esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Essential Oils Characterization by Gas Chromatography-Mass Spectrometry (GC-MS)
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**Percentage in the**} \\  & & & & \multicolumn{2}{c}{**Samples (\%)**} \\ \cline{4-5}
**Identified Compound** & **R.T. (min)** & **RI\({}_{\text{exp}}\)** & **RI\({}_{\text{lit}}\)** & \multicolumn{2}{c}{**A.**} & \multicolumn{2}{c}{**L.**} \\  & & & & _foneticulum_ & _angustifolia_ \\ \hline Lavandulol & 18.30 & 1177 & 1169 & - & 1.9 \\ \hline Isopulegone & 18.66 & 1186 & 1177 & 0.3 & - \\ \hline Terpinen-4-ol & 18.70 & 1187 & 1177 & - & 3.0 \\ \hline \(p\)-Cymen-8-ol & 18.95 & 1195 & 1185 & - & 0.1 \\ \hline Cryptone & 19.09 & 1196 & 1187 & - & 1.5 \\ \hline \(\alpha\)-Terpineol & 19.30 & 1202 & 1190 & - & 4.2 \\ \hline n-Hexyl butyrate & 19.52 & 1207 & 1192 & - & 0.6 \\ \hline
**Estagole** & **19.74** & **1212** & **1196** & **57.5** & 0.3 \\ \hline n.d. & 20.12 & & & - & 0.1 \\ \hline n.d. & 20.24 & & & 0.0 & - \\ \hline _trans_-Carveol & 20.60 & 1234 & 1220 & 0.1 & 0.1 \\ \hline n.d. & 21.05 & & & - & 0.2 \\ \hline _cis_-Geraniol & 21.08 & 1246 & 1232 & - & 0.5 \\ \hline \(p\)-Cumic aldehyde & 21.55 & 1257 & 1240 & - & 0.6 \\ \hline
**Pulegone** & **21.56** & **1258** & **1244** & **20.6** & - \\ \hline Carvone & 21.76 & 1262 & 1252 & 0.1 & 0.2 \\ \hline n.d. & 22.22 & & & 0.1 & - \\ \hline _trans_-Geraniol & 22.30 & 1275 & 1258 & - & 0.7 \\ \hline
**Linalyl acetate** & **22.41** & **1278** & **1259** & - & **6.9** \\ \hline Phellandral & 23.12 & 1296 & 1275 & - & 0.1 \\ \hline Bornyl acetate & 23.68 & 1309 & 1288 & - & 0.3 \\ \hline \(p\)-Cymen-7-ol & 23.86 & 1314 & 1291 & - & 0.1 \\ \hline
**Lavanduly acetate** & **24.00** & **1317** & **1301** & - & **7.9** \\ \hline Carvacrol & 24.39 & 1327 & 1317 & - & 0.1 \\ \hline Piperientone & 26.04 & 1367 & 1341 & 0.1 & - \\ \hline Neryl acetate & 27.18 & 1395 & 1368 & - & 0.7 \\ \hline Geranyl acetate & 28.00 & 1415 & 1386 & - & 1.4 \\ \hline Caryophyllene & 29.38 & 1449 & 1444 & 0.5 & 0.3 \\ \hline \(\alpha\)-Santalene & 29.40 & 1450 & 1447 & - & 0.1 \\ \hline Germacrene D & 31.93 & 1512 & 1481 & 0.1 & - \\ \hline \(\Upsilon\)-Elemene & 32.55 & 1527 & 1494 & 0.2 & - \\ \hline n.d. & 33.47 & & & - & 0.1 \\ \hline n.d. & 34.73 & & & - & 0.1 \\ \hline Spathulenol & 35.70 & 1605 & 1605 & 0.2 & - \\ \hline Caryophyllene oxide & 35.90 & 1610 & 1613 & 0.1 & 1.7 \\ \hline \(t\)-Cadinol & 38.10 & & & - & 0.1 \\ \hline n.d. & 39.21 & & & - & 0.1 \\ \hline
**Total identified** & & & & **99.8** & **99.3** \\
**compounds (\%)** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
(SWT) [10], maximally stable extremal (MSER) [11], Edge Boxes [12], etc. The resistance of traditional text detection methods to interference is weak, and the detection effect is limited. The shape of the text, layout, and other reasons will cause significant interference in the detection of text. Therefore, the traditional text recognition methods do not work well in detecting calligraphic characters.  
