esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Dual Circularly Polarized Sequentially-Fed Patch Antennas for Satellite Applications
## 5 Conclusions  
In this paper, the design, fabrication and measurements of a dual-CP sequentially-fed circular patch antenna, loaded by a fence of passive monopoles, are proposed. The antenna radiates a broad pattern in both the azimuthal and elevation planes with a good cross-polarization rejection. To achieve dual-CP operation mode, the circular patch was sequentially-fed by three pins, whose mutual  
Figure 8: (**a**) Fabricated \(\Sigma-\Delta\) network. (**b**) Layout. (**c**) Reflection coefficient (\(S_{jj}\)) over all the considered bandwidth (**d**) Insertion Loss. (**e**) Relative phase. (**f**) Comparison of simulated (filled markers) and measured (empty markers) normalized difference patterns required for satellite tracking. Such patterns are obtained by connecting the antenna array to a standard \(\Sigma-\Delta\) network.  
phase-shift is equal to 120\({}^{\circ}\), while the antenna feeding network was designed in microstrip technology. Moreover, the presented antenna was used as a building block for the design and fabrication of a 2-by-2 array for higher UHF band satellite applications, showing a fair agreement between simulations and measurements. The proposed antennas can be profitably adopted as primary feeds for reflector systems for Satcom application.  
[PERSON] defined the specific application requirements and selected the antenna topology. S.C.P., G.S. and L.D.D. addressed conceptualization and methodology for the study, development and analysis of circularly polarized antenna. [PERSON] carried out the antenna optimization and experimental characterizations. Writing--original draft preparation, S.C.P.; writing--review and editing, S.C.P., L.D.D. and G.S. The research was carried out in the framework of an ongoing collaboration between the University of Catania and Sicilsat Communications S.r.I, for the development of compact, low cost, circularly polarized antennas for Satcom applications. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work has been supported by the PON Research and Innovation project AIM (Attraction and Mobility of Researchers), Action I.2, granted by the FSE European Union program. The authors would like to thank [PERSON] and [PERSON] from Sicilsat Communications S.r.I., for antenna pattern measurements in anechoic chamber. The authors declare no conflict of interest. The authors declare no conflict of interest.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 2 Optimized Mathematical Modeling of the Optimized Waveform
### Additional Constraints of the Pan-Function  
As an example, we consider the partial-response quadrature modulation with a symbol length of \(4T\). The analytic solution of the optimized mathematical model is fairly complex under restriction of the symbol boundary condition, single-symbol signal energy condition, constant-envelope condition,  
Figure 1: Waveforms of \(g\left(\omega\right)\) for various \(n\) (i.e., from 1 to 5).  
and minimum cross-correlation function. However, we may obtain the optimized baseband signal waveforms using numerical calculation.
## 4 Simulation Comparison with Existing Schemes
### Current Stress  
Figure 13 compares the current-stress performance of the five modulation strategies under different voltage conversion ratios \(k\) and normalized transmitted power \(p\). Figures 13a and 13b present the curves of normalized current stress \(ip_{m}\) versus normalized transmitted power \(p\) for \(k\) = 1.5 and \(k\) = 0.667, respectively. As shown in Figure 13, SPS exhibits the highest current stress under both buck and boost conditions, particularly at low transmitted power levels. The current-stress performance of OPCB-EPS and CSO-TPS is identical, as indicated by the cyan curve in the figure; therefore, only CSO-TPS is considered in subsequent analyses. Both CSO-DPS, CSO-TPS, and EIOS-TPS effectively reduce current stress. Under extremely low-power conditions (\(p<0.07\)) the current-stress relationship is EIOS-TPS > CSO-DPS > CSO-TPS. In practice, a slight increase in current stress is expected to enable ZVS. For \(0.07<p<1\), EIOS-TPS and CSO-TPS exhibit almost identical current stress, both lower than that of CSO-DPS. Overall, EIOS-TPS maintains lower current stress across most power ranges, except for slightly higher stress at extremely low transmitted power levels.
