esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Dandruff--Dermatological Description, Treatment, and Skincare Methods  
Dandruff is a common scalp condition affecting almost half of the world's population. It manifests as dry, flaking desquamation. Seborrheic dermatitis (SD) and dandruff are considered to be basically the same condition, differing in severity and the parts of the body affected. Dandruff is restricted to the scalp (rarely eyebrows, mustache, and beard), while seborrheic dermatitis can also be present in other seborrheic regions of the body, including the face, retro-auricular area, upper chest, and upper back. Its clinical presentation may range from mild patches to diffuse scalp scaling. It is a chronic and relapsing inflammatory skin condition. In the case of dandruff, the inflammation is not visible [22; 23; 24].  
In the case of immunosuppression, endocrine, or neurological diseases, SD is much more common. Its triggering factors include stress, and cold or dry weather. Also, external factors such as dust or dirt and the excessive use of hair cosme can exacerbate SD. The negative social impact of dandruff is also described [22; 23; 24].  
Despite its high prevalence, the exact pathophysiology is not well established and is understood to be multifactorial. For a long time, _Malassezia_ spp. has been considered the main factor leading to seborrheic dermatitis, based on the fact that the overgrowth of _Malassezia_ was observed on the skin and antifungal treatment helped manage symptoms [22]. More recent studies (sequencing-based) showed that other microbiota also change in addition to _Malassezia_ spp. [25; 26; 27]. _Staphylococcus_, especially _S. aureus_, is observed in SD, and is correlated with itching and scaling. It is probable that an environment with an overproduction of unsaturated fatty acids caused by _Malseszia_ promote _S. aureus_. [28; 29; 30]. Also, a higher incidence of _Staphylococcus epidermidis_ is observed [31]. _Cutibacterium_ levels are decreased, although this usually develops in sebum-rich areas [31; 32].  
In seborrheic dermatitis, the increase in ceramide, cholesterol, and fatty acids is also observed, and the permeability of the epidermal barrier is disturbed. Free fatty acids and other metabolites can increase sebum production on the scalp [33; 34; 35].
## 3 Results
### Tempered EM: Application to Mixtures of Gaussian
#### 3.2.1 Context and Experimental Protocol
Where \(0<T_{0}\), \(0<r\), \(0<b\) and \(0<a<1\). The oscillations in this profile are meant to achieve a two-regimes behaviour. When the temperature reaches low values, the convergence speed is momentarily increased which has the effect of \"locking-in\" some of the most obviously good decisions of the algorithm. Then, the temperature is re-increased to continue the exploration on the other, more ambiguous, parameters. Those two regimes are alternated in succession with gradually smaller oscillations, resulting in a multi-scale procedure that \"locks-in\" gradually harder decisions. For some hyper-parameter combinations, the sequence \(T_{n}\) can have a (usually small) finite number of negative values. Since only the asymptotic behaviour of \(T_{n}\) is the step \(n\) matters for convergence, then the theory allows a finite number of negative values. However, in practice, at least for the sake of interpretation, one may prefer to use only positive values for \(T_{n}\). In which case, one can either restrain themselves to parameter combinations that result in no negatives values for \(T_{n}\), or enforce positivity by taking \(T_{n}\leftarrow\mathit{max}(T_{n},\epsilon)\) with a certain \(\epsilon>0\). For our experiments, we select the hyper-parameter values with a grid-search. The \"normalised\" _sinc_ function is used \(sinc(x)=sin(\pi x)/(\pi x)\) and the chosen tempering parameters are \(T_{0}=5\), \(r=2\), \(a=0.6\), \(b=20\) for the experiments with the _barycenter_ initialisation, and \(T_{0}=100\), \(r=1.5\), \(a=0.02\), \(b=20\) for the _2v1_ initialisation. We have two different sets of tempering hyper-parameters values, one for each of the two very different initialisation types. However, these values then remain the same for the three different parameter families and for every data generation within them. This underlines that the method is not excessively sensitive to the tempering parameters, and that the prior search for good hyper-parameter values is a worthwhile time investment. Likewise, a simple experiment with 6 clusters, in supplementary materials, demonstrates that the same hyper-parameters can be kept over different initialisation (and different data generations as well) when they were made in a non-adversarial way, by drawing random initial centroids uniformly among the data points. Figure 4: 500 sample points from a Mixture of Gaussians with 3 classes. The true centroid of each Gaussian are depicted by black crosses, and their true covariance matrices are represented by the confidence ellipses of level 0.8, 0.99 and 0.999 around the centre. Each sub-figure corresponds to one of the three different versions of the true parameters. From (**a**) to (**c**): the true \(\mu_{k}\) of the two left clusters (\(\mu_{1}\) and \(\mu_{2}\)) are getting closer while everything else stays identical.
