esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Example and Validation
### Experiments  
In order to validate the proposed methodology experimentally, a modified Texas Instruments High Voltage Single Phase Inverter Kit [56] (Figure 13) was utilized.  
The control algorithm was realized adopting first-order hold digitization [57] by a TMS320F28335 DSP-based control card under an average-current-controlled inner loop. The utility was emulated by an APS-7100 Gw Instek programmable AC power source operating as 50 Hz/60 Hz \(\pm\) 1%, 230V\({}_{\mathrm{rms}}\) sinusoidal voltage source. The M9715B Maynuo DC electronic load functioning in constant power mode was employed to emulate the power load \(p_{L}\) (cf. Figure 9).  
During the simulation framework, it was shown that frequency variations have a negligible influence on the shape of transient responses; hence, the latter were recorded for nominal mains frequency values only. The corresponding results are depicted in Figure 14, matching well the corresponding simulation results demonstrating \(\sim\)10 V DC link deviations upon a step-like load variation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\omega_{C}\)/2\(\pi\) & **49.5 Hz** & **50 Hz** & **50.5 Hz** & **59.4 Hz** & **60 Hz** & **60.6 Hz** \\ \hline THD\% & 5 & 0.1 & 4.52 & 3.98 & 0.067 & 3.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSIM-measured THD in simulations.  
Figure 13: Texas Instruments High Voltage Single Phase Inverter Development Kit.  
The steady-state performance under a rated load around 50 Hz and 60 Hz mains is demonstrated in Figures 15 and 16, respectively. Prior to the experimental framework, the AC power source voltage THD was measured and found to be 0.5%, as shown in Figure 9 in [22]. This value should be taken into account in order to correctly interpret the measured values of _THD\({}_{i}\)_. Table 2 summarizes the corresponding steady-state THD values. It is evident that the system designed according to the proposed approach complies well with _THD_*.  
Figure 14: Experimental results. DC link voltage response to load increase for (**a**) \(\omega_{\text{C}}=2\pi\cdot 50\) rad/s, and (**b**) \(\omega_{\text{G}}=2\pi\cdot 60\) rad/s.  
Figure 15: Experimental results, 50 Hz mains. Steady-state performance under (**a**) \(\omega_{\rm G}\) = 2\(\pi\)-49.5 rad/s; (**b**) \(\omega_{\rm G}\) = 2\(\pi\):50 rad/s; and (**c**) \(\omega_{\rm G}\) = 2\(\pi\):50.5 rad/s.  
Figure 16: Experimental results, 60 Hz mains. Steady-state performance under (**a**) \(\omega_{\rm G}=2\pi\cdot 59.4\) rad/s; (**b**) \(\omega_{\rm G}=2\pi\cdot 60\) rad/s; and (**c**) \(\omega_{\rm G}=2\pi\cdot 60.6\) rad/s.
# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 5 Discussion
### Comparison with Previous Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Techniques** & **Datasets** & **Challenges** & **Results** \\ \hline [PERSON], 2025 [19] & Multimodal framework (MANM, GFRM, CFLM) & MAMI, MMHS150K & Difficulty interpreting irony/sarcasm & Macro-F1: 11.87\% on MAMIS150K \\ \hline [PERSON], 2021 [20] & Neural architecture with embeddings, BERT & Evalita 2018 & Handling multi-label nature & Misogyny detection: F 0.796, Acc. 0.812; Classification: Fw 0.644, Frac. 0.541, Acc. 0.657 \\ \hline [PERSON], 2021 [21] & BLISTM, BiGRU & Urban Dictionary & Subtle, context-dependent language & BLISTM: Acc 90.12\%, Sens 87.53\%, Spec 92.41\%; BiGRU: Acc 93.10\%, Sens 92.08\%, Spe 93.96\% \\ \hline [PERSON], 2024 [22] & CNN, BiLSTM, ResNet50 & EXIST 2024 & Complex multimodal data analysis & Text: P 0.72, R 0.72, F1 0.71, Acc. 0.73; Image: P 0.63, R 0.63, F1 0.62, Acc 0.66 \\ \hline [PERSON], 2019 [24] & Linguistic feature analysis & IberEval 2018, Evalita 2018 & Distinguishing between misogyny and sexism & Acc 84\%, Prec 81\%, Rec 83\%, F1 82\% \\ \hline [PERSON], 2022 [25] & GCNs, neural networks & Spanish Tweets, AMI task & Cultural variance in language detection & Acc 77\% (Spanish tweets) \\ \hline [PERSON], 2021 [26] & Attention-Based LSTM & AMI Datasets & Multilingual data and context sensitivity & Cross-domain: F1 90.9\%, Acc 90.9\% \\ \hline [PERSON], 2018 [27] & ML models with NLP attributes & Custom Twitter Dataset & Misogyny categorization complexity & Best SVM Accuracy: 79.95\%, Best RF Macro F1: 36.51\% \\ \hline [PERSON], 2020 [29] & BERT multi-task learning & TRAC-2 Dataset & Subtlety and indirectness of aggression & ENG-B: F1 0.8579, Acc 0.8550; HIN-B: F1 0.8008, Acc 0.8042; BEN-B: F1 0.9206, Acc 0.9242 \\ \hline [PERSON], 2023 [30] & GPT-3-5 zero-shot detection & Reddit & Prompt response consistency & Accuracy: 91\%, Macro F1: 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of previous work in online misogyny detection. framework to overcome these shortcomings. This enhancement not only addresses the deficiencies observed in earlier models but also boosts the generalizability of the detection mechanisms across different languages and cultural contexts.
