esa-sceva/satcom-chunk-collection huggingface dataset:
# Fronthaul Design for Wireless Networks
## 2 Background on C-RAN and Fronthaul Communication Technologies and Related Work
### Related Work
Nevertheless, it could be concluded from their work that the \"vertical FSO\" solution was, again, more costly than the other options; on the other hand, their work showed that the cost-effectiveness of the MRT and FSO solutions depended, among other things, on the tower separation (namely, they compared hops of 3 km vs. 5 km ones), thus suggesting that a thorough study of this costs dependence on the hop distance, notably considering different equipment and communication technologies, would be of great value. In light of the above, this paper advances the state-of-the-art by providing a quantitative cost-effectiveness comparison of three communication technologies, namely MRT, FSO, and FO, for individual fronthaul segments under different weather conditions, link lengths, and bit rate requirements. Furthermore, an assessment is also provided regarding the impact of low and high RRH densities on the selection of cost-effective communication technologies for C-RANs. In addition to the studies presented and discussed herein, this paper provides a generic and versatile methodology to determine the most cost-effective solution for each RRH-BBU link, as well as to compute the required number of BBUs and where they should be positioned in order to minimize the overall network costs.
## 3 Results  
### The Dynamic of SIF, GPP, and Meteorological Factors during the Growing Season  
The growth of cork oak plantations starts from March and basically stops in mid-November. The canopy closes at the end of May and stays stable from June to September. We conducted our research during this stable time period. During the growing season, SIF showed conspicuous diurnal and day to day variations (Figure 1). The diurnal pattern of SIF changes with meteorological factors mostly due to the incoming radiation under different weather conditions (Figures 1 and 2). However, the overall trend was relatively stable across the whole season. The trend of SIF was relatively consistent with PAR, which is the dominant driving factor of seasonal changes. At the early stage of the growing season, compared with GPP, SIF fluctuated more. GPP was relatively stable at the early stage of the growing season, and varied drastically in the later stage (Figure 1). During the growing season, the daily mean Ta, PAR, VPD, and soil moisture (SM) varied from 15.62-32.2 \({}^{\circ}\)C, 19.2-641.16 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), 0-2.209 KPa, and 11.71-27.84%, respectively. The maximum 30 min mean values were 40.22 \({}^{\circ}\)C, 1883 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), 4.13 KPa, and 39.08%, respectively, while the minimum SM was 11.59%. Comparing the meteorological factors in the growing season, VPD and PAR had a relatively consistent trend. Meteorological factors all showed obvious daily fluctuations in the growing season except for SM, which showed a small diurnal variation (Figure 1). SM only increased significantly after rain and then gradually decreased with time. SM varied more by season than by day. NDVI fluctuated greatly in the early growth phase, stayed relatively stable in the middle phase, and slowly decreased in the later phase. Some of its changes were consistent with SM, and were due to meteorological fluctuations caused by rain. Compared to NDVI, SIF fluctuated more drastically during the growing season and can better track the daily changes in productivity.  
In order to observe the diurnal characteristics of SIF, diurnal patterns were plotted for 3 days that were under different weather conditions (Figure 2). For these 3 consecutive days, deviation caused by canopy structure and leaf chlorophyll content were negligible. However, SIF presented an obvious diurnal pattern, increasing steadily in the morning and decreasing in the afternoon on sunny days, reaching a maximum at approximately 12:30, along with incoming radiation that reached its maximum around noon, which means that PAR may be the main driving factor of SIF change. The rate of change in the afternoon was greater than that in the morning. SIF on cloudy and rainy days exhibited gradual variation of PAR at the diurnal timescale and showed a hysteretic phenomenon with PAR, which was not found on sunny days. SIF on the cloudy and rainy day reached its maximum at approximately 12:00 and 16:00, respectively, consistent with changes in PAR. The time interval of each measurement was different because light intensity affected the integration time.
