esa-sceva/satcom-chunk-collection huggingface dataset:
# Sentiment Analysis on Algerian Dialect with Transformers
## 4 Classification of Algerian YouTube Comments Using Transformers
### Model Performance  
In this section, we will share the final model metrics, including F1-score, accuracy, and the confusion matrix.  
The training was on the Arabic pre-trained BERT model [34]. After a lot of experiments that we will show in the experiment results section, we have come to have a reliable model that is able to correctly classify 81.74% of the total sentences in the test set (accuracy). In contrast, the F1-score reached 78.38% in the test set.  
Figure 1(a) shows the F1-score of the validation set during training, whereas Figure 1(b) shows the accuracy score.  
As shown in the F1-score and accuracy figures, after more than 5500 training steps, the F1-score obtained its highest value in the last step. For accuracy, the highest value was after 2000 training steps, and since the F1-score is our primary metric, we loaded the training weights after 5500 steps and then calculated the metrics for the testing set.  
Figure 3 shows the confusion matrix of the optimal model in the testing set.  
As you can see, out of all predicted positives, 84.78% were classified correctly; out of all predicted negatives, 81.88% were classified correctly; and out of all predicted neutrals, 72.98% were classified correctly.  
The model did very well in positive and negative classes, but it struggles in comparison in the neutral class, which is due to many reasons, including but not limited to the imbalance of the dataset. Only around 22% of the total samples are neutral, including controversial sentences that were mistakenly annotated as neutral by our annotators.  
Figure 2: F1-score and the accuracy of the validation set during training: (**a**) F1-score; (**b**) accuracy.
## 3 Literature Reviews
### Identity-Based Cryptography
#### 3.5.2 Identity-Based Digital Signature  
For the authentication of broadcast messages in the proposed protocol, we will use an online/offline identity-based digital signature which is suitable for low-cost wireless sensor networks. The online/offline identity-based digital signature performs the signature generation procedure in two phases. The first phase is performed offline (prior to the knowledge of the message to be signed) and the second phase is performed online (after knowing the message to be signed). The online phase is typically very fast, and hence can be executed efficiently by sensor nodes. One efficient online/offline identity-based signature solution for the proposed protocol can be found in [24]. The contributions of that protocol are:  
* It does not require any certificate attached to the signature for verification.
* It does not require any pairing operation in signature generation or in verification.
* It allows the offline information to be re-usable. The signer is not required to execute the offline algorithm every time it wants to sign a new message. Furthermore it does not require any secret information from the signer. Thus it saves a lot of communication bandwidth since the node does not need to return to the BS for the renewal of the offline information every time signing is performed.
## 3 Hypothesis and Functional Specification
### RAG Method  
The solution comprises three essential phases: indexing, retrieval, and response generation, which are utilized in the RAG method. The processing and structuring of documents are entailed by indexing to facilitate rapid and efficient access to information. At the same time, the identification of semantically relevant text that aligns closely with the user's query is focused on by retrieval. The responses are grounded in an indexed content pool, resulting in answers derived from carefully acquired contextual data. This process significantly enhances the reliability of responses, as the model does not merely generate text but is also referenced from pertinent information.  
The mitigation of hallucinations, a phenomenon characterized by the fabrication of inaccurate or misleading information by models, is identified as one of the primary objectives of the RAG method. The model is provided with relevant context, resulting in a reduction of the likelihood of hallucinations; however, several challenges are still faced by the approach. The challenges encountered include the risk of irrelevant literature being retrieved, which may dilute the quality of the response, the introduction of redundancy within the context, and sporadic hallucinations occurring in specific instances where the retrieved content is insufficiently aligned with the query.  
The challenges are counteracted by incorporating a robust query formatting mechanism within the architecture. The mechanism ensures that only the most pertinent context is employed during response generation, with redundant data being filtered out. Additionally, the architecture refines the retrieved documents through ranking based on relevance, which enhances the model's ability to generate accurate and concise responses. The role of query formation in shaping output is critical, as user queries are structured to align better with the content of the document repository.  
Moreover, it has been observed that the system leverages semantic search capabilities, allowing for the detection of nuanced relationships between terms and concepts, thereby making retrieval more aligned with user intentions. Accuracy is improved, and retrieval latency is reduced, which allows for a promper response from the system. Figure 3 illustrates the implementation of the RAG method, outlining the workflow for query formation and contextual response generation.  
