esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations  
Traditional substations, designed in the late 20 th century, rely on analogue control and legacy communication protocols like Modbus and Distributed Network Protocol (DNP), which introduce specific vulnerabilities. While still operational, many have been supplemented with additional monitoring systems. Communication between master and slave devices occurs via fiber converters, as shown in Figure 3. A failure in a fusion unit could compromise circuit protection, exposing weaknesses in this architecture.  
With the development of digital technologies, the substations move from legacy analogue systems to fully integrated digital solutions. As described in [16], the architecture of the substations may involve several communication protocols coexisting due to the gradual development towards modernization. In [17], the detailed review of digital substations has shown increasing requirements for secure systems and algorithms regarding access restrictions and cyber threat detections. It also stresses that personnel operating smart substations should have sophisticated skills related to network and data analysis. The authors trace the evolution of communication protocols and automation systems, showing how these digital environments are increasingly complex environments [18]. Figure 4 presents a typical architecture for fast bus protection schemes using proprietary protocols. Once more, the relays with limited communication capability use converters for fiber communications  
Figure 3: Typical communication architecture using Modbus/DNP protocols.  
to guarantee the effectiveness of the communications means to ensure the robustness of the protection of the power system. Figure 5 illustrates the more enhanced configuration, wherein the relays that have native IEC 61850 station bus capabilities are interconnected via an Ethernet switch and have direct fiber optic outputs from the relays. This allows the relays to perform internal logic and the protection signals to communicate without additional devices managing the logic, a serious enhancement in substation technology. Finally, Figure 6 presents a protection automation and control architecture based on IEC 61850, where Generic Object Oriented Substation Event (GOOSE) messages and sampled values are utilized. Relays subscribe to the needed sampled values for protection and metering with significant increases in design efficiency. The instrument's transformer wiring is minimal, as all signals can be transmitted over the same Ethernet fiber network. This is another way in which modern substation integration may be improved and reliability increased. Figure 7 shows a simplified view of the transition from legacy to centralized substation; we are currently at what would be described as the fourth generation of substation.  
Figure 4: Standard architecture for implementing a fast bus protection scheme using a proprietary protocol.  
Figure 5: Typical architecture used to implement a fast bus protection scheme using IEC 61850.
## Appendix C Qualitative Analysis
### Impact of Temperature on Prompt Techniques
#### 3.1.3 Retrieval-Augmented Generation Prompting  
Low temperatures in RAG prompt consistent delivery of structured, precise, and well-supported diagnostics. All models, particularly Mistral and Gemini, excel in this setting, providing well-justified diagnoses grounded in clinical evidence. The diagnostics are clear and concise, with a balanced number of diagnoses that avoid overwhelming the user. However, in complex cases, particularly in Medichat Llama 3, the output can become dense, requiring more effort to extract key information.  
High-temperature RAG prompting introduces a broader range of diagnoses, but the trade-off comes in reduced coherence and precision. In Mistral, speculative reasoning increases, diluting diagnostic certainty. Medichat Llama 3 suffers from more varied yet speculative and sometimes unfocused outputs. While this broader scope can be beneficial for exploratory purposes, it makes the outputs less reliable for routine clinical applications. High temperatures in RAG prompting are better suited for generating creative possibilities rather than definitive, actionable diagnostics.
# Optical Injection Locking for Generation of Tunable Low-Noise Millimeter Wave and THz Signals
## 5 Conclusions  
This work presented a detailed study of optical injection locking in combination with optical frequency comb for generation of tunable, high-quality sub-THz and THz signals for applications in communications and spectroscopy. Performance improvement in terms  
Figure 8: Fine-tuning of the RF tone generated with two locked SLs.  