# ConDPC: Data Connectivity-Based Density Peak Clustering
## 4 Experiments and Results
### Experiments on Synthetic Datasets  
#### 4.1.1 Synthetic Datasets  
The synthetic datasets used in the experiments have different sizes and distributions. The details of the datasets are shown in Table 2.  
The synthetic datasets can be generalized into three types: (1) The density of different categories is similar (e.g., the Aggregation, Flame, and R15 datasets) (2) The density of different categories is quite different (e.g., the Jain and Twocircle datasets) (3) The data distribution is enclosed/semi-enclosed structure (e.g., the Halfk  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter(s) and the Search Range** \\ \hline ConDPC & \(percent\in\{0.1,0.2,\ldots,10\}\) \\ DPC & \(percent\in\{0.1,0.2,\ldots,10\}\) \\ DBSCAN & \(eps\in\{0.01,0.02,\ldots,0.09\}\), \(Min\text{{Pts}}\in\{2,3,\ldots,40\}\) \\ K-means & \(n\): number of clusters \\ DPC-DBFN & \(k\in\{3,4,\ldots,40\}\) \\ \(\gamma\in\{001,0.02,\ldots,0.50\}\), \\ \(\theta\in\{0.01,0.02,\ldots,0.50\}\lambda\in\{0.01,0.02,\ldots,1.00\}\), \\  & \(pct\in\{0.3,0.4,\ldots,6.0\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of the input parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Source** & **Instances** & **Features** & **Clusters** \\ \hline Aggregation & [28] & 788 & 2 & 7 \\ Flame & [29] & 240 & 2 & 2 \\ R15 & [30] & 600 & 2 & 15 \\ Jain & [31] & 373 & 2 & 2 \\ Twocircle & [32] & 314 & 2 & 2 \\ Halfkernal & [17] & 1000 & 2 & 2 \\ Halfcircle & [33] & 238 & 2 & 3 \\ Threecircle & [33] & 299 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the synthetic datasets.  
Threecircle datasets). These three types of datasets may be intersecting, such as Twocircle and Threecircle datasets with uneven density and also nested structure of distribution. During the experiments, the influence of different characteristics of the datasets on the clustering results will be discussed.  
The first type of datasets is used to verify whether the proposed algorithm retains the good clustering effect of the original DPC algorithm on such data sets. The latter two types of datasets are used to verify whether the proposed algorithm can solve the problem that the original DPC algorithm has a poor clustering effect on such datasets.
## 5 Simulation Model  
In order to compare the two spectrum management schemes and also investigate the impact of using the appropriate scheduler on the performance of the spectrum management schemes, an open source network simulator that is able to provide performance evaluation of a realistic 4G and beyond network is used in this work. It is an event driven based simulator and developed in C++ [34]. The simulator has been adapted for the satellite scenario. There are two scenarios: Scenario A considers a total bandwidth of 10 MHz for shared spectrum and bandwidth of 3 MHz for IoRT devices for dedicated spectrum which is equivalent to one-third of the bandwidth of shared spectrum and Scenario B considers a total bandwidth of 15 MHz for shared spectrum and a bandwidth of 5 MHz for IoRT devices for dedicated spectrum which is one-third of the bandwidth used for shared spectrum. The traffic and channel models presented in previous sections have been considered for this simulation. The scheduling and channel reporting intervals of 1 and 100 TTI, respectively, have been adopted.  
This channel reporting interval has been adopted to utilize the limited satellite power since the satellite has a slow varying channel. The Equivalent Isotropically Radiated Power (EIRP) for the GEO satellite and UE are 63 dBm and 23 dBm respectively. The channel noise of \(-\)208.1 dBm in 180 kHz bandwidth subchannel and polarization loss of 3.5 dB are adopted. The details of the other parameters considered for the simulations are presented in Table 1.
