esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
###### Abstract  
Hate speech is a complex topic that transcends language, culture, and even social spheres. Recently, the spread of hate speech on social media sites like Facebook has added a new layer of complexity to the issue of online safety and content moderation. This study seeks to minimize this problem by developing an Arabic script-based tool for automatically detecting hate speech in Roman Urdu, an informal script used most commonly for South Asian digital communications. Roman Urdu is relatively complex as there are no standardized spellings, leading to syntactic variations, which increases the difficulty of hate speech detection. To tackle this problem, we adopt a holistic strategy using a combination of six machine learning (ML) and four Deep Learning (DL) models, a dataset from Facebook comments, which was preprocessed (tokenization, stopwords removal, etc.), and text vectorization (TF-IDF, word embeddings). The ML algorithms used in this study are LR, SVM, RF, NB, KNN, and GBM. We also use deep learning architectures like CNN, RNN, LSTM, and GRU to increase the accuracy of the classification further. It is proven by the experimental results that deep learning models outperform the traditional ML approaches by a significant margin, with CNN and LSTM achieving accuracies of 95.1% and 96.2%, respectively. As far as we are aware, this is the first work that investigates QLoRA for fine-tuning large models for the task of offensive language detection in Roman Urdu.  
deep learning; machine learning; support vector machine +
Footnote †: journal: _Algorithms_
## 4 Implementation
### Monitoring Model
#### 4.3.2 Evaluation  
In practice when the length of the path-message \(|P|\) exceeds the limit of \(c|A|\), the path-message gets into the evaluation state, so it stops being transmitted from agent to agent and a metric called detection ratio is calculated, in order to detect possible attempts for DDoS attacks. In case of a DDoS attack being executed and given the fact that a high percentage of agents the path-message has passed through (\(a\in P\)) monitor infected devices \(b\in B\), the attack should be detectable. It is expected that the \(T_{out}^{i}(h_{v})\) metric, the total packet rate towards the victim of the attack \(h_{v}\), should be significantly higher than the similar metric calculated for any other hosts.  
The host receiving the most packets in the path-message is denoted as \(h_{m}\). Specifically \(h_{m}\) is the host for which:  
\[T_{out}(h_{m})>T_{out}(h_{i}),\forall i\
eq m. \tag{8}\]  
The main metric (detection ratio) used to detect committed DDoS attacks is the ratio of \(T_{out}(h_{m})\) to the corresponding value for the next busiest host of the path-message.  
\[detection\_ratio(h_{m})=\frac{T_{out}(h_{m})}{\max_{\forall i\
eq m}(T_{out}(h_{ i}))} \tag{9}\]If \(detection\_ratio(h_{m})>2\) then \(h_{m}\) is a candidate for being a DDoS attack victim.
## 3 Methodology  
The research methodology is based on a number of necessary steps including literature review, feature selection, data collection, agent definition, and evaluation; refer to Figure 6.  
Figure 5: Key findings from the literature review of concepts, theories, and existing frameworks [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 23; 29; 33; 43; 44; 45; 46; 47; 48; 49; 50; 51].  
The research process performed an orderly and critical review of existing studies. The study of past research papers about the latest trends of cloud computing acted as a data collection tool to gain knowledge about the existing frameworks and results achieved. Based on the literature review understanding, the system features were identified and later classified into different sets of classes. The core feature selection greatly helped in meaningful decision-making. The newly conceptualized techniques were formed/extended to address the untouched issues of cloud computing. The eco-friendly dynamic resource discovery and resource allocation models were identified to meet the computational demands of the cloud jobs.  
It became evident from the selected feature that existing techniques to achieve better resource allocation and reduce the power consumption of the computing nodes in a cloud environment have some serious deficiencies. Most of these techniques monitor the resource load over a period of time and decide to turn off the computing node to reduce the power consumption. Moreover, the main scheduler and resource handler run on the master node, which defines the critical bottleneck entry and a single point of failure (SPF) in the computing environment. Mostly the defined systems only consider the CPU allocation while ignoring the other parameters. The latency due to changes in the state of machines (on/off) is also not considered.  