In recent years, with the continuous progress of deep learning technology, a large number of breakthrough achievements based on deep convolution neural networks (CNN) [13], deep belief networks(DBN) [14], and other deep models have emerged in the field of computer vision [15; 16]; appropriate methods have been gradually applied to the field of handwriting character detection [17], especially in the field of Chinese character detection [18; 19; 20], and have also been applied to calligraphy style recognition [21; 22].  
However, there are still some problems with calligraphic character detection. The training data used for text detection often uses the labeling method of the external bounding box of the text area, which will make the algorithm suffer from two problems when predicting. (1) When the text areas are too small to be close to each other, the network is easy to cause sticking. It cannot accurately split its boundaries because, after many convolution and pooling operations of the network, the high-level features have high semantics and low resolution, resulting in the loss of image detail information in the prediction map. (2) Significant differences exist in the distribution of characters in different languages and text areas. When detecting Chinese characters with left and right structures or upper and lower structures, non-text pixels cause \"excessive segmentation\" due to accurate classification.  
For the above questions, this paper adopted the semantic segmentation method suitable for multi-scale problems, put forward a multi-font calligraphy character detection method based on scale estimation from accurate calligraphy character area segmentation and calligraphy character area scale estimation, and then obtained the calligraphy character area features according to the feature mapping. Our contribution is proposing a calligraphy character region representation that predicts the calligraphy character region boundary as independent spatial information to solve the problem of detecting individual characters in calligraphic works that contain multi-scale characters, which will play a great role in the subsequent analysis of Chinese calligraphy, such as the recognition of calligraphy and painting style features.
## 3 Proposed Method
### Bit-Slicing Techniques  
In order to apply the bit-slicing technique, the operation process of the cryptographic algorithm must be converted to a combination of simple logic gates such as AND, OR, and NOT. In addition, a packing process to convert the data into multi-block bit-sliced representation, and an unpacking process to return to the original representation, are required. Therefore, the overhead caused by these processes must be considered. On the other hand, the bit permutation in the linear layer of PRESENT and GIFT is complex and not efficient to implement in software, but it can be solved by applying the bit-slicing technique. Since the bit-slicing technique encrypts multiple blocks, it is suitable for platforms that require high throughput, such as GPU devices. A bit-slicing implementation that computes many blocks in parallel can save several computational domains but can incur overhead because it consumes many registers. The GPU environment provides relatively sufficient registers to efficiently perform this highly advanced bit-slice implementation. As shown in Table 1, 32 64-bit plaintext blocks are implemented in parallel since the GPU core is a 32-bit architecture.  
Two types of overheads created by the bit-slicing technique are considered in the proposed implementation. When an implementation uses the same key, the key schedule is pre-launched to generate round keys to avoid duplicated work. When the bit-slicing technique is applied, there are 32 plaintexts encrypted in parallel, the round key is also increased by 32\(\times\). In this case, the kernel may have an additional delay in retrieving the pre-computed round keys from the global memory. On the other hand, these pre-computed round keys can be stored in the registers to achieve low latency, but it may not work if the number of registers are more than supported amount. For instance, [PERSON] et al. [4] hard-coded the AES round keys during compilation to avoid using an excessive number  
Figure 3: Grid, Thread, Block, and Warp configuration in CUDA.  
of registers, but this seriously limits the usefulness of such software. To resolve this issue, we proposed to calculate the round keys on-the-fly instead of generating them in advance. Although this may introduce some additional computation overhead, it is still beneficial because the round key generation for lightweight ciphers is generally not complex.  