## 3 Proposed Method for Comparing between Original and Manipulated Videos
### Description of Image Processing, Optimization Search Method, and Comparison Algorithm
#### 3.2.2 Optimized Comparison Search Technique for Original and Manipulated Videos
\begin{tabular}{|l|} \hline _i, j, a, count = 0, x = n/2, k = Continuous section [Table. 2]_ \\ \hline
**WHILE (TRUE)**[ \\ \hline
**IF ( \(n<k\)) THEN** \\ \(x=1\) \\
**END IF** \\ \hline
**COMPARE \(Q_{1+i}\) WITH \(A_{x+j}\)** \\
**IF ( \(Q_{1+i}\)SIMILAT TO \(A_{x+j}\) ) THEN** \\ \(i=i+1\), \(j=j+1\), count = count + 1 \\
**ELSE** \\ \(j=i+1\) \\
**END IF** \\ \hline
**IF ( \(n/2\)+(\(a+1\))\(=n\)\(-ak\)) THEN** \\ \(\text{IF }(x+j)=n\) - \((a+1)k\) THEN** \\ \(j=0\), \(x=1+ak\) \\
**ELSE** \\ \(\text{IF }(x=2/n+ak)\)**AND( \(j=k\)) THEN** \\ \(j=0\), \(x=1+ak\) \\
**END IF** \\ \hline
**IF ( \(1\) - \((a+1)k\)\(\geq\)\(n/2\)) THEN** \\ \(\text{IF }(x+j)=n/2\) THEN** \\ \(\text{END WHILE}\) \\
**ELSE** \\ \(\text{IF }(x=1+ak)\)**AND( \(j=k\)) THEN** \\ \(j=0\), \(x=n\) - \((a+1)k\) \\
**END IF** \\ \hline
**IF ( \(n\) - \((a+1)k\)\(\leq\)\(n/2\) + \((a+1)k\)) THEN** \\ \(\text{IF }(x+j)=n\) - \((a-1)k\) THEN** \\ \(j=0\), \(x=1+ak\) \\
**ELSE** \\ \(\text{IF }(x=n\) - \((a+1)k)\)**AND( \(j=k\)) THEN** \\ \(a=a+1\), \(j=0\), \(x=n/2+ak\) \\
**END IF** \\ \hline
**IF (\(count\) = 10) THEN** \\
**PRINT**^A video \(\coloneqq\) Query video\" \\
**END WHILE** \\
**END IF** \\ \hline \(i\) : Variable for counting Query frames \\ \(j\) : Variable for counting Comparison frames \\ \(a\) : Variables that remove duplicates or change the \\ direction of the comparison \\ _count_ : Variable that counts the result of \"similar\" \\ \hline \end{tabular}   
Figure 3: Pseudocode used for comparison. Here, 2 is the base case, where the total number of frames of a comparison is smaller than the number of a continuous section composed of \(k\) frames. In this case, frames \(A_{1}\) through \(A_{n}\) are compared in order. Next, 2 is a similarity comparison. If a query frame is similar to a comparison frame, both frames shift to the next frame. If not similar, the query frame remains unchanged, but the comparison frame shifts one frame, and the comparison continues. In 3, 4, and 5, the sequence comparison to sections of \(k\) frames is conducted from the center. The first frames and last frames of the video are then compared, which is considered a single cycle. This cycle repeats, excluding the already compared frames, until the ending conditions are met. In 6, after a shift from the middle frame to \(k\) frames away from the middle, the location of the comparison frame comes back to the front. In 4 after the shift from the front to the \(k\) section, the frame moves to the back. In 5, after the shift from the back to the \(k\) section, a frame goes to the middle. If the compared frames are duplicates, they are removed. When the comparison of all frames finishes, the WHILE statement ends. In 1, if the number of similarities is more than 10 as a result of a frame comparison, no more inspections are needed, and thus the WHILE statement ends and it is determined that \"the two videos are similar.\"