## 3 Results and Discussion  
### Experimental  
#### 3.1.1 Characterisation of the Extruded ABS Filaments  
As the first step, ABS pellets were extruded into printable filaments at 1.75 \(\pm\) 0.07 mm in diameter. To obtain a consistent filament with diameters very close to 1.75 mm, parameters such as the extrusion heat zone temperatures, winder speeds, cooling systems, and polymer purging speeds were adjusted to an optimum level as stated in Table 1. Figure 5a,b show a comparison between the measured diameter of the as-received ABS filament with the extruded filament.  
From the TGA results (Figure 6a), the ABS degradation onset temperature was recorded to be around 387 \({}^{\circ}\)C. Given that the temperature range required for 3D printing is well below this value (between 220 \({}^{\circ}\)C and 280 \({}^{\circ}\)C), therefore, no significant changes/degradations in the produced polymers would occur during the FDM printing process. From the DSC results (Figure 6b), the glass-transition temperature \(T_{g}\) related to the polymer samples without additives is in the range of 100 \({}^{\circ}\)C-102 \({}^{\circ}\)C which indicates that the pre-selected 240 \({}^{\circ}\)C printing temperature is well above the glass-transition temperature and can guarantee a good flow of polymer while extruding and printing.  
To investigate the material properties of the FDM-printed samples when the FDM key process parameters including the layer thickness, raster angle, infill percentage, and nozzle temperature are varied, mechanical tests were conducted in this study and the experimental results are presented in the following four Sections 3.1.2-3.1.5.  
Figure 5: SEM image of the cross-section of **(a)** extruded ABS, **(b)** as-received ABS filaments.  
Figure 6: (**a**) TGA and (**b**) DSC results of the ABS polymer.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 3 Proposed Framework for Enhancing Arabic Spam Detection
### Applying Machine Learning for Spam Detection
#### 3.3.3 Utilizing Machine Learning Techniques  
To detect spam content, a variety of machine learning classifiers have been used. Three classifiers are trained based on the extracted features to evaluate the effectiveness of these features in identifying spam content. Naive Bayes, Logistic Regression, and LinearSVC are the used classifiers. These classifiers were selected because they are widely used as a baseline in the previous works for Arabic spam detection. The outcomes of the experiment are discussed in Section 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Feature Name** & **Description** \\ \hline \multirow{6}{*}{Content Features} & \multirow{6}{*}{TF-IDF} & Multiplication of TF and IDF scores \\  & & whereas TF is a scoring of the frequency of the word in the current document \\  & & and IDF is a scoring of how rare the word is across documents. \\  & & _TF = (Frequency of a word in the document) / (Total words in the document)_ \\  & & _IDF = Log ((Total number of docs) / (Number of docs containing the word))_. \\  & Spam words & Number of spam words in the tweet \\  & Hashtag counts & Number of hashtags in the tweet \\  & URLs counts & Number of URLs in the tweet \\ \hline \multirow{6}{*}{Interaction features} & \multirow{6}{*}{Retweet counts} & Number of retweets for the tweet \\  & Mention counts & Number of mentions in the tweet \\ \cline{1-1}  & Duplication counts & The count of tweet text duplication \\ \cline{1-1}  & Favorite count & Number of favorites for the tweet \\ \hline \multirow{3}{*}{Account features} & Followers count & Number of followers of this Twitter user \\ \cline{1-1}  & Friends count & Number of _followings/friends_ of this Twitter user \\ \cline{1-1}  & Description length & Number of characters in the user description \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spam Detection features.