On top of that, when the bit-slicing technique is applied, the additional overhead of packing and unpacking to change the state occurs. This overhead increases as the number and size of blocks to be converted increase. In lightweight ciphers using 64-bits, the block size is relatively small, so it is suitable for high-level bit-slicing on the GPU.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 2 Communication Protocols GOOSE and R-GOOSE and Disadvantages of the IEC61850 Standard
### R-Goose  
With the development of smart grids, the concept of WAMPAC (wide area monitoring, protection and control) applications was developed. WAMPAC makes use of synchrophasor technology. A synchrophasor is the calculation of a phasor concerning an absolute time reference. With this measurement, the absolute relationship between the phases (angles) of phasors in different locations of the electrical power system can be determined. The time reference is usually a Global Positioning System (GPS). WAMPAC is based on PMUs (phasor measurements units) and PDCs (phasor data concentrators).  
PMUs are units of synchrophasor measurements that allow the observation of the static and dynamic behavior of an electrical power system, through the variables of current (amplitude and angle), voltage, frequency, and rate of change of frequency (ROCOF), and are then sent to PDCs through a communication network. There are two PMU communication frameworks: IEEE C37.118 and IEC61850-90-5. To achieve compatibility between IEEE C37.118-based synchrophasor data transfer with the IEC61850 substation automation standard, IEC61850-90-5 was introduced [13; 14].  
Since synchrophasor-based technology normally requires transmitting data over a WAN, IEC61850-90-5 specifies the use of UDP as a transport protocol, and thus, the R-GOOSE and R-SV messaging type was born. IEC61850-90-5 also defines the digital security requirements that R-GOOSE and R-SV must have, indicating that authentication and integrity of messages are mandatory, while confidentiality (encryption) is optional. This point is very relevant because IEEE C37.118 does not specify cybersecurity requirements to protect data communication over an insecure network, which is supplemented by IEC61850-90-5.  
Synchrophasor technology plays a key role in the monitoring, control, and protection of electrical power systems and any failure in this field can have serious consequences such as blackouts. To achieve authentication and integrity of R-GOOSE and R-SV messages, IEC61850-90-5 specifies the use of Message Authentication Code (MAC) algorithms, such as HMAC-SHA256 or AES128-GMAC. Although confidentiality is optional, the technical report specifies AES-128 and AES-256 algorithms as an encryption method to ensure confidentiality. Figure 2 shows an R-GOOSE/R-SV message, including fields with digital security specifications that implements IEC61850-90-5 [15].
## 3 Simulated Results and Discussions  
This section presents the full-wave simulation results of the proposed FSS structure. Figure 2a manifests the spectral response of the FSS unit cell. The FSS resonates at 11.8 GHz and offers a stop bandwidth of almost 4 GHz. Figure 2b illustrates that incorporation of the U-shaped stub shifts the operating frequency to 8.6 GHz, but leads to a reduction in the rejection bandwidth. Thus, the edges of the resulting structure are chamfered and tuned to achieve the desired response around 10 GHz frequency, as shown in Figure 2c. Finally, inductive stub loading into the flower-like geometry broadens its operational bandwidth and enhances angular stability. The structure's parameters are further optimized to obtain a response at 10 GHz, the X-band's central frequency, as in Figure 2d.
## 4 Applications of Heterogeneous Photocatalysis in Water Purification
### Degradation of Organic Micropollutants  
Organic micropollutants such as pharmaceuticals, personal care products, pesticides, endocrine-disrupting compounds (EDCs), and industrial chemicals are increasingly detected in surface and drinking water sources. These substances, even at low concentrations, pose risks to human health and ecosystems due to their persistence and bioaccumulative properties [2; 4; 9; 17; 110].  
Photocatalysis has proven to be effective in degrading these compounds by breaking their complex molecular structures into simpler intermediates, ultimately leading to mineralization into CO\({}_{2}\) and H\({}_{2}\)O. Figure 8a illustrates the photocatalytic degradation process of organic micropollutants, highlighting the breakdown of complex contaminants such as pharmaceuticals, hormones, and pesticides into simpler, less harmful intermediates, ultimately leading to complete mineralization into carbon dioxide and water [55; 111]. It visually supports the role of photocatalysts like TiO\({}_{2}\) and g-C\({}_{3}\)N\({}_{4}\) in enabling effective treatment under UV and visible light conditions. Studies using TiO\({}_{2}\) and g-C\({}_{3}\)N\({}_{4}\) have demonstrated successful degradation of antibiotics (e.g., sulfamethoxazole, ciprofloxacin),  
Figure 8: Application of catalysts in water purification: **(a)** photocatalytic degradation pathway of organic micropollutants in water treatment; **(b)** mechanism of photocatalytic pathogen inactivation in water treatment; **(c)** photocatalytic transformation pathways of inorganic contaminants in water treatment; **(d)** photocatalytic mitigation of natural organic matter and disinfection byproduct precursors; **(e)** pilot-scale applications of photocatalytic systems in real water matrices.  