## 2 Materials and Methods
### Main Research
#### 3.2.7 Summary of Research Data
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Mixture** & **d\({}_{\mathbf{p}}\)** & **d** & **\(\sigma_{\mathbf{c}}\)** & **\(\sigma_{\mathbf{b}}\)** & **FRL** & **S\({}_{\mathbf{w}}\)** & **WPL** & **k\({}_{\mathbf{cr}}\)** \\  & & & & & & & & & **(NaCl/HCl/Na\({}_{2}\)SO\({}_{4}\))** \\ \hline Type 1: & RS & 0.564 & 2.380 & 42.8 & 5.16 & 200–350 & 5.72 & W6 & 0.511/0.825/0.871 \\ Reference sample & & & & & & & & & \\ Type 2: 0/80/20 & 0/80/20 & 0.570 & 2.398 & 49.3 & 5.41 & 350–500 & 5.55 & W8 & 0.592/0.849/0.885 \\ Type 3: 0/75/25 & 0/75/25 & 0.571 & 2.403 & 50.2 & 5.47 & 350–500 & 5.57 & W8 & 0.595/0.850/0.887 \\ Type 4: 0/70/30 & 0/70/30 & 0.573 & 2.408 & 51.3 & 5.52 & 350–500 & 5.49 & W8 & 0.597/0.857/0.889 \\ Type 5: 15/80/5 & 15/80/5 & 0.584 & 2.411 & 52.0 & 5.53 & 400–600 & 5.28 & W10 & 0.630/0.863/0.923 \\ Type 6: 15/75/10 & 15/75/10 & 0.591 & 2.414 & 53.8 & 5.66 & 400–600 & 5.22 & W10 & 0.658/0.868/0.932 \\ Type 7: 15/70/15 & 15/70/15 & 0.593 & 2.419 & 53.9 & 5.74 & 400–600 & 5.17 & W10 & 0.663/0.873/0.936 \\ \hline \hline \end{tabular} d\({}_{\mathbf{p}}\)—packaging density, %; d—density, g/cm\({}^{3}\); \(\sigma_{\mathbf{c}}\)’compression strength, MPa; o\({}_{\mathbf{b}}\)’bending strength, MPa; FRL-freeze–thaw resistance level; S\({}_{\mathbf{w}}\)’water saturation (absorption), %; WPL’water permeability level; k\({}_{\mathbf{cr}}\)’coefficient of chemical resistance by strength.  
\end{table} 
Table 14: Freeze–thaw resistance: physical characteristics and durability indicators.
## 3 Micro-Accelerator-in-the-Loop Framework
### MA and MCU Data Interaction  
In this paper, MA is defined as peripherals connected to MCU core peripheral bus. Access to this peripheral is through SFRs mapped to memory addresses. To connect the MA to the core as a peripheral, we need to map a memory address space with registers that enable TinyML software to configure the MA hardware. Registers are used to trigger the MA operation start or to exchange data required for computations. However, for neural network computation acceleration, a bulk of data transfer is required due to the large amount of weights, activations, and output values. Conventional accelerator users in on-cloud AI rarely need to consider physical memory addresses when transferring large amounts of data between the device and host, as the operating system's virtual memory technique efficiently manages these transfers. However, MCU has memory constraints compared to PC and server. Therefore, memory size constraints are important when TinyML software uses SFR to transfer data required for computational processes from memory to MA peripheral devices. If all the data used by the MA were mapped as register addresses in memory, it would exceed the physical memory address range of the MCU. Therefore, it is inevitable to use DMA controllers to access the data that MA needs for neural network computations, and vice versa.  