The enhancements made to the system's architecture specifically aim to overcome the inherent challenges within the RAG framework. This design decision is a testament to the balance between delivering precise responses and maintaining an efficient retrieval process.
## 3 Materials and Methods
### Case Study
#### 3.4.3 Support Infrastructure Components  
As previously explained, the services offered by Support Infrastructure Components do not have any specific use on their own and cannot accomplish any business need. However, they provide infrastructure services to the application components that do achieve business goals. Therefore, the design of applications is dependent on the Support Infrastructure Components available and the services they offer. For this case study, five components have been considered:  
* MQTT broker: It runs the Fog-IoT Message Hub that acts as a link between the IoT and Fog layers. As its name suggests, it uses MQTT, a lightweight message protocol used in IoT applications, which is based on a publish-subscribe model.
* Kafka: The Intra-Fog Message Hub is implemented through Kafka. It is an event streaming platform based on a publish-subscribe model. It allows and enables the communication of the application components that run on the cluster.
* eXist-DB: A NoSQL database designed to store XML data in a native way.
* InfluxDB: A NoSQL database designed to store time series data.
* Grafana: A tool for monitoring and analyzing data from different databases. It allows visualizing the data processed and stored by the applications.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### Related SDR Networks  
WISCANet was inspired by several related SDR networks developed at various research institutions. These are briefly summarized below. Reconfigurable Ecosystem for Next-generation End-to-end Wireless (RENEW) and its companion software RENEWLab is a software suite that operates with the Faros and Iris hardware [57] to develop applications for experimentation on the RENEW platform [58, 59]. WARPLab is a framework designed for rapid Physical Layer (PHY) prototyping [60, 61]. It utilizes MATLAB and FPGA implementations of the framework and runs on its own reference hardware implementation rather than utilizing common off the shelf (COTS) SDR hardware. Other SDR networks have been proposed or implemented in [62, 63, 64, 65, 66]. In those works, customized or commercial SDR devices are used to suit the authors specific needs and research applications. In [67], the authors develop a system similar to WISCANet but instead use GNURadio as the intermediary between MATLAB and UHD. WISCANet provides a more flexible interface and a more efficient method of interfacing with USRPs using MATLAB, Python or other high level languages.
## 6 Specific Applications of Wastewater Bioprocessing with Simultaneous Biofuel Production
### Anaerobic Treatment Residues of Organic Waste  
As consumption of meat and animal products is expected to double by 2050 due to global population growth, the need to increase feed production will intensify, and this will lead to impacts on the food chain and greenhouse gas emissions. The management of organic food and feed production waste is a sector with ever-increasing demands. Anaerobic treatment is the dominant way of dealing with this issue, as it produces renewable energy and fertilizers while reducing emissions from waste storage. In particular, anaerobic treatment produces biomethane and a nutrient-rich residue, which is applied to agricultural crops, ensuring waste stabilization, reduction in greenhouse gas emissions, reduction in odor, and provision of nutrients necessary for crop growth. The residue is rich in macroscopic nutrients (N, P, K, S, Mg, Ca, Fe, and Na) and contains several trace elements (Co, Fe, Se, Mo, and Ni), either as a result of the original raw material used or due to appropriate additions for improved anaerobic treatment efficiency. It can be used in soils, untreated or after treatment, but it can also be subjected to separation of liquid and solid fractions. Filtration and dehumidification are among the most common treatment methods used depending on the needs of residue application and depending on the type of waste generated. However, the nitrogen content of the residue is quite enhanced and the form in which it is found is very easily absorbed by plants. This property creates a problem in areas rich in nitrogen, and therefore limits and hinders its application. Microalgae crops are one of the options for making more efficient use of anaerobic digestion residue to avoid burdening soils with nitrogen loads and producing protein feed with low greenhouse gas emissions. Microalgae need nutrients to grow, and can therefore derive them from the residue of anaerobic treatment. The biomass produced is high in protein and can be used as a source of feed or fish feed. This approach creates a circular economy solution for organic waste that will reduce the impact of agricultural crops by drastically reducing nutrient pollution, greenhouse gas emissions, and the requirement for land use change in order to boost feed production, thereby ensuring the demanded food security [51] (Figure 2).
## 4 Experimental Results and Discussion  
In this section, we provide comprehensive details related to the experiments, followed by the validation of our proposed method using relevant datasets and evaluation metrics. The experiments were conducted on Python 3.7, utilizing the Open3D and PCL libraries. The entire algorithm was executed on a computational workstation equipped with a 3 GHz Intel CPU, 256 GB of RAM, and an NVIDIA GeForce RTX 3090 GPU.