hrmones (e.g., 17\(\alpha\)-ethinylestradiol), and herbicides (e.g., atrazine) under both UV and visible light [92,112].
## 5 Results
### Parameter Sensitivity with Respect to Top-\(K\)  
Figure 6 presents the MAE and RMSE curves when the Top-\(K\) parameter is swept from 20 to 45 under matrix densities of 15%, 18%, and 20%. The lowest average errors are observed in the interval \(K=25\)-30.  
Figure 3: Comparison of actual vs. SE-TFF predicted daily flow for 2022, 2023, and 2024.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Improvement (\%)**}} & \multicolumn{10}{c}{**Matrix Density \(m\)d**} & \multirow{2}{*}{**Avg.**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**Cliff\(s\)\(\delta\)**} \\ \cline{3-14} \multicolumn{1}{c}{} & & & & & & & & & & & & & & \\ \cline{3-14} \multicolumn{1}{c}{} & Hybrid (\%) & 62.4\% & 61.8\% & 61.1\% & 59.3\% & 58.6\% & 58.4\% & 56.5\% & 56.2\% & 54.9\% & 54.8\% & 58.4\% & 0.005 & \(-1.0\) \\ \cline{2-14} \multicolumn{1}{c}{} & SVR (\%) & 67.1\% & 67.8\% & 67.9\% & 66.7\% & 65.1\% & 56.3\% & 55.1\% & 57.0\% & 55.2\% & 49.7\% & 60.8\% & 0.005 & \(-1.0\) \\ \cline{2-14} \multicolumn{1}{c}{} & ANN (\%) & 71.7\% & 70.5\% & 70.2\% & 67.8\% & 66.4\% & 68.0\% & 67.2\% & 67.5\% & 67.4\% & 68.0\% & 68.5\% & 0.005 & \(-1.0\) \\ \cline{2-14} \multicolumn{1}{c}{} & ARIMA (\%) & 28.1\% & 26.6\% & 27.0\% & 25.6\% & 24.4\% & 23.8\% & 21.5\% & 23.6\% & 22.5\% & 21.1\% & 24.3\% & 0.005 & \(-1.0\) \\ \cline{2-14} \multicolumn{1}{c}{} & ARMAX (\%) & 35.3\% & 34.7\% & 34.2\% & 33.1\% & 32.3\% & 28.4\% & 27.0\% & 21.2\% & 20.6\% & 20.8\% & 27.7\% & 0.005 & \(-1.0\) \\ \hline \multirow{14}{*}{RMSE} & Hybrid (\%) & 72.9\% & 71.9\% & 71.4\% & 67.7\% & 65.0\% & 63.6\% & 60.0\% & 68.1\% & 65.6\% & 63.2\% & 66.9\% & 0.005 & \(-1.0\) \\ \cline{2-14} \multicolumn{1}{c}{} & SVR (\%) & 61.0\% & 60.5\% & 59.8\% & 58.7\% & 57.7\% & 56.6\% & 55.5\% & 53.8\% & 52.0\% & 54.5\% & 57.0\% & 0.005 & \(-1.0\) \\ \cline{1-1} \cline{2-14} \multicolumn{1}{c}{} & ANN (\%) & 81.6\% & 81.0\% & 79.8\% & 74.7\% & 70.3\% & 66.5\% & 64.2\% & 62.0\% & 59.5\% & 59.7\% & 69.9\% & 0.005 & \(-1.0\) \\ \cline{1-1} \cline{2-14} \multicolumn{1}{c}{} & ARIMA (\%) & 32.0\% & 31.2\% & 30.7\% & 30.7\% & 30.6\% & 30.1\% & 30.6\% & 28.4\% & 26.1\% & 27.6\% & 29.8\% & 0.005 & \(-1.0\) \\ \cline{1-1} \cline{2-14} \multicolumn{1}{c}{} & ARMAX (\%) & 33.0\% & 32.4\% & 31.0\% & 31.7\% & 28.3\% & 27.8\% & 27.9\% & 27.5\% & 26.1\% & 24.8\% & 28.1\% & 0.005 & \(-1.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance improvement comparison for different matrix density levels.