## 3 Obstacles in Oral Microbiome Studies
### Contamination Concerns  
Contamination is a significant concern in oral microbiome research, as it can introduce biases and confound results. There is a risk of contamination from both the tumor surface and laboratory kits used during sample collection and processing. Contaminants can skew the microbial profiles, leading to erroneous conclusions about the microbial communities associated with OPMD and OSCC [165; 166]. Contamination from the tumor surface occurs when microorganisms present on the surface of the lesion are inadvertently included in the sample. This can happen during biopsy or swab collection, especially if the sampling method is not sufficiently stringent. Surface contaminants may not accurately reflect the microbial communities within the tissue, thus leading to misleading data about the microbiome's role in disease progression [166; 167]. To mitigate this, researchers must employ careful and consistent sampling techniques, ensuring that samples are taken from the deeper layers of the tissue, where the true microbial communities reside. Laboratory kit contamination is another major source of bias. DNA extraction kits, sequencing reagents, and other laboratory chemical and plastic consumables can introduce exogenous microbial DNA into the samples, contaminating the data. This is particularly problematic in microbiome studies, where the goal is to identify and characterize the microbial communities accurately. The presence of contaminant DNA can lead to false positives and obscure the true microbial signals [6; 167]. To address this, researchers should use kits and reagents that are certified for low microbial contamination, and include appropriate negative controls to detect and account for any contaminants. Rigorous protocols are essential to minimize contamination risks. These protocols should include steps for ensuring sample integrity from collection through to analysis. For instance, using sterile instruments and maintaining a clean working environment can help reduce the risk of introducing contaminants during sample collection. Additionally, protocols for DNA extraction and amplification should be optimized to minimize the risk of contamination from reagents and equipment [167]. Implementing stringent quality control measures is also crucial. This includes regularly validating the sterility of consumables, performing routine checks for contamination, and using advanced bioinformatics tools to identify and exclude contaminant sequences from the data analysis [6; 165]. Moreover, adopting standardized protocols across different laboratories can enhance reproducibility and comparability of results, helping to ensure that findings are robust and reliable. Collaborative efforts among researchers to share best practices and develop guidelines for contamination control can further enhance the quality of microbiome research. By addressing contamination concerns proactively, the scientific community can improve the accuracy and reliability of studies on the oral microbiome's role in OPMD and OSCC, ultimately contributing to better diagnostic and therapeutic strategies [6; 166].
## 4 Compactibility WMA with Zeolites  
Studies indicate the positive effect of zeolite addition on mix asphalt workability when using gyratory and vibratory compaction [24; 25; 26; 27]. [PERSON] and [PERSON] recommend vibratory compaction for compactibility testing [27; 28]. However, due to its many additional benefits, it is practical to use gyratory compaction. By recording the height changes over time it is possible to observe the change in volumetric density in relation to compaction energy which allows to draw a compaction curve. This curve is used to determine three parameters describing mix asphalt compactibility: the K compactibility index, the construction densification index (CDI), and the traffic densification index (TDI) [29]. This method is also recommended by National Asphalt Paving Association (NAPA) [30]. The Table 3 shows relation between compaction method and amount of voids in mix asphalt with zeolite addition, taking into account WMA production temperature, the type, and amount of added material. The analysis of presented results indicates improvement in compactibility, regardless of zeolite structure type and study method, excluding the Marshall method. At the same time, the range of technology temperatures is very wide, between 15 and 40 \({}^{\circ}\)C. The temperature decrease will be determined by a number of factors, including mix type, binder grades, or content of the recycled mix [30]. The air void content in samples prepared in Marshall compaction is higher in WMA with zeolites than reference mixes. However, in the study of gyratory and vibratory compaction improved workability WMA with zeolites (Table 3). This indicates that the compactibility of WMA zeolites should not be tested by the Marshall method. However, after one hour of WMA conditioning with zeolite, an improvement is shown in compactibility, even in tests performed using the Marshall compactor [33]. Conditioning the mixture in a laboratory is very important. The procedures for mixture conditioning according to AASHTO R 30 are used in the USA as standard. According to a report by National Cooperative Highway Research Program (NCHRP), WMA mixtures for both volumetric mixture design and mechanical property testing should be conditioned for 2 h at the planned compaction temperature [22]. According to authors of the review, due to the character of zeolite's action in this technology, the conditioning time should be set individually, depending on the type of zeolite structure. Determination of the