Figure 5 represents the data transfer modeling of MA connected to the peripheral bus. When DMA transfers bulk data without CPU intervention, iterative transfer is required because of the size of data that can be handled at once is fixed. Simultaneously, the source or destination address of data can be fixed or auto incremented. Because, as mentioned above, MA requires constrained register sector space, the DMA accesses with a single peripheral address, and when a peripheral bus access occurs from DMA, the internal buffer address is increased within MA. When the DMA stores data into the MA's input and weight buffers, and loads data from the output buffer, the MA can transfer sequential data over the same peripheral address access. Such a DMA operation model is defined in the MA's CR register. When the MA is enabled by SFR configuration, DMA request is set to fill input and weight buffer. DMAC Req. Clock Timing is the clock parameter until DMA respond data to the peripheral bus in this request. Memory Read Clock, Memory Write Clock are the clock parameters required to access memory in continuous DMA operations. The DMA  
Figure 4: MA internal PE’s weight and input buffer connection structure and dataflow direction.  
configuration parameters are used for testbench generation that models the data transfer process in MA and peripheral bus protocols. When a data transfer request is generated from the MA, the data used for neural calculation is supplied to the MA or transferred to the emulator, depending on the behavior of the configured DMA controller.
# Energy Efficiency Policies to Face Buildings' Climate Change Effects in Paraguay
## 2 Paraguayan Climate Scenario and Climate Change Effects  
The annual average temperature of the subtropical climate of Paraguay is 24 \({}^{\circ}\)C [10]. Projections revealed an average increase of temperature of 4.2 and 3.4 \({}^{\circ}\)C for the climate scenario A2 and B2, respectively, taking as a basis the period 1961 to 1990 [11]. Taking as time horizon 2050, considering scenarios A2 and B1 (as high and low scenarios, respectively), the results depicted an average increase in temperature of 2.5 \({}^{\circ}\)C [12]. Employing RCP 4.5 and RCP 8.5, the temperature can increase 3 and 4 \({}^{\circ}\)C, respectively, from 2041 to 2050, compared with the average temperature values of 1961 to 1990 [13].  
Resourcing COREX climate models, [PERSON] et al. [14] used RCP 4.5 and RCP 8.5 as low and high CO\({}_{2}\) concentration pathways climate scenarios, respectively, to analyse temperature trends for Asuncion, Paraguay, simulating temperature data for 1990, 2030, 2050 and 2070. It was concluded that comparing the percentage of the time during the year in which a certain temperature is presented with the RCP 4.5, the differences between historic data and 2030 data are not significant since the temperature increase is sustained because this scenario considers a future with emission reductions. For the years 2050 and 2070, the weather datasets obtained present higher temperatures. Considering the RCP 8.5, a higher percentage of the time during the year in which high temperatures are recorded were obtained [14].  
Paraguay is characterised by a tropical and subtropical climate with very hot and rainy summers, which is likely to worsen as a result of inexorable global warming that will cause an increase in the country's temperature [12; 15]. Scientific research has shown that this effect will affect the indoor temperature of buildings, which will probably cause an increase in energy consumption for space cooling [16; 17; 18; 19; 20]. The authors' previous research work [21; 22], developed for typical housing of the historic centre of Asuncion (the capital of Paraguay), showed that at the current state (original state) the indoor overheating rate is 31% for 2009 climate conditions. Considering the climate scenario RCP 8.5 (high CO\({}_{2}\) concentration pathways climate scenario established by the IPCC Fifth Assessment Report), the overheating rate will be 42% for 2050, showing that climate change affects buildings' indoor thermal comfort [14].  
In this context, considering the high probability of the increase of the country's temperature as a consequence of climate change, the leaders of Paraguay need to analyse the best strategies to be implemented in the country to mitigate this phenomenon but also to implement strategies focused on adapting vulnerable areas, for which effective policies have to be developed.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 3 Background
### IoT Botnet
#### 3.3.4 IoT Botnet Architecture  
Conventional botnets share the same architectures as IoT botnets. They can be defined as centralized botnets, decentralized (peer-to-peer) botnets and hybrid botnets.