## 5 Applications in Physiology  
The physical explanation of the previously obtained results can predict the transport of various physiological fluids, such as blood, semen, mucus, etc., in smaller vessels of the body. In the male reproductive system, ductile differences are microsized tubules that connect the rete testis to the epididymis and are composed of columnar ciliated cells. These ciliated cells serve to transport human semen. The volume flow rate of human semen is estimated to be 0.006 mL/h in [PERSON] and [PERSON] [3]. This value was predicted by many scientists and was based on many experiments conducted for the flow rates of semen in various animals, such as the rete testis of rat, ram, and bull. However, [PERSON] and [PERSON] correlated these values with a mathematical model [3]. They utilized a viscous fluid model to estimate and compare their theoretical values with the experimental value of the flow rate in the human reproductive system. According to their calculations, the following values were used in the model.  
\[d=50\mu m,\ c=200\mu/\ \text{sec},\ \varepsilon=0.1,\ \delta=1.0,\ \text{and}\ \beta=0.1.\]  
According to their calculations, the approximate value of \(Q\) (i.e., the dimensionless volume flow rate) was 0.022 and the approximate value of \(Q^{*}\) (i.e., the dimensional volume flow rate) was 0.00012 mL/h. For the present model, we display the numerical values of \(Q\) and its dimensional counterpart \(Q^{*}=(\pi d^{2}c\times Q)\) for various values of the key parameters in Table 1. These numerical results demonstrate the fact that the viscoelastic parameter and the Hartmann number had reducing effects on the volume flow rate, whereas the Darcy number had an opposite effect on the flow rate. The effect of the Grashof number was to accelerate the flow rate, while the chemical reaction parameter decelerated it. Indeed, these observations are consistent with many other studies on peristaltic and cilia transport mechanisms [23; 30; 31]. Here, we point out that the results obtained in References [3; 20] for a Newtonian fluid could be recovered from our model by setting \(\lambda_{1},H_{a},S_{t},g_{t},S_{c},g_{c}\) and \(M\) equal to zero and \(D_{a}\rightarrow\infty\). However, the difference between these values and the experimentally estimated values of the volume flow rate of human semen (i.e., 0.006 mL/h) was not minor. This indicates a need for further improvements, both theoretically and experimentally. The drawbacks and limitations of the present model can be evaluated by considering other main physiological factors involving in these mechanisms. We hope that the present results are reasonably utilized in investigating thephysiological flows inside the human vessels and in producing the magnetically actuated artificial cilia in lab-on-chip devices.
# Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
###### Abstract  
Road traffic collisions are among the world's critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.  
road collision traffic; data imbalance; machine learning; driving simulation Article
## 3 MALDI Non-Imaging MS Analysis of GSLs
### The Influence of Matrices Used in MALDI Non-Imaging MS Analysis of GSLs  
MALDI is a soft ionization technique that involves a laser striking crystals containing a small amount of analyte dispersed in a large amount of a matrix of small molecules in a substantial quantity of a small molecules matrix in order to obtain analyte molecules in the gas phase without fragmenting or decomposing them. In mass spectrometry (MS), the matrix plays several pivotal roles. It serves to both dilute and ionize the sample, as well as to absorb the laser energy. Additionally, the matrix helps prevent the aggregation of analyte molecules and the formation of clusters [114,115]. Both the sample and matrix are vaporized from the target's surface, triggering a chemical reaction. In the positive ion mode, this reaction leads to the attachment of a proton or alkali metal atom to the sample molecules, whereas in the negative ion mode, \([\text{M}-\text{H}]^{-}\) ionic species are typically generated. The matrices are organic compounds with low molecular weights, exhibiting low vapor pressure and volatility. They often possess an acidic nature, facilitating the excitation of photons and subsequent ionization of the analyte. Although a few basic matrices are available, the majority are acidic. The matrices also possess robust energy absorption capabilities within the UV and IR spectra. As a result, they can harness energy from the laser source, becoming excited and releasing photons for the ionization process. In recent decades, there have been many matrices used in the MS process (Figure 2), each having their advantages and drawbacks (Table 3). Figure 1: Nomenclature of fragmentation of GSL ions in MS/MS spectra. (**a**) Fragmentation scheme of the GSL oligosaccharidesacharide chain: designation of the ions obtained from fragmentation of the ether bonds between the monosaccharides (Y, Z, B, C), and ions obtained from breaking the inner bonds of the saccharide ring (ring fragmentation, A, X) [110]. (**b**) Designation of ceramide moiety fragment ions according to the nomenclature of Ann and Adams [111]. S, T, U, and V ions, marked in purple, contain the N\(-\)acyl chain; O and P ions containing the sphingoid base are marked in green; G and H ions, which result from the fragmentation of the sphingoid base, are marked in blue; and the E ion, which results after fragmentation of both the N\(-\)acyl chain and the sphingoid base, is yellow.