# Hake Fish Preservation Using Plant-Based Impregnated Polylactic Acid Food Films as Active Packaging
## 3 Results and Discussion
### Characterization of Impregnated PLA Based-Film
For food simulant D2, the migration was 121.827 \(\pm\) 11.9 mg dm\({}^{-2}\) after 10 days at 5 \({}^{\circ}\)C. Similar trends have previously been reported by other authors using polymeric films impregnated with olive leaf extract. [PERSON] et al. found migration values of 0.56 \(\pm\) 0.48 mg dm\({}^{-2}\) into simulant A and 2.01 \(\pm\) 0.27 mg dm\({}^{-2}\) into simulant D2 (olive oil) after 10 days at 40 \({}^{\circ}\)C using PLA films [49]. [PERSON] et al. also obtained higher migration values using ethanol 95% \(v/v\) (simulant C) in comparison with distilled water (simulant A) after 10 days at 40 \({}^{\circ}\)C using PET/PP films (0.15 \(\pm\) 0.012 vs. 0.09 \(\pm\) 0.012 mg dm\({}^{-2}\), respectively) [16]. According to current legislation, plastic materials and articles shall not transfer their constituents into food in quantities exceeding the overall limit of 10 mg dm\({}^{-2}\)[33]. The values observed in the present study significantly exceeded this regulation, thus prompting discussion on two critical aspects. Firstly, this legislation applies specifically to the migration of substances incorporated into plastics to produce a technical effect in the finished product or those used to provide a suitable medium for polymerization. It does not, however, address bioactive compounds that are not detrimental to human health. Currently, there is no specific legislation governing active packaging. Secondly, while food simulants provide a controlled environment for studying the migration and interaction of compounds, they have inherent limitations when extrapolating results to real food matrices. Real food systems exhibit greater complexity, with variable compositions (e.g., fats, proteins, water content), pH levels, and structural properties that can significantly influence the diffusion and activity of bioactive compounds. These complexities highlight the need for further research to validate findings in real matrices and inform the development of regulations specific to active packaging. These results suggest that films impregnated with olive extract may perform better with lipophilic media, making the proposed packaging potentially more effective at preserving fatty foods. In this sense, hake fillets were selected as a model of fatty food considering a total fatty acid content of 0.97-1.97% \(w/w\)[50]. Moreover, due to the rapid deterioration of fatty foods, as previously mentioned, they represent an appropriate choice for evaluating the effectiveness of the proposed active packaging.
# Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System
## 1 Introduction  
### Background  
Tire failure is a problem that occurs worldwide, and every driver knows that there are tire failures when driving a vehicle that can affect your safety, such as inflation pressure, overload, sudden braking, etc., which may be the cause of accidents. The primary source of tire failure is high shear due to plying anisotropy, modulus, rubber mismatch, and hydrostatic tensile stresses at the tread edge [1].  
In addition, the number of \"End-of-Life-Tires\" (ELT) is increasing at a rapid rate around the world, which has a tremendous adverse impact on the environment, ecological systems, and human health [2]. One of the options to take advantage of your tire's lifetime is retreeding.  
This leads to regularly checking the tires that can determine if they can be retreaded or discarded; however, carrying out this process does not guarantee that the retreaded tire can extend its useful life [3] as it should, although pressure inflation and road hazards are the main causes of continuous wear of tires both newly and already retreaded.  
Going into the retreading process, in addition to other factors such as the reliability with which a tire was retreaded, it was unreliable, as it was not guaranteed that the life of the tire could be extended as it should, or they only retreaded the tire to obtain a temporary solution for the driver or customer. In addition, there was a lack of confidence in retreading companies in general when making an incorrect diagnosis. Likewise, different tire retreading companies carried out diagnoses in an empirical way, and because of the results obtained due to the high demand for tire evaluations, it was possible to verify that there was a margin of error on the operator's decision when evaluating a tire.  