Figure 9: Phase noise of two locked lasers before (**a,b**) and after combining (**c**).  
of frequency stability and linewidth of the single laser during injection locking was shown. The linewidth of the beating tone of two lasers was reduced from more than 100 MHz to below 100 kHz. Phase noise measurements for different injection ratios and different master tone frequency offsets were conducted. It was shown that for frequency offsets below 100 kHz, phase noise of the slave laser follows the phase noise of the master for any injected power, but for higher offsets, additional phase noise was observed. The locking range was measured for different injection ratios. Selecting two lines from the OFC by means of IL and generation of RF signals was demonstrated. Tuning mechanisms were shown for two cases: coarse-tuning by selecting different sets of optical tones from OFC; fine-tuning by changing the repetition rate between the comb lines. It was also shown that the phase noise of the resulting RF signal is a sum of the phase noise of two separate signals generated with the slave lasers. This work proves the feasibility of the concept of generating of low-noise sub-THz and THz signals by means of injection locking.  
[PERSON] and [PERSON] developed the system concept and designed the experiment. [PERSON] executed the experiment and data collection and performed the data analysis. [PERSON] designed the DBR lasers. [PERSON], [PERSON], [PERSON] and [PERSON] discussed the results and their interpretation. [PERSON] and [PERSON] wrote the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] revised and corrected the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the NWO HTSM project FREEPOWER under project 17094.  
Not applicable  
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 4 Results and Discussions  
### Metrics  
The articles that were selected for this study were based on metrics such as article publishers, journals, citation numbers, and the impact factors. Articles from conferences proceedings were also considered. The details of the articles selected are presented in Table 4. The largest number of articles were selected from IEEE with 46 articles, followed by Springer and Elsevier with 37 articles and 30 articles, respectively. Inderscience, MDPI, and IOP publishing, respectively, had six, five, and three articles each. PMC, ProQuest, and ScitePress have two articles each, while all other publishers have one each. Thirty-two of the articles were indexed in Science, twenty-four in WOS, sixty-one in Scopus, sixty-six in Google Scholar, and twenty-two in DBLP. All the articles were gathered between 19 May 2021 and 23 June 2021.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 3 Experiment II: Dummy-Head MOBRIR vs. ARIR
### Listening Experiment: Implementation  
While ARIR-based rendering could be implemented in a multichannel DAW (e.g., Reaper) by using freely available convolution ([[http://www.mathiaskronlachner.com/?p=1910](http://www.mathiaskronlachner.com/?p=1910)]([http://www.mathiaskronlachner.com/?p=1910](http://www.mathiaskronlachner.com/?p=1910))), rotation, and rendering plug-ins ([[https://plugins.iem.at/](https://plugins.iem.at/)]([https://plugins.iem.at/](https://plugins.iem.at/))), there is no tested and easy-to-use plug-in for MOBRIR rendering, yet. To rule out any effects due to different implementations, we used the _pure data_ implementation as described in Section 2.3 to also emulate ARIR-based rendering. To this end, we evaluated the ARIR BRIRs according to [22] to get a \(\Delta\varphi=1^{\circ}\) MOBRIR (for each ear)  
\[AMOBIR_{q}(t)=\sum_{\tau=0}^{T-1}\sum_{n=0}^{N}\sum_{m=-n}^{n}b_{nm}(\tau)\sum _{m^{\prime}=-n}^{n}r_{n}^{mm^{\prime}}(q\Delta\varphi)\ h_{nm^{\prime}}(t-\tau), \tag{8}\]  
where \(b_{nm}(t)\) is the FIR binaural Ambisonic renderer, \(h_{nm^{\prime}}(t)\) is the ARIR of the order \(N\), \(q\) is the orientation index, and \(r_{n}^{mm^{\prime}}\) is the Ambisonic rotator. As binaural renderer \(b_{nm}(t)\), we employed the one from [37] with KU100 HRIRs measured for 2702 directions [21]. The resulting AMOBRIR was linearly interpolated like the reference condition.  
For playback, we again used AKG K702 headphones equipped with the IEM headtracker [8] and the experiment was conducted in a quiet office room.
## 4 Clustering Algorithm
### Initialization  
Our proposed protocol consists of two phases, i.e., the setup phase and the steady-state phase. Cluster head (CH) selection and cluster formation are performed in the setup phase, while data transmission is performed in the steady-state phase. Initially, the number of members and an optimal number of clusters is obtained for the given \"\(N\)\" value. Then, each node in the network broadcasts a message to the neighbors for the nomination of the cluster head (CH). Finally, all the information is collected by the base station and saved.