###### Abstract
Papers reported in the literature [38; 39] have placed capacitance on the high voltage AC side for AC grid-forming and included its dynamics in the control loop. It results in reactive power consumption and increases filtering cost, where MMC generates a small number of harmonics. In almost all of these articles mentioned above for referencing, non-real-time software like Matlab Simulink and PSCAD/EMTDC were used to model and prepare results against different kinds of fault. In PV systems, the two most widely used MPPT controllers are perturb & observe method (P&O) [40; 41; 42] and incremental conductance (IC) method [43; 44; 45] for tracking the maximum power point due to the variation of irradiance and temperature. Although IC is computationally expensive, it tracks the maximum power point (MPP) more rapidly than the P&O method. However, real-time implementation of IC creates problems as the algorithm contains the PV voltage difference in the denominator, which usually does not change during a sudden change in irradiance. In this work, the MMC detailed model-based PV-battery connected HVDC network is formed. Active power reduction-based control strategy for PV is developed to replace the dynamic braking resistor and improve the fault ride-through capability during the AC side balanced and unbalanced fault. Furthermore, a control strategy is made for the battery to regulate the excess energy in the MMC-HVDC system and mitigate the energy shortage of PV panel caused by the temperature change and sudden cloud passing. Modified incremental conductance-based optimum solar energy tracking is carried out. Feedforward controller is employed to create the standalone AC grid instead of capacitance-included decoupled controller. A complete system with all detailed model-based converters and controllers is modelled and simulated in real-time digital simulation (RTDS). The efficacy of active power reduction technique and battery-based control strategy for the MMC-HVDC system is evaluated against the severe balanced and unbalanced disturbances at the point of common coupling of AC grids and fluctuation of the solar radiation and temperature. The following shortcomings in current studies are to be addressed as the main contributions of this research:  
1. Active power curtailment of PV-Battery integrated MMC-HVDC system has not been reported;
2. Local voltage signal based PV-Battery control for the HVDC link voltage protection has not been addressed;
3. Detailed model-based complete system with real-time simulation results has not been presented.
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
## 3 Phase Offset Estimation Algorithm  
### Viterbi-Viterbi Phase Offset Estimation Algorithm  
According to Equation (6), raising to the fourth power, we can obtain:  
\[{s_{k}}^{4}=e^{i4(a_{k}+\theta_{k})}=e^{i4a_{k}}\cdot e^{i4\theta_{k}}=e^{i4 \theta_{k}} \tag{9}\]  
because \(e^{i4a_{k}}=1\). Then, the phase offset can be obtained as:  
\[\theta_{k}=\frac{1}{4}arg(s_{k}^{4}) \tag{10}\]  
The above phase offset calculating method described by Equations (9) and (10) is called the Viterbi-Viterbi phase offset estimation (VVPE) algorithm.  
Figure 1: The schematic of coherent receiver based on DSP (digital signal processing).  