On the other hand, Artificial Intelligence (A.I.) is widely accepted as a technology that offers an alternative way of tackling complex and ill-defined problems. They can learn from examples; they can handle noisy and incomplete data; and, once trained, they can make reasonable predictions and generalizations. One branch of A.I. is fuzzy logic, which is more efficient than traditional logic by using multiple binding inputs to a single output in a non-linear domain [4].  
Consequently, due to this problem, the present work focuses on improving the possibility of delivering an accurate response regarding a tire's faults through fuzzy logic. The focus was primarily to contribute the solution to the hands-on human worker experience diagnosing tire failures using fuzzy logic in a web app to determine if a tire should be retreaded or scrapped.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 3 Research Methodology
### Attribute Analysis  
To provide a comprehensive understanding of prominent XAI frameworks, we will analyze them based on a set of predefined attributes. This analysis empowers researchers and practitioners to select the most suitable framework for their specific application domain. The attributes considered include the following:  
* _Year Introduced_: This indicates the framework's maturity and potential level of development.
* _Reference Paper_: The core research paper describing the framework's theoretical foundation and methodology.
* _XAI Category_: This classifies the framework based on its explanation approach (e.g., extrinsic, intrinsic, model-agnostic, model-specific, global, or local).
* _Output Formats_: The types of explanations the framework generates (e.g., arguments, textual explanations, or visualizations).
* _Current Use Cases_: Examples of real-world applications where the framework has been employed.
* _Programming Language_: The primary programming language used to develop the framework.
* _Machine- and Deep Learning Scope_: Whether the framework is designed for traditional ML models or specifically caters to deep learning models.
* Understanding Model Complexity: The framework's ability to explain complex models effectively.
* _Development Model_: Categorization as a commercial or open-source framework.
* _License Type_: Applicable only to open-source frameworks, specifying the license terms under which they are distributed.  
By analyzing these predefined attributes, we aim to create a detailed profile for each chosen XAI framework. This profile will highlight the framework's strengths, limitations, and suitability for various application domains.
## 3 Results and Discussion
### Influence of Cutting Width on Cutting Force Components  
A numerical simulation was carried out to verify the validity of the numerical model, evaluating the impact of different cutting widths on the F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\) components when milling the NHC core. This analysis was performed using RUM technology regardless of whether or not ultrasonic vibrations were applied. To carry out this study, four cutting width values were examined, including 4 mm, 6 mm, 8 mm, and 10 mm. Other cutting parameters remained constant throughout the analysis, including a feed rate of 2000 mm/min, a spindle speed of 3000 rpm, and an amplitude vibration is 25 \(\upmu\)m. The obtained results are illustrated in Figure 8 [20].  
The results of the numerical simulation confirm the experimental tendency noticed for the F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\) components, revealing a significant increase in these components as a function of the cutting width when milling the NHC structure, regardless of whether ultrasonic vibrations are used or not. Increasing the cutting width causes a distension of the contact surface between the milling cutter and the part, which results in an increase in the volume of material removed per unit of time. Similarly, a large cutting width can result in high friction resistance during the machining process, thereby causing an increase in  
Figure 7: Evolution of the F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\) components as a function of the mesh size.  
cutting force components. During the process of cutting the Nomex honeycomb structure using rotary ultrasonic machining, it is clear that the use of ultrasonic vibrations results in a remarkable decrease in the F\({}_{\mathrm{x}}\) and F\({}_{\mathrm{y}}\) components, with a reduction reaching up to 42%. In this context, the cutting tool undergoes intense rotation and vibrations, which encourage the formation of cracks in the walls of the NHC structure. This eases the penetration of the tool without encountering resistance from the material constituting the NHC structure. Although increasing the cutting width can improve cutting efficiency, it is also linked to an increase in cutting force components, which can lead to other complications such as premature wear of the cutting tool and degradation of the quality of the machined surface. It is important to determine an optimal value of the cutting width in order to reconcile these two characteristics when machining the NHC core. This optimal value aims to maximize the material removal volume while keeping the cutting force components at a satisfactory level. Thus, this approach guarantees acceptable cutting efficiency and robust numerical results.