* Centralized botnets. The botmaster manages and tracks all bots from a unified central server, which decreases the latency; i.e., all bots receive instructions from and report to a central server (C&C server) [11; 21]. The botmaster in this architecture may have one or more central servers to use [23]. The server uses protocols such as HTTP and IRC. The botmaster server may have the disadvantage of being a single point that can cause total failure [11; 21]. One of the famous families of centralized IoT botnets is the Mirai family [26].
* Decentralized botnets. These are also called peer-to-peer (P2P) botnets. Each bot operates as a client and a server; each bot is linked to at least one other bot. Only if all the bots are interconnected will the commands reach each bot. In this architecture, it is difficult to coordinate between bots, but at the same time, it is more sophisticated and not easy to detect because of the different communication between peers. This type of IoT botnet uses a peer-to-peer protocol in communication [11; 21]. One of the well-known decentralized (P2P) IoT botnets is Hajime [38].  
Figure 4: A flow diagram of an IoT botnet.  
Hybrid botnets. A hybrid botnet contains two types of bots; some of them have functionality as servers and clients, and others just as clients, so it is a combination of the previous two types (centralized and decentralized) of architecture. There is high message latency [21].
## 3 Results
### X-ray Photoelectron Spectroscopy  
The chemical composition of the surface of each filler was measured with XPS. The survey spectra with characteristic peaks for each filler is shown in Figure 9, and elemental composition for each material is given in Table 5. As can be seen, all materials exhibited a significant amount of oxygen on the surface. Therefore, the elemental spectra were peak-fitted to define the corresponding oxidation states of different metals and metalloid Si.  
Figure 8: Crossover point \(\omega\) (G\({}^{\prime}\) = G\({}^{\prime\prime}\)) for the composites with various loadings. The point at 30 vol.% for Ti was extrapolated from the measured results.  
Figure 9: XPS survey spectra with characteristic peaks for the fillers: (**a**) Al, (**b**) 316L, (**c**) Ti, and (**d**) GB.  
On the AlSi10 Mg surface, we observed a high amount of Al\({}_{2}\)O\({}_{3}\) at 74.6 eV in comparison to the elemental Al (0) at 73.1 eV in an atomic ratio of 7.4:1, which indicates high oxidation of the surface [56; 57; 58]. Moreover, magnesium and zinc were found in oxide forms of MgO at 1304.5 eV and ZnO at 1022.6 eV, while silicon was mainly found in the elemental state at 99.1 eV. Due to high oxidation of alloy surface, smaller silicon content of the element, mainly in the non-oxide form, was observed.  
Similarly, high oxidation of the filler surface was detected for 316L stainless steel alloy. Similar findings were also reported by other authors [59; 60]. Most of the iron was in a Fe\({}_{2}\)O\({}_{3}\) form with a binding energy of 711.2 eV. The same was applied for other elements which were also in the oxide forms as MnO\({}_{2}\) at 642.0 eV, Cr\({}_{2}\)O\({}_{3}\) at 576.6 eV, and SiO at 101.5 eV, respectively [61].  
On the titanium alloy surface, we found titanium and aluminium in the form of TiO\({}_{2}\) at 458.4 eV and Al\({}_{2}\)O\({}_{3}\) at 74.0 eV [62]. Moreover, also, this surface showed a high concentration of oxidised metals on the surface, and therefore, almost no response was observed in the spectrum.  
Borosilicate glass, on the other hand, is a chemical-resistant material. The higher the B\({}_{2}\)O\({}_{3}\) content in the glass, the more chemically resistant the material becomes [63]. From the XPS composition of boron and silicon, we estimated 55.8% of SiO\({}_{2}\) and 28.3% of B\({}_{2}\)O\({}_{3}\) on the surface. With that high amount of B\({}_{2}\)O\({}_{3}\) content in the surface composition, it was reasonable to assume that the surface was chemically stable and did not interact with the matrix material.  
Oxides on the metal alloy surface can interact with the components of the binder system, particularly with the maleic anhydride (MA). The interaction between the filler particles and the matrix was investigated using ATR-FTIR analysis and is discussed in the following section.