In reality, each symbol at the receiver includes the channel gain and the addictive noise as is expressed in (5). Given that the channel gain is 1, the received symbol with noise is:  
\[r_{k}=s_{k}+n_{k}=e^{j(a_{k}+\theta_{k})}+n_{k} \tag{11}\]  
and raising the symbol to the fourth power, the result is:  
\[r_{k}^{4}=\left[e^{j(a_{k}+\theta_{k})}+n_{k}\right]^{4}=e^{j4(a_{k}+\theta_{k })}+4e^{j3(a_{k}+\theta_{k})}n_{k}+o(n_{k}) \tag{12}\]  
In the small angle approximation, the angle of \(r_{k}^{4}\) is [8]:  
\[arg(r_{k}^{4})=4\theta_{k}+\delta(\theta_{k})n_{k}+o(n_{k}) \tag{13}\]  
where \(\delta(\theta_{k})\) is a variable related to phase offset. The VVPE error is of the order of \(\frac{\delta(\theta_{k})n_{k}}{4}\). To suppress the effect of the noise on the phase offset estimation error, a commonly used method is to average the estimation values over a sequence of symbols. The more accurate estimation result can be calculated by [16; 17]:  
\[\theta_{k}=\frac{1}{4}arg(\sum_{i=1}^{N}r_{i}^{4}) \tag{14}\]  
This method processes the symbol sequence in blocks and the block length \(N\) influences the estimation error. Owning to this method is similar as a mean filter, we define it as VVPE-MF. Two block length values \(N=6\) and \(N=12\) are employed in this paper.
## 2 Theory and Methods
### Model Improvements
#### 2.2.2 P2-LSCSbd  
Small-target detection tasks require the integration of multi-scale information to effectively capture features across different scales, which is especially critical for accurately detecting and recognizing small targets. Multi-scale fusion significantly enhances the model's sensitivity to small targets while maintaining the detection capabilities for larger objects. In this paper, we introduce the P2 layer into the original Neck network and combine it with shared convolution and independent BN (Batch Normalization) operations in the Head layer to design a new lightweight detection head, P2-LSDSBD (P2 with Lightweight Shared Convolutional Separate BN Detection). This design achieves both multi-scale information fusion and lightweight model operation.  
The P2-LSDSBD detection head leverages P2 layer information on top of the original YOLOv8 network, enhancing the model's ability to detect small targets through multi-scale information fusion. Traditional detection heads often use multiple convolutional layers to generate bounding box and category predictions, which can be computationally expensive and limit training and inference efficiency. To address these challenges, the proposed detection head optimizes the processing of feature maps by utilizing lightweight shared convolutional layers alongside an independent BN detection head, improving both detection speed and accuracy.  
As shown in Figure 3, the network structure includes Conv_BN, a multi-layer convolutional module using \(1\times 1\) convolution kernels to adjust the number of input feature map channels. Conv_GN is the shared convolution layer, which applies \(3\times 3\) convolution kernels to extract local features. The BNAct module represents the independent batch normalization layer, ensuring that different feature maps undergo separate normalization processes. The detection head further incorporates techniques such as shared convolutional layers, separate batch normalization, and distributed focus loss to optimize both bounding box and category predictions. The entire model is designed to minimize computational resource usage while maintaining high detection performance.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 8 Experimental Results  
To automate hardware generation methods discussed in this work, we developed a compiler framework as depicted in Figure 10 that serves as a low-level intermediate representation (IR) to generate efficient hardware from a domain-specific language (DSL) for SDR. The framework leverages Scala's functional language constructs for embedding DSLs. The first step in compilation flow is a light-weight intermediate language that accepts the descriptions of applications that are translated into an SDF-AP model. We discuss further details of the DSL implementation in our future work. The compiler then proceeds by employing _scala-graph_ (i.e., graph library for Scala) [23] library to implement and validate the SDF-AP model. Before HDL generation starts, the SDF-AP model undergoes analysis and scheduling which are a key to validating the system and in determining the system properties; these properties include buffer size, latency and component compatibility from given throughput constraint. The HDL generator then generates the VHDL from the SDF-AP model using the _vMagic_ library [24]. vMagic used by our framework to read the VHDL code for existing IP cores, to stitch cores together in VHDL, and to write out the final top-level design in VHDL. Moreover, the optimizations are applied during code generation to enable efficient hardware design results. After code generation, the framework invokes the compilation functions of the _Xilinx ISE 14.7_ tool-chain. These compilation functions include synthesis, build, map, place & route and finally the binary file creation to target the Xilinx Spartan-6 xc6 sk1x50t FPGA device that we use in our testing.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 4 Analysis of Anisotropic Mechanical Characteristics of Shale
### Compressive Strength Anisotropy of Shale
According to the data in Table 2, the values of \(c\) and \(\varphi\) of shale samples with five bedding angles are shown in Table 4.  