# Color Recognition of Transparent Plastic Based on Multi-Wavelength Transmission Spectrum
## 2 Materials and Methods  
### Experimental System  
As shown in Figure 1, the experimental system is composed of LED transmission light source with 4 wavelengths (381 nm, 439 nm, 620 nm and white), transmission spectrum acquisition camera (DFK23 GPO31, The Imaging Source Inc., Bremen, Germany), micro-controller control unit (MCU) and data processing unit (Computer). The 381 nm, 439 nm, 620 nm and white wavelengths of the LED transmission light source were selected from the existing wavelengths of 381 nm, 439 nm, 495 nm, 520 nm, 620 nm and white by the experiment based on the comprehensive consideration of recognition effect and data size. The LEDs of the 4 wavelengths successively irradiate the transparent plastic on one side, and the corresponding transmission spectrum is obtained by the camera that is located on the other side of the plastic. The obtained spectra are then processed by a computer for color recognition.  
Figure 1: Schematic diagram of color recognition system of transparent plastic based on multi-wavelength transmission spectrum.
## 1 Introduction  
Automation in oyster sorting and packaging is crucial in the seafood industry due to inefficiencies and hygiene risks associated with manual labor. Conventional methods are time-consuming, error-prone, and increase contamination risks, especially for raw oyster consumption [1, 2]. To address these challenges, combining robotic systems with computer vision has emerged as an innovative solution to enable robots to mimic human movements and accurately recognize and handle objects [3, 4, 5].  
Convolutional Neural Networks (CNNs) have significantly advanced object detection and robotic grasping tasks, enhancing image recognition, segmentation, and object detection capabilities in complex automation scenarios [6, 7, 8, 9]. Recently, CNN-based models have increasingly been applied in food safety, such as Mask R-CNN for grading South Rock Lobsters [10] and Faster R-CNN for detecting fish bones in Atlantic salmon [11]. In agriculture, YOLOv5 combined with tracking algorithms tracked and detected apples [12], while R-CNN-based deep neural networks identified and classified fruits [13]. However,challenges remain in real-world applications due to the complex nature of agricultural products, occlusion, lighting variations, and background noise [14].  
In the food processing industry, robotic arms are increasingly being deployed to address inefficiencies and contamination risks [15; 16; 17]. Some studies have incorporated angle information into bounding box regression to improve grasping accuracy and detection speed [18; 19; 20; 21]. For dynamic objects, CNNs combined with You Only Look At Coefficients (YOLACT) and Long Short-Term Memory (LSTM) networks have achieved high grasp accuracy [22]. However, research on the robotic autonomous inspection and sorting of aquatic products such as oysters is still limited, facing challenges such as conveyor-based dynamic sorting, light variations, and motion blur. In dynamic grasping scenarios, trajectory prediction accuracy is critical. While simple methods like Moving Average can reduce noise, advanced filters such as KF combined with Low-Pass filters are necessary to balance real-time performance and stability.  
This study introduces an advanced robotic system to automate oyster sorting on a conveyor belt, integrating machine learning and robot control techniques. The system uses deep learning for real-time oyster recognition and KF for motion prediction. Key contributions include the following: (a) Development of a Vision System: replacing the backbone network structure with the YOLOv8-OBB model achieves a lightweight effect optimized for dynamic real-world environments where lighting, motion blur, and occlusion pose significant challenges. (b) Utilization of KF and a Low-Pass filter: KF predicts the position of oysters on a conveyor belt, improving grasping accuracy under static and dynamic conditions. Mean smoothing was applied to reduce measurement noise, and a Low-Pass filter was employed to suppress high-frequency disturbances. The hysteresis effect caused by the Low-Pass filter cutoff frequency ensures stable tracking and accurate gripping.