# Analysis of Potential Markers of Pork Freshness Based on Volatile Organic Compounds
## 3 Results and Discussion  
### Analysis of Pork TVC and TVB-N  
TVC and TVB-N are used as critical indicators for assessing pork freshness, with their variations throughout the storage period illustrated in Figure 1. Initially, pork exhibited a TVC value of 4.02 \(\pm\) 0.1 log CFU/g, which increased steadily to 6.39 log CFU/g by day 6, exceeding the spoilage threshold of 6-7 log CFU/g for fresh meat, as reported by [34].  
TVB-N acts as an indicator of protein degradation, signifying the aggregate of ammonia and other volatile amines produced by microbial activity and endogenous enzymes [35]. Initially, the TVB-N level in pork registered at 9.8 mg/100 g, rising gradually to 14 mg/100 g by day 3, just below the acceptable threshold. Initially, TVB-N concentrations exhibited a gradual increase for two primary reasons: first, microbes preferentially utilize carbohydrates as substrates in the early stages [36]; second, microbes need a period of adaptation and growth, during which their metabolic activities have not yet peaked, resulting in a slower increase in TVB-N levels. Subsequently, TVB-N content enters a rapid growth phase, showing an exponential increase in later storage stages as microbial populations and metabolic activities accelerate [35]. According to the Chinese National Food Safety Standard GB 2707-2016 (Sanitary Standard for Fresh and Frozen Livestock Meat) [37], the permissible TVB-N level in fresh meat must not exceed 15 mg/100 g.  
Figure 1: Changes in total viable count (TVC) and total volatile basic nitrogen (TVB-N) in pork over different storage periods (6–8 °C). Error bars represent standard deviation (SD). TVC value (A), TVB-N value (B). Different superscripts (a–g) indicate statistically significant differences (\(p<0.05\)).
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## Materials and Methods
### Satellite Based Yield Estimation
#### 2.4.1 Agricultural Production Systems Simulator (APSIM) Simulation  
The Agricultural Production Systems Simulator (APSIM)-Maize model was used to generate 50 simulations for each hypothetical site using a typical range of local sowing dates (Figure S4), initial soil moisture (i.e., 60%, 80%, and 100% of the total extractable soil water), fertilizer rates (i.e., 0, 25, and 50 kg/ha N), and seeding rates (i.e., 3, 4, and 5 plants/m\({}^{2}\)). The simulated canopy nitrogen content was then converted to daily VIs (e.g., GCVI) based on relationships obtained in field experiments [18] and along with simulated yields were used to build the predictive linear regression model. We used a single cultivar Hybred511, which gives the typical maize phenology in this region, and a single predefined soil profile for the Katumani, Kenya, research station within APSIM for all simulations. Further varying the cultivar maturity length and soil profile properties had nominal impact on yield estimation. Weather inputs to the APSIM model, including daily maximum and minimum temperature, precipitation, and solar radiation, were obtained from the aWhere weather database. We randomly selected two hypothetical sites (i.e., 0.418\({}^{\circ}\)N, 34.768\({}^{\circ}\)E and 0.682\({}^{\circ}\)N, 34.801\({}^{\circ}\)E) to generate APSIM simulations for the B-K sub-region, and another seven sites for the 1 AF region (Figure 1).
## 3 Results and Discussion
### Radiant Heat  
In Figure 9, the temperatures of tested linings' dependence, \(t\), on the exposure time of the outer shell to radiant heat are presented.  