Our research model is based on the microservice architecture in which a cloud application is divided into several small services that will be capable enough to perform the signal functionality. A prototype of a microservices-based system is shown in Figure 7, where multiple actors access the database server and web server through API gateway, frontend interfaces, and storage gateways. In between, multiple independently running microservices provide the required service while being able to handle millions of access requests and ensuring low latency, high availability, scalability, and resilience to network failures. To handle a large number of microservices in the system, we proposed an agent-based service (see Figure 8) which will be responsible for providing four core services including intelligent partitioning, dynamic allocation, resource optimization, and mutation actions.  
Figure 6: Research methodology flow.  
Figure 7: A microservice system prototype.
# Bottlenose Dolphins' Clicks Comply with Three Laws of Efficient Communication
###### Abstract  
Bottlenose dolphins' broadband click vocalisations are well-studied in the literature concerning their echolocation function. Their potential use for communication among conspecifics has long been speculated but has yet to be conclusively established. In this study, we first categorised dolphins' click production based on their amplitude contour and then analysed the distribution of individual clicks and click sequences against their duration and length. The results show that the repertoire and composition of clicks and click sequences adhere to the three essential linguistic laws of efficient communication: [PERSON]'s rank-frequency law, the law of brevity, and the Menzerath-Altmann law. Conforming to the rank-frequency law suggests that clicks may form a linguistic code subject to selective pressures for unification, on the one hand, and diversification, on the other. Conforming to the other two laws also implies that dolphins use clicks according to the compression criterion or minimisation of code length without losing information. Such conformity of dolphin clicks might indicate that these linguistic laws are more general, which produces an exciting research perspective on animal communication.  
linguistic laws; animal communication; [PERSON]'s rank-frequency law; [PERSON]'s law of abbreviation; Menzerath-Altmann's law; _Tursiops truncatus_ Article
## 2 Materials and Methods
### Finite Element Analysis
#### 2.1.2 Boundary Conditions  
The global coordinate system was oriented so that the \(Z\)-axis is parallel to the bone axis. As mentioned above, we assumed that the bone grows predominantly in the direction of the bone axis (i.e., oppositional growth) and, therefore, the alignment of the axes allows simple loading of the bone segments in the epiphyseal plate in the \(Z\) direction. We assumed that for the growth to occur, the loading force Fz needs to match the weight of the person. As the bone growth happens in the early stages of life (childhood, adolescence), we have chosen our bone to come from an adolescent standing on one leg with a chosen 100 kg body weight equivalent to Fz = 980.7 N. In [24], the growing force was determined to be approximately 500 N, i.e., our force Fz was overestimated to err on the side of safety. The force boundary condition is illustrated in Figure 10.  
The distal end of the cut tibia is fully fixed (i.e., prescribed displacements are ux = uy = uz = 0); in the proximal part; there is a partial fixation (i.e., prescribed displacements ux = uy = 0) allowing for a movement in the \(Z\) direction. Deformation boundary conditions are shown in Figure 11.  
Apart from the force and deformation boundary conditions, frictional contacts between the staples and the bone must be considered. The Coulomb friction coefficient between stainless steel and bone ranges from approximately 0.25 to 0.7, according to [25]. The friction coefficient is highly dependent, among other things, on the surface quality of both bone and steel, hardness of bone, etc. In this analysis, the friction coefficient was set to 0.2 (estimated by an educated guess), giving the possibility for staples to migrate out of bone. The friction coefficient used in this study is lower than in [25], taking into account the body fluids and tissues reducing the friction. Nevertheless, even with the low friction coefficient used in our study, the displacement in contact areas was very small, so friction does not have a major effect on stress distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Part** & \
\begin{tabular}{c} **Number of FE** \\ **Elements** \\ \end{tabular} &
\begin{tabular}{c} **Number of FE** \\ **Nodes** \\ \end{tabular} \\ \hline \multirow{2}{*}{Tibia} & Epiplysis & 66,083 & 102,706 \\ \cline{2-4}  & Metaphysis-Diaplysis & 80,183 & 126,033 \\ \hline \multirow{2}{*}{Staple} & Medial & 132,292 & 438,008 \\ \cline{2-4}  & Lateral & 132,581 & 442,394 \\ \hline \multicolumn{2}{c}{Total} & 411,139 & 1,109,141 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of FE elements and nodes.  