Figure 8 shows the variation of the cohesion \(c\) and the internal friction angle \(\varphi\) of the shale samples with the bedding angle \(\theta\). It can be seen that the \(c\) and \(\varphi\) of samples decrease first and then increase with the increase in the bedding angle \(\theta\), and both obtain the minimum value when the bedding angle \(\theta\) is 60\({}^{\circ}\), which is the same as the variation of compressive strength \(\sigma_{\text{cs}}\) with the increase in bedding angle \(\theta\). The difference is that the maximum cohesion \(c\) obtained when \(\theta\) is 0\({}^{\circ}\), while the maximum internal friction angle \(\varphi\) is obtained when \(\theta\) is 90\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bedding** & \(A\) & \(B\) & \(R\)**-Square** &
\begin{tabular}{c} **Removed Abnormal Data** \\ **(\(\sigma_{3}\), \(\sigma_{\text{cs}}\))** [\(R\)-Square before** \\ **Removing Abnormal Data]** \\ \end{tabular} \\ \hline
0 & 2.559 & 175.273 & 0.988 & / \\
30 & 2.452 & 119.208 & 0.984 & / \\
45 & 2.148 & 98.667 & 1.000 & (10, 152.8) [0.816] \\
60 & 1.722 & 62.453 & 0.965 & / \\
90 & 4.282 & 114.427 & 0.954 & (10, 225.4) [0.763] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter values of Equation (1). Figure 7: Linear fitting curve of compressive strength \(\sigma_{\text{cs}}\) and confining pressure \(\sigma_{3}\) of shale.
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 5 Experiment Design and Discussion
### Evaluation Metrics  
Several famous evaluation metrics are used to validate the proposed system. P@K is widely used as a measure of prediction accuracy. It represents the proportion of relevant POIs among the top K recommendations provided to the tourist:  
\[\text{P@K}=\frac{1}{|U|}\sum_{u\in U}\frac{n_{rel,u}}{n_{u}} \tag{7}\]  
where \(U\) represents the set of users, \(n_{rel,u}\) represents the number of relevant POIs among top K recommendations for user u, and \(n_{u}\) represents the number of recommended POIs among top K for user u. The Average Precision@k is the sum of Precision@k where the POI at the \(k\)th rank is relevant (\(rel(k)\)) divided by the total number of relevant POIs (\(r\)) in the top K recommendations. MAP@K (or Mean Average Precision@K) calculates the average precision across multiple tourists, giving higher importance to relevant POIs that appear higher in the list:  
\[\text{MAP@K}=\frac{1}{|U|}\sum_{u\in U}\frac{1}{r}\sum_{k=1}^{K}Precision@k \cdot rel(k) \tag{8}\]  
\(rel(k)=1\) if item at \(k\)th rank is relevant; 0, otherwise. The mean reciprocal rank (MRR) is utilized to calculate the multiplicative inverse of the rank of the first relevant POI:  
\[\text{MRR@K}=\frac{1}{|U|}\sum_{u\in U}\frac{1}{rank_{u}} \tag{9}\]  
where \(rank_{u}\) represents the rank of its first relevant POI of the recommendation list for user u. Compared with measuring the importance of the first hit in the recommendation list using MRR, normalized discounted cumulative gain (NDCG) measures the quality of the entire ranking list. By adding a position decay factor in the process of calculating the cumulative gain, the NDCG score can be derived by the following:  
\[\text{NDCG@K}=\frac{\text{DCG}_{k}}{\text{IDCG}_{k}} \tag{10}\]  
\(\text{DCG}_{k}\) is the DCG for a rank position \(k\) and can be formulated as \(\text{DCG}_{k}=\sum_{i=1}^{k}rel_{i}/log_{2}(i+1)\) where \(rel_{i}\) shows the graded relevance of the result at position \(i\); \(\text{IDCG}_{k}\) denotes the maximum \(\text{DCG}_{k}\) in an ideal ranking and can be expressed as \(\text{IDCG}_{k}=\sum_{i=1}^{|REk|_{k}}\left(2^{rd_{i}}-1\right)/log_{2}(i+1)\) where \(REk_{k}\) represents the list of relevant POIs (ordered by their relevance) up to position \(k\).  