## 4 Simulation Environment
### Scheduling of Radio Resources, Precoding, and Power Allocation
The maximum number of spatial layers per RU is selected to ensure a reasonable processing time for ZF precoding. In step 16, power is allocated by splitting it equally among all spatial layers, independently within each RU. Power allocation in the UCCF M-MIMO network is a topic of significant importance. While some recent works address this problem, they are not designed for such a complicated system model utilizing an MCS, a scheduler, and a realistic radio channel. Moreover, it remains a challenging issue for the research community [6]. Existing power allocation algorithms are either optimized under a simplified system model, characterized by significant computational complexity, or unscalable. Instead, we utilize baseline power allocation, as presented in [3], which is characterized by low complexity and ensures the scalability of the UCCF M-MIMO network, i.e., equal power allocation among spatial layers within each RU. Having done this, the expected \(SINR_{i,r}\) can be computed for each UE that has the \(r\)-th RB scheduled using (4). The calculation of the SINR is performed in two places within the simulator: when scheduling users and when calculating the achieved rate. The first application is more challenging as it is based on channel estimates, calculated intra-serving cluster interference, and estimated inter-serving cluster interference. The channel between each UE and its serving RUs is estimated based on the uplink Sounding Reference Signal (SRS), and the knowledge of the utilized precoding can be used to calculate intra-cluster interference. Inter-cluster interference is assumed to be based on a user historical report similar to the Channel Quality Indicator (CQI), Reference Signal Received Quality (RSRQ), or SS-SINR, as outlined in the 5G specifications [23]. Using the expected \(SINR_{i,r}\), the \(newSumRate\) can be computed based on the Shannon formula. If the \(newSumRate\) is higher than the previously computed \(sumRate\), the new spatial layer is created and the \(k\)-th UE has \(\delta_{k,r}\) set to 1. Otherwise, it is removed from the \(allocatedUEs\), and the loop breaks, as shown in steps 19-25 of the algorithm. After that, the procedure is repeated for the next RB.
## 4 Results of Optical-System Design and Simulation
### Telescope  
The telescope design has an off-axis dual-paraboloid mirror structure, of which the primary and secondary mirrors are eccentric along the \(Y\) axis. It has the advantage of higher optical efficiency due to having fewer components compared with a traditional Cassegrain telescope, and it is suitable for small FOV systems because the parabolic mirror can perfectly image the on-axis point, but cannot meet the aplanatic condition.  
Figure 4: (a) FOV of pointing system (green) and spectrometers (red CH1, blue CH2) when ideally aligned. (b) Layout of final system FOV.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wavelength coverage & Spectral: 2–8 μm; pointing: 0.5–2 μm \\ F number & 14.754 (2–4 μm); 7.377 (4–8 μm) \\ Resolving power & \(\sim\)100 (2–4 μm); \(\sim\)30 (4–8 μm) \\ SNR & \(\sim\)7 \\ Entrance aperture & \(\geq\)1.5 μm \\ System throughput & \(\geq\)0.4 \\ FOV & Diffraction limited: 27.9′′; unvigneting: 44′′ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of optical indices.  
The primary and secondary mirrors are both circular aperture mirrors. There is a focus point between the two mirrors that can be used to limit stray light. The telescope collects parallel beams from distant astronomical targets by the 2.1 m diameter primary mirror, and the beams are then compressed as collimated beams with smaller apertures and injected into the dichroic system. The detailed optical design parameters of the telescope are listed in Table 6.  