Figure 9: Refined FE mesh: (**a**) Detail of the mesh in/around the hole for the staple; (**b**) Mesh of the staple; (**c**) Element size on staple (mm).
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Data Acquisition and Communication  
Good quality data is a fundamental principle to performing mature, useful, and reliable mechanisms for alarming, detecting, and predicting anomalies and failures. For that reason, the process of acquiring data from the very beginning, from the sensors layer, should be carefully designed.
## 2 Materials and Methods
### Extraction Methods  
#### 2.2.1 Microwave-Assisted Extraction (MAE)  
The microwave-assisted extraction was performed in a MAS-II Plus microwave synthesis/extraction reaction workstation (Sineo Microwave Chemistry Technology Co. Ltd., Shanghai, China). Approximately 1 g of _C. vulgaris_ powder was loaded in a double-wall vessel along with an appropriate amount of aq. ethanol, 90% \(v/v\). The choice of solvents and the ethanol/water ratio resulted from a preliminary study as well as the findings of [PERSON] et al. [24], which exhibited the solvent's advantage over other ratios and organic solvents regarding the extraction of _C. vulgaris_' biocomponents. The mixture was stirred at 500 rpm, and the extraction conditions of temperature, duration, solvent-to-biomass ratio, and microwave power were regulated according to an appropriate experimental design (see Paragraph 2.4). Solvent losses were minimized by adjusting a condenser on the top of the extraction vessel. After the MAE, the mixture was centrifuged at 1110\(\times\)\(g\) for 8 min in a Hermle centrifuge Z206-A (Hermle AG, Baden-Wurttemberg, Germany). The supernatant of the centrifuged mixture was filtered through a ChromPure PTFE/L 0.45 \(\upmu\)m filter and evaporated under vacuum at 100 mbar and 45 \({}^{\circ}\)C in a Hei-Vap Advantage ML rotary evaporator (Heidolph Instruments GmbH & Co, KG, Bayern, Germany). Finally, all the dry extracts were temporarily maintained at \(-\)18 \({}^{\circ}\)C until further analysis. The extraction yield was determined gravimetrically by the received extracts' weight, and the experimental error was determined from the triple repetition of the central point of the experimental design.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Copulas  
We chose the extreme value (EV), generalized extreme value (GEV), exponential (EXP), gamma (GAM), Poisson (POISS), normal (NOR), generalized Pareto (GP) and Weibull (WBL) distribution functions to fit the monthly GPP series (Table S1). The SPEI series is a statistic distributed normally and accordingly, the normal distribution is employed as its marginal. The probability distribution functions of monthly GPP and SPEI series are defined as \(F_{GPP}\) (\(gpp\)) and \(F_{SPEI}\) (\(spe\)), respectively.  
We constructed a joint distribution function in each pixel of the globe in every month based on monthly GPP and SPEI series. Considering \(F_{GPP}\) (\(gpp\)) and \(F_{SPEI}\) (\(spe\)), these two marginal distribution's joint distribution function can be defined as a copula C:  
\[F_{GPP,SPEI}(gpp,spei)=C(F_{GPP}(gpp),F_{SPEI}(spei)). \tag{10}\]  
In this study, we used the methods of maximum likelihood and inference functions for margins to estimate the fitted parameters in \(F_{GPP}\) (\(gpp\)), \(F_{SPEI}\) (\(spe\)) and \(F_{GPP,SPEI}\) (\(gpp\), \(spei\)) [40]. The bivariate distribution functions used commonly, including the Frank,Clayton, Gumbel, t and Normal copulas, were constructed to couple FGPP (gpp)) and FSPEI (spei) in each pixel of the globe (as shown in Table S2).  