The real-world implications of these metrics are addressed as follows. Metrics P@K and MAP@K help tourists by ensuring that the recommendations they receive are relevant and appropriately ranked, reducing the time spent finding the best places to visit. MRR directly  
\begin{table}
\begin{tabular}{c c c c} \hline
**Seq\_id** & **Recommended POI Sequence** & **Score** & **RT (Hours)** \\ \hline \(Seq_{27378}\) & \(<\)\(cpcia_{2}\)\(\rightarrow  
impacts how soon a tourist sees a POI that they are truly excited about, making the system feel more responsive and helpful. NDCG ensures that the system provides relevant POIs and a sequence that maximizes the tourist's satisfaction, offering the most valuable options at the top of the list.
## 2 Related Work
### Neuron Computational Model  
The neuron is the essential computational unit in an ANN, which is modeled according to the concept introduced by [PERSON] and [PERSON] [26]. The computational model of a neuron comprises two essential elements: a summing function and an activation function. Data are conveyed to a neuron through \(n\) inputs. Initially, the neuron computes the weighted sum of these inputs, and subsequently, the neuron output is determined by this weighted sum and the activation function. A graphical representation of this neuron model can be observed in Figure 1.  
The mathematical model of a neuron is expressed in (1).  
\[y=\text{f}\left(\left(\sum_{j=1}^{n}w_{j}x_{j}\right)-b\right) \tag{1}\]  
Figure 1: A graphical representation of the mathematical model of a neuron.  
In (1), the summing function calculates the sum of each input, \(x_{j}\), multiplied by its corresponding weight, \(w_{j}\). The neuron's threshold (or bias), \(b\), can be regarded as a weight with a factor of \(-1\). The neuron will fire if the weighted sum of its inputs exceeds the threshold. The binary result is the output of the neuron's model and is denoted by \(y\). The activation function, f, serves as a squashing function for the weighted sum, constraining the allowable range of the output signal's amplitude. The activation functions utilized for generating continuous or discrete output values are as follows:  
\[\text{f}(v)=\frac{1}{1+e^{-\lambda v}} \tag{2}\]  
and  
\[\text{f}(v)=\left\{\begin{aligned} +1,& v>0\\ 0,& v<0\end{aligned}\right. \tag{3}\]  
In (2), \(\lambda\) (\(\lambda>0\)) is proportional to the neuron gain, and it plays a critical role in identifying the steepness of the continuous function \(f(v)\) when \(v\) is near 0. It is worth noting that as \(\lambda\) approaches infinity (\(\lambda\rightarrow\infty\)), the continuous function converges towards the function stated in (3).
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 2 Materials and Methods
### Model Development and Application
#### 2.2.5 Service Indicators' Normalization  
In the AHP, indicators are usually scaled between 0 and 1 to be comparable and integrable with their relative weights possible. To this purpose, service indicators can be scaled using different methodologies based on the purpose of the study. In this work, to calculate suitability, the model was run using two linear scaling approaches: the supply-based scaling, according to which 0 was associated to the minimum value of the indicator, and 1 to the maximum value of the indicator in the study area; the demand-based scaling, according to which 0 was associated to the minimum level of the indicator required by the demand and 1 to the values of the indicator able to meet the demand completely. The former method was targeted at expressing the relative quantity of service supply. In this case, each indicator is scaled between its minimum value and maximum value in the study area. So, the resulting indicator has only a relative value clearly expressing the differences within the area in terms of supply and is not comparable with other study areas. Differently, with the latter method, indicators were scaled based on demand-driven minimum and maximum to obtain indices potentially comparable with other areas.  
In the demand-based scaling approach, we considered that services demand is influenced by two main factors: population beliefs and preferences, which are variable across space and time [60], and biophysical characteristics of the landscape. Only this last variable was considered to identify minimum and maximum values of service demand. So, the potential service demand of each service needed to be assessed before performing the demand-based scaling. The demand was considered constant in some cases, while in others, it varied across space due to physical landscape features (such as slope, presence of specific land uses, etc.).  
Depending on the type of service indicators used in the study (presence-absence indicators or intensity indicators, further differentiated in two sub-types each), we identified a different method for defining the potential demand (Table 3, Table S1).