the sake of comparison, even though for some analyzed configurations it could have been greater.  
The considered metasurfaces stirrers in the present study were based on two types of unit cells, with \"0\" and \"\(\pi\)\" phase responses to mimic the \"0\" and \"1\" elements of the 1-bit digital coding metasurface (these can be controlled using existing digital technology [14]). The details of the unit cell are described in [11]. With such unit cells, we designed three different metasurfaces, which were compared with each other and with the metallic stirrer. The metasurfaces vitrrors contained (i) two unit cells arranged alternatively (#1), (ii) two unit cells arranged in a chessboard-like manner (#2), and (iii) two unit cells in random arrangement (#3). By following the concept of coding metamaterial, case #1 was characterized by coding sequences 0101... /0101..., case #2 by coding sequences 0101... /1010..., and case #3 was the 1-bit random coding case (Figure 2).  
In order to analyze the scattering characteristic of the stirrers, we first simulated the far-field pattern of the stirrers by means of CST Microwave Studio(r) (CST China Ltd. Nanjing, China). The different stirrers generated a diffuse far-field scattering pattern, as depicted in Figure 3. The pattern obtained by full-wave simulation was in agreement with the result of the optimization algorithm [14]. In the CST software, we set the maximum number of cells per wavelength to 20, both in near- and far-form model options.  
Figure 1: Schematic diagram of the reverberation chamber (RC) with metallic stirrer or with metasurfaces stirred (**a**), three-dimensional view of the antenna (**b**), and its reflection coefficient (**c**). L = 1.2 m, W = 0.8 m and H = 1.2 m. Dimensions of the metallic stirrer were \(a_{m}\) = 0.3 m, \(b_{m}\) = 0.4 m, \(\theta_{m}\) = 120\({}^{\circ}\) and the thickness of all the metallic blades was 2 mm. The diameter of the metasurfaces stirrer was 1.2 m, and the considered dielectric layer was 0.031 m thick, with dielectric constant \(\varepsilon_{r}\) = 2.65 and loss tangent tan\(\delta\) = 0.001.  
Figure 2: Metallic stirrer (**a**), periodic metasurfaces stirrer with coding sequences 0101... /0101... (**b**) and 0101... /1010... (**c**), and 1-bit random coding metasurfaces stirrer (**d**).
## 5 Experimental Work and Results  
Cyclic dynamic generator (CDG) results were generated for three datasets, wherein two datasets; TampereU and UJIndoorLoc, belonged to Wi-Fi localization; and one belonged to other machine learning areas; KDD99 from cloud security.  
This experiment aimed to generalize the cyclic dynamic concept to other machine-learning fields, where the classifier is required to remember old knowledge that was already gained but might not be preserved due to its dynamic features. The data were repeated for three cycles, and the number of active features in each cycle was not changed, in order to allow investigation of performance changes in the cyclic dynamic situation. Furthermore, accuracy and time results were generated. Section 5.1 provides the datasets description. Model characterization with respect to the number of neurons and regularization factor, as well as the accuracy results are presented in Sections 5.2 and 5.3, respectively.
# Sensorless Current Pulsation Compensation in a Hybrid Energy Storage
###### Abstract  
This paper presents a dual active bridge DC/DC converter used as an AC current compensator in a hybrid energy storage application. The AC current in the DC link appears when a three-phase, four-wire inverter operates with unbalanced output currents--for example, when trying to compensate for grid voltage unbalance. This AC current has adverse effects on the operation of the electrochemical energy storage, and it should be compensated. To achieve this, a compensator is introduced into the DC link circuit of the inverter--a DC/DC converter with a capacitor bank. The DC/DC converter is responsible for compensating the AC pulsation by creating its own pulsation with the opposite phase. In the paper, the genesis of this pulsation is explained, and a compensation circuit is proposed along with a sensorless compensation algorithm. The algorithm is based on symmetrical decomposition and is used to generate a reference signal for the compensator. The numerical analysis of the algorithm is presented, and the operation of the compensator is verified on the laboratory bench.  
energy storage; protection circuit; DC link current pulsation; dual-active bridge converter; proportional-resonant controller +
Footnote †: journal: Applied Sciences
## 3 Proposed System Model
### Propagation Model  
The details of the propagation model are given in [37], but the key aspects are summarised here for completeness. As the goal is to generate realistic synthetic data, the radio  
Figure 1: Outline of the 5G coverage area showing the 5G uplink/downlink and the \(X_{n}\) interface between gNBs.  
propagation model is based on a version of [PERSON]'s free-space propagation equation [38], as shown in (1):  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{(4\pi d)^{2}} \tag{1}\]  
where \(P_{r}\) is the received signal strength (in watts) received by the UE from each specific base station and \(P_{t}\) is the transmit power (in watts) of the BS. \(G_{t}\) is the gain of the transmitting antenna at the base station and \(G_{r}\) is the gain of the receiving antenna at the UE. Variable \(d\) is the distance (in metres) between the BS and the platoon leader (UE) and \(\lambda\) is the wavelength (in metres) of the transmitted signal.  
The MR data are generated by a MATLAB simulation that calculates the location of the platoon leader every second and the propagation distance as follows:  
\[d=\sqrt{\left(V\times T-X_{BS_{i}}\right)^{2}+\left(Y_{BS_{i}}\right)^{2}} \tag{2}\]  
where \(V\) is the vehicle speed (m/s), \(T\) is the time taken by the simulation clock (s), and the coordinates of the ith BS are \(\left(X_{BS_{i}},Y_{BS_{i}}\right)\). At \(T=0\), the leader is considered to be on the left side of the scenario. The substitution of (2) into (1) yields a simple calculation of the expected value of the received signal strength at the UE for each time step of the simulation:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}}{(V\times T-X_{BS_{i}})^{2}+\left(Y_{BS_{i}}\right) ^{2}}\bigg{(}\frac{\lambda}{4\pi}\bigg{)}^{2} \tag{3}\]  
To model the variation in path loss due to the presence of other vehicles and buildings and radio fading events, a standard statistical model of radio propagation [39], was applied. In this case, the variation is bounded between zero and two to model destructive and constructive interference. The expected power level is then multiplied by this randomised variable and the received power is converted to the dBm scale as follows:  
\[Rxlev=10\log_{10}(P_{r}\times x(t))+30 \tag{4}\]  
where the channel's random variation is given by \(x(t)\).
## 4 Space-Qualified RF Generator Techniques
### Asic  
Although not tested or simulated in the frame of this research, for the sake of completeness, the author would like to mention the technique of an Application-Specific Integrated Circuit (ASIC) in the frame of space applications [47, 48, 49]. In this approach, intellectual property cores are used from a space-qualified library and custom built in the ASIC [49, 50]. The device is able to fully integrate the DDS, the DAC, the balun convertor, as well as the low-pass filter, described in Section 4.2. Hence, a single-chip approach is possible, using minimal PCB area, mass, volume, and power. The available market for building these ASICs for space applications is rather limited. The disadvantage of using such a technique is its very high cost, together with the long lead time due to the custom development process and possibly ITAR/EAR restrictions.