The telescope's optical system meets the performance requirements. The spot diagram (Figure 5a) on the image surface of the telescope system shows that the spot radius at a wavelength of 4 \(\upmu\)m in 7 characteristic sampling fields (as shown in the 7 boxes in the figure) is close to zero, far less than the Airy radius of 0.02905 mrad. The spot diagram indicates that the radius of the geometric encircling circle of the image point is much smaller than that of the Airy spot at the central wavelength, which means that the telescope can realize diffraction limit imaging. Figure 5b is a field map of the root-mean-square (RMS) wavefront error (WFE) at the imaging surface, which is used to show WFE distribution according to the FOV. As shown in Figure 5b, the RMS WFE at 4 \(\upmu\)m wavelength was less than 7.6 nm (0.0019 \(\lambda\)) in full FOV, far less than the diffraction limit of 288 nm (0.072 \(\lambda\)).
## 3 Methodology
### Feature Extraction
#### 3.2.1 Peak Envelope for Each Channel  
Peak envelope analysis aims to detect and emphasize the signal peaks or maximum amplitudes. For a given signal \(x_{band,hemisphere}[n]\), the peak envelope \(e_{band,hemisphere}[n]\) can be computed using the Hilbert Transform (HT). The peak envelope is expressed mathematically using Equation (2):  
\[e_{band,hemisphere}[n]=\sqrt{x_{band,hemisphere}[n]^{2}+\hat{\text{h}}_{band, hemisphere}[n]^{2}} \tag{2}\]  
with \(\hat{\text{h}}_{band,hemisphere}[n]\) the HT of a real valued signal \(x_{band,hemisphere}[n]\). The HT is defined as the convolution operation (\(\otimes\)) of the signal \(x_{band,hemisphere}[n]\) with the filter impulse response \(h[n]\). This is mathematically represented by Equation (3):  
\[\hat{\text{h}}_{band,hemisphere}[n]=x_{band,hemisphere}[n]\bigotimes h\ [n] =\sum_{k=-\infty}^{\infty}h\ [n-k]\times x_{band,hemisphere}\ [k] \tag{3}\]  
Figure 4: FFT representation of each decomposed EEG band (Delta, Theta, Alpha, Beta, and Gamma) in the frequency domain after zero-phase filtering. Each color in each sub-band represents an EEG channel. The sixth-order Chebyshev Type-II band-pass filters, with a stopband attenuation of 50 dB, effectively separate the EEG signal into distinct frequency ranges, ensuring minimal signal distortion and clear isolation of each band.  
where \(\rm{h}[n-k]\) is the HT filter impulse response; a 10-tap HT filter was used, which specified the number of samples around each local maximum considered for interpolation.
## 3 Proposed Scheduler to integrate the MES and AIV
### LabVIEW-AIV Communication
#### 3.2.2 LabVIEW Application  
As mentioned in Section 1, the LabVIEW add-on tool '_ROS for LabVIEW_' is used here. This LabVIEW tool is used to communicate with ROS applications and can handle node-to-node transport negotiation and communication setup using TCP as its transport mechanism.  
The LabVIEW Publisher and Subscriber continuously run using a '_While Loop_' to send and receive messages to/from ROS applications running on the AIV. The '_ROS for LabVIEW_' tool includes a set of VIs that establish a connection with ROS applications. Some of these VIs are used to design the Publisher and Subscriber node as shown in Figure 8. Each of these VIs performs specific tasks, as explained below.  
The ROS node is initialized using ROS_Topic_init VI, which takes the following as inputs (a) topic name, (b) topic type, (c) action (Publisher or Subscriber), (d) update rate, and (e) queue size. The node name (/LV1) is also assigned using the '_node_' terminal of the mentioned VI. The ROS_Topic_init VI is connected to ROS_Topic_Read VI (in case of Subscriber) and ROS_Topic_Write VI (in case of Publisher). The ROS_Topic_Read VI is used to receive messages and ROS_Topic_Write VI is used to send messages. The messages are sent to the AIV in the form of strings. The Subscriber and Publisher topics are closed using ROS_Topic_Close VI.  
Figure 8: Block diagram of LabVIEW Publisher/Subscriber node. (**a**) Publisher; (**b**) Subscriber.