The chart shows a pronounced difference in the heating process of assemblies containing phase-change materials (\(A\) and \(C\)) and reference assemblies (\(B\) and \(D\)). As one can easily see, the assemblies with inserts containing PP macrogranules heat up much faster. The course of the curves showing the temperature increase in assemblies containing PCM inserts can be divided into two stages. The first stage lasts for about 2.5 min of the experiment. At this stage, a significantly lower temperature rise rate \(R_{\mathrm{T}}\)\({}^{0-2.5}\) of these assemblies can be observed (2.12 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) for assembly \(A\), 2.49 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) for assembly \(C\)) compared to the corresponding reference samples (6.86 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) for assembly \(B\), 8.43 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) for assembly \(D\)). The reason for the slower heating of the PCM-containing assemblies was the isothermal phase transition (melting) of n-octadecane, which was ongoing at this stage of the experiment. Due to the significantly higher heat flux in the radiant heat method compared to the _contact heat_ method, in Figure 9, the two-stage process of the temperature increase during the phase transition of n-octadecane was not revealed (which was revealed in Figure 8). As one can see in Figure 9, the applied radiant heat flux (5 kW\(\cdot\)m\({}^{-2}\)) used in this method caused-- after about 15 s (0.25 min), in both assemblies (\(A\), \(C\))--the entire amount of n-octadecane to reach the melting temperature. As a result, intensive melting of the n-octadecane occurred. The time interval (0-0.25 min) is the time taken for the heat to transfer from the outer shell (exposed to radiant heat) to the lining. Similarly, for the contact heat method on the basis of the course of the curves, the values of time gains, \(\Delta t_{\mathrm{AB}}\) and \(\Delta t_{\mathrm{CD}}\) (2.12 and 1.75 min, respectively), were calculated and are marked in Figure 9.
# MoSe\({}_{2}\) with Ultra-Fine Pt Decoration for Efficient Photodegradation
## 3 Results and Discussion
### Chemical States, Mechanism of Efficient Photocatalytic Reactions  
The chemical electronic states of the MoSe\({}_{2}\)-Pt samples were evaluated through XPS. The high-resolution XPS spectra of Pt 4\(f\) and Se 3\(d\) are shown in Figure 3. In Figure 3a-c, there are two Pt species which were identified by fitting the original Pt 4\(f\) spectra. Pt\({}^{0}\) peaks are the reduced Pt nanoparticles in metal form, and Pt-Se peaks are attributed to the Pt-Se bonding. The binding energies of these Pt peaks are slightly shifted among the three samples which may be caused by partial electron transfer between MoSe\({}_{2}\) and Pt [31]. In Figure 3d-f, two main contents are found in the Se 3\(d\) spectra, which are the Se-Mo peaks and Se-Pt peaks. Such results are typical for MoSe\({}_{2}\) and PtSe\({}_{2}\) characteristics [32], and further confirm the formation of Se-Pt bonding. Therefore, it is obvious that chemical bonding of Pt-Se formed after the synthesis. Such chemical bonding can provide an effective pathway for charge transportation during photocatalytic reactions [9]. The Se-Mo 3\(d\)5/2 peaks at 54.56 eV are barely shifted among the three samples, suggesting that MoSe\({}_{2}\) takes a dominant content compared to the Se-Pt species.  
Based on the XPS results, it is noticed that the relative contents of Pt-Se varies among the samples, which may have influence on electron transportation. The relative contents of Pt-Se can be calculated from Pt 4\(f\) spectra, which are 34.78%, 40.02%, and 19.79%, respectively. It suggests that too much MoSe\({}_{2}\) involved in the synthesis (the M3 Pt sample) may limit the formation of Pt-Se bonding.  
In this case, there may be two ways that the photocatalytic abilities could be affected. Firstly, Pt-Se bonding provides an efficient pathway for charge transfer, so more contents of Pt-Se would be beneficial to the photocatalytic reactions. Secondly, the existence of Pt nanoparticles (Pt\({}^{0}\)) can be confirmed by TEM results in Figure 1. Such ultra-fine Pt nanoparticles offer active sites for photocatalytic reactions where degradation could take place [33]. Introducing noble metal nanoparticles on edge sites of MoSe\({}_{2}\) is a typical method for enhancing the photocatalytic properties.
# Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles
## Results  
### Baseline Correction  
A simple baseline correction method Equation (1) was applied to each of the measured ChIP spectra (examples in Figure 4). Predictably, the baseline correction had a larger effect on the red ChIP tail wavelengths, where the filter transmittance and possible spectrometer resolution effects discussed in Section 2.3 were expected to have a larger impact. Importantly, the fact that REF3, a reference measurement acquired under the same conditions but with a Spectralon@panel instead of a leaf sample (Figure 4), presented non-zero values across the ChIP spectral range denoted that the baseline correction affected all ChIP wavelengths. The magnitude of the correction varied, however, from case to case, and from method to method, in part due to differences in leaf reflectance among samples (Supplementary Material, Figure S5). Leaf reflectance tended to be higher in the OC compared to FW and IS methods for birch and lignonberry, something expected due to inclusion of the specular reflectance component in OC, and which translated into an increased baseline correction. Interestingly, this was not the case for pine needles, which presented larger reflectance in FW than in OC and especially in IS. The performance of the baseline correction was also demonstrated in birch leaves illuminated with light sources with contrasting emission spectra: a halogen light (with substantial emission in the NIR) and a white LED light (with very small emission beyond 750 nm (see Supplementary Material, Figure S4)). Although the correction effect near the cut-off filter region was larger for the halogen light source compared to the LED, similar levels of correction were found across the rest of the ChIP spectral range, providing direct evidence for the saturation effect (I) described in Section 2.3. Baseline correction served also to corroborate that the black tape used as background in the OC method was indeed non-fluorescent for the range 680-850 nm (Supplementary Material, Figure S1). However, the remains of the uncorrected signal around the filter cut-off area reflect that the baseline correction method presented here still leaves room for improvement.  
After correction, ChIP spectra were close to zero above 850 nm in OC and FW, but this was not the case for the IS method, where the baseline correction clearly undercorrected the spectra throughout (Figure 4). It is important to note also that the slight increase in REF3 with wavelength observed across methods was not present in the raw data (digital counts) and was a result of the radiometric calibration function. Unless stated otherwise, all plots presented hereafter represent baseline-corrected ChIP spectra.
## 5 Experimental Results and Discussion
### Digit Recognition Results using Deep Transfer Learning
#### 5.3.2 CNN-Based Handwritten Arabic Digit Recognition on the Local Data  
We tested the CNN architecture on the local test dataset. We observed the accuracies of training and test set for 100 epochs (see Figures 10 and 11). We obtained 80% accuracy on the separate test set which is promising and outperformed the DTL accuracies. Both datasets (HODA and AHDBase) are recorded recently (after the 2000s). Therefore, their quality is higher than manuscripts recorded in the 1840s. Therefore, when we trained and tested a CNN model in our local dataset, the system also learns the properties of historical documents. However, when the DTL method is applied from these modern datasets, they could not capture the properties of these historical manuscripts. That could explain the relatively lower performance of DTL techniques. These results are also higher than learning CNN directly from the local data (80%), which shows the advantage of using DTL based feature extraction in our dataset.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **DTL from AHDBase** & **DTL from HODA** \\ \hline LDA & 58.6 & 46.2 \\ \hline RF & 62.1 & 58.6 \\ \hline MLP & 72.4 & 65.5 \\ \hline SVM & 57.9 & 49.0 \\ \hline kNN & 64.1 & 62.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained by applying different classifiers to extracted features by using deep transfer learning. DTL from AHDBase and HODA datasets are shown in separate columns.  
Figure 10: Training and test accuracies of CNN-based handwritten Arabic digit recognition system by epochs are shown. The local dataset is separated to 80% training and 20% test sets.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Accuracy** & **F-Measure** & **Area under ROC** \\ \hline kNN & 84.61 & 0.829 & 0.964 \\ \hline RF & 89.74 & 0.897 & 0.970 \\ \hline MLP & 92.31 & 0.923 & 0.977 \\ \hline SVM & 89.74 & 0.897 & 0.944 \\ \hline LDA & 92.31 & 0.923 & 0.958 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results obtained by applying different classifiers to extracted features by using deep transfer learning. Results obtained with applying DTL from the local CNN architecture are shown.