Next, we obtained the optimal joint distribution function of \(F_{GPP}\) (\(gpp\)) and \(F_{SPEI}\) (\(spei\)) as follows by referring to the method of the goodness-of-fit test [PERSON] et al. (2019) provided.  
\[F_{GPP,SPEI}(gpp,spei)=P(GPP\leq gpp,SPEI\leq spei)=\int_{-\infty}^{gpp}\int_{- \infty}^{spei}f(gpp,spei)d_{u}d_{v}. \tag{11}\]  
Ultimately, we generated a set of conditional probabilities of vegetation productivity losses for diverse drought conditions with a bivariate statistical framework. Larger values of vegetation productivity loss probabilities under the same scenario imply greater ecosystem vulnerability, and such ecosystems are categorized thereby as the drought-prone class. Given multiple drought scenarios, conditional probabilities of vegetation productivity lower than different percentiles (e.g., the 10 th, 20 th, 30 th, 40 th percentiles were considered in the study) are derived using copula-based joint distribution and the conditional distribution formulas. The conditional probabilities of different percentiles of vegetation productivity loss under moderate, severe and extreme drought scenarios can be calculated by the following formula.  
\[P(GPP<gpp|~{}-1.5<SPEI\leq-1)=\frac{F_{GPP,~{}SPEI}(gpp,-1)-F_{GPP,~{}SPEI}( gpp,~{}-1.5)}{F_{SPEI}(-1)-F_{SPEI}(-1.5)} \tag{12}\]  
\[P(GPP<gpp|-2<SPEI\leq-1.5)=\frac{F_{GPP,~{}SPEI}(gpp,-1.5)-F_{GPP,~{}SPEI}( gpp,~{}-2)}{F_{SPEI}(-1.5)-F_{SPEI}(-2)} \tag{13}\]  
\[P(GPP<gpp|~{}SPEI\leq-2)=\frac{F_{GPP,~{}SPEI}(gpp,-2)}{F_{SPEI}(-2)}. \tag{14}\]
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
## 3 Discussion  
The results are discussed in the sections below based on the different hardware types. General assumptions were that space-based detectors have 15,000 dark counts per second while ground-based detectors have 2500 dark counts per second. This was based on pessimistic cases for single photon detectors before and after radiation damage [10]. Furthermore, ground-based detectors were assumed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Photon Source** & **QKD Protocol** & **PKD Encoding (Example)** \\ \hline Weak coherent pulse (WCP) & Decoy state BB84 & Pulse position modulation (PPM) \\ \hline Spontaneous parametric & BBM92 & Heralded Left or \\ down conversion (SPDC) pairs & (entanglement-based BB84) & Right-handed polarized photons \\ \hline Ideal single photon source (SPS) & Single photon BB84 & Left or Right-handed polarized photons \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantum key distribution (QKD) and photon key distribution (PKD) methods modelled for different hardware implementations.  
Figure 1: Modelling results for schemes described in Table 1. Assumptions are discussed in Section 3. (a) Key generation rate for quantum key distribution (QKD) protocols: decoy state BB84, BBM92, and BB84 using an ideal single photon source (SPS); (b) Key generation rate for photon key distribution (PKD) schemes: pulse-position modulated (PPM) photon key distribution (PKD), PKD with a heralded photon source, and PKD using an ideal SPS; (c) Quantum bit error rate (QBER) for QKD protocols; (d) QBER for simplified PKD schemes.  
to have an additional 1000 background counts per second due to scattered light entering the receiver. This was slightly more conservative than noise counts estimated in recent literature [11]. All sources were assumed to have perfect visibility, e.g., for a SPDC source this means it was assumed to produce perfectly entangled states.
# Set-Point Control of a Spatially Distributed Buck Converter
## 3 Open-Loop Simulation
### Transient Motion under Pulse-Width Modulation  
Now, we excite the converters with a PWM signal. We use \(E=12\) V and a duty ratio \(d=0.5\), which should result in an average output voltage of \(E\,d=6\) V. For the PWM switching period \(T\) we used integer multiples of the delay time \(\tau l\approx 30\) ns. The simulation results are shown in Figure 6. The output voltage \(v(t)\) of the classical converter oscillates around \(6\) V. For smaller PWM switching times (i.e., higher switching frequencies) the voltage ripples become smaller. Among the simulation scenarios, the best result is obtained with \(T=\tau l\).  
Figure 5: Transient voltages for constant input voltage \(E\) with \(d\equiv 1\): wave propagation of the voltage in the transmission line w.r.t. time and space (**left**), output voltage \(v(l,t)\) on the right boundary of the distributed converter and output voltage \(v(t)\) of the classical converter (**right**).  
Figure 6: Transient voltages for PWM duty ratio \(d=0.5\) and different switching periods \(T\): output voltage \(v(t)\) of the classical converter (green), output voltage \(v(l,t)\) on the right boundary of the distributed converter (blue), PWM input voltage (red).  
The situation is different in case of the distributed converter. For \(T=\tau l\) and \(T=2\,\tau l\), the output voltage sweeps the full range from \(0\,\mathrm{V}\) to \(12\,\mathrm{V}\). For a switching period with a greater multiple of the delay time \(\tau l\), we obtain smaller voltage ripples. The smallest voltage ripples occur with \(T=4\,\tau l\), where the travelling waves partially compensate each other. This is consistent with the findings reported in [11]. In contrast to the classical converter, the voltage ripples cannot be arbitrarily reduced.
## 4 Predicting the Porosity in Selective Laser Melting Parts
### Predicting the Porosity Using RCNN and BA-RCNN
#### 4.2.2 RCNN and BA-RCNN Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original RCNN** & **Hybrid BA-RCNN** \\ \hline Average Percent of Porosity & 0.0229 & 0.0230 \\ Error in Training Set & & \\ \hline Average Percent of Porosity & 0.0231 & 0.0214 \\ Error in Validation Set & & \\ \hline Average Percent of Porosity & 0.0236 & 0.0234 \\ Error in Testing Set & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average percent of porosity error (final version). The minimum percent of porosity error value of 0.0214 comes with the validation set in BA-RCNN, and the following Table 10 shows the optimal CNN parameters values optimized using BA. The minimum error value of 0.0214 comes with a section depth of 1, 0.0101 initial learning rate, a momentum parameter of 0.9568, and a regularization value of 0.0097, and is less than the error value of 0.3167 for the image binarization method. The following Table 11 shows prediction accuracy for training, validation, and testing sets with a difference less than a threshold of 0.02 (percent of observations with error less than 0.02), along with computational time for best iteration for all algorithms. The existing original RCNN produced 75.50% prediction accuracy in the testing set, which is approximately 7% better than the accuracy resulting from the image binarization method with a value of 68.60%. The novel hybrid BA-RCNN produced better prediction accuracy in all training, validation, and testing sets, which means it has better generalization capability than RCNN. The accuracy in the testing set has a value of 85.33% which is approximately 17% better than the existing image binarization method, and the hybrid BA-RCNN is cost-effective since the computational time is reduced by approximately 2 min. The following Table 12 shows the prediction accuracy for all three methods. The contribution of the paper is developing a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert a single slice to a binary image, the model achieved better prediction accuracy than the existing image binarization method by 17%. In order to test the algorithm, as the training of the RCNN would require a large amount of experimental data, artificial porosity images mimicking real CT scan slices of the finished SLM part were created with a similarity index of 0.9976 with real images.
## 7 Processing Architecture
### CHP2 Physical Layer
#### 7.1.1 Transmit Engine  
The transmit engine (Figure 15) is a large IQ data buffer collaborating with a timer-controlled finite state machine. This engine is driven by the 40 MHz OCXO on the TRS board. The state machine periodically designates a transmit timestamp for the CHP2 processes. The data buffer is populated with raw IQ data through the AXI bus. Once the transmission time is reached, the buffer content is transferred to the AD9361 control core. The IQ data is loaded into local memory using the AXI DMAC, which resolves compatibility issues with existing Analog Devices drivers.