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis of Volatile Compounds
For the same reasons, our data differ from older ones published by Senatore & Bruno on _P. spinosa_ harvested in Southern Italy. Their results show a predominance of oxygenated sesquiterpenoids in the EO (60.2% of the oil). Among the 38 components that were identified, the most abundant were germacra-1(10),5-dien-3,4-diol (18.4%), \(\alpha\)-cadinol (14.1%), 3-acetoxygermacra-1(10),5-dien-4-ol (13.0%), \(\tau\)-cadinol (8.2%) and \(\delta\)-cadinene (5.8%) ([PERSON] et al., 2010). Several volatile plant metabolites and their mixtures are known to exert antimicrobial effects. For instance, \(\beta\)-pinene, one of the main volatile compounds from the aerial parts of _P. spinosa_, is highly toxic to _Candida albicans_ and several pathogenic bacteria, such as the methicillin-resistant _Staphylococcus aureus_ (MRSA), which causes severe infections in humans ([PERSON] et al., 2010). \(\beta\)-Caryophyllene, a well-known sesquiterpene, is also toxic to MRSA and exerts anti-inflammatory activity via inhibiting different inflammatory mediators such as interleukin 1\(\beta\), interleukin-6, and tumor necrosis factor-\(\alpha\). It has also analgesic, myorelaxing, sedative, and antidepressive effects ([PERSON] et al., 2010). Other terpen contributing to the volatile profile of _P. spinosa_, such as p-cymene and \(\beta\)-selinene, are known to exert antioxidant, analgesic, anti-inflammatory, and antimicrobial activities ([PERSON] et al., 2010; [PERSON] et al., 2010). Overall, these data suggest that these compounds may be involved in the therapeutic effects of _P. spinosa_, although further studies are needed to verify this hypothesis.
## 2 Related Work  
### Single-Stage Detectors  
Different from the two-stage detection algorithm, the single-stage detector directly trains the convolutional neural network model to map image pixels to the coordinates of the bounding box. In 2016, [PERSON] et al. proposed a real-time detector called You Only Look Once (YOLO) [5]. YOLO divides the image space into a fixed number of grids and then predicted each grid. YOLO achieves relatively good detection results at the time. However, YOLO only uses the last feature map to predict, which is not suitable for multi-scale object detection. Later, to solve the limitations of YOLO, [PERSON] et al. proposed another single-stage object detector--Single-Shot MultiBox Detector (SSD) [4]. SSD and YOLO both use a convolution neural network for prediction. Still, SSD is different from YOLO in (1) using multi-scale feature map detection; the advantage of this is that a relatively large feature map is used to detect relatively small objects, and small feature maps are responsible for detecting large objects; (2) unlike YOLO, which uses full connectivity, SSD directly uses convolution for detection; (3) SSD draws on the concept of anchor in Faster R-CNN [6], and each cell sets prior boxes with different scales as the basis of the predicted bounding box, which reduces the difficulty of training. Some other single-stage detectors also be proposed recently, e.g., Single-shot Refinement [25], RetinaNet [26], and CornerNet [27].  
SSD uses the adjusted VGG as the backbone network and then adds additional convolution layers to obtain more feature maps for detection. A basic SSD model is shown in Figure 1. SSD predicts objects on multiple feature maps, and each feature map predicts objects of different scales. In the prediction module, it is divided into two parts: positioning and classification. The redundant prediction frame is then filtered through an algorithm called non-maximum suppression to form the final prediction result. Using multiple feature maps for object detection, the detection accuracy of SSD is comparable to Faster R-CNN, and the speed is faster than it. However, because a pre-trained VGG or even deeper network is required as the backbone network, SSD is not ideal for embedded devices with limited memory. Meanwhile, it is not suitable for detecting small objects as each feature map is predicted separately.
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 3 Methodology
### Task-Adaptation Approach
#### Sentiment Models  
For the sentiment task, we further pre-train three different sentiment models using the three sentiment datasets: STD, SRD, and STRD. The first model is the QARiB-Sentiment-Tweets (QST) Model, which is a QARiB language model that was further pre-trained for 300 k training steps and roughly 28 epochs using the STD (shown in Table 1). The second model is the QARiB-sentiment-reviews (QSR) Model, which is a QARiB language model that was further pre-trained for 300 k training steps and roughly 26 epochs using the SRD(shown in Table 2). Despite the fact that the QST and QSR models were trained for the same number of training steps (300 k steps) and roughly the same data size (see Tables 1 and 2), Table 5 shows that the QSR has lower validation loss and better perplexity results than the QST. The reason could be that the review data has a longer text and is less noisy than the tweet dataset. As a result, we decided to continue training from the last checkpoint of the QSR model and trained the third model, which we call the QARiB-sentiment-reviews-tweets (QSRT) model, for an additional 300 k training steps (600 k in total with QSR training steps). The QSRT model was trained for roughly 52 epochs using STRD. Table 5 shows that the QSRT model outperformed the QST and QSR models in terms of training results. This was expected, given the increased amount of training data and the variety of data types (i.e., tweets and reviews). Table 5 provides additional details about the model's training results.  
Emotion ModelsFor the emotion task, we further pre-train two different emotion models using the emotion dataset ETD. The first model is the QARiB-emotion-300k (QE3) model, which is a QARiB language model that was further pre-trained for 300 k training steps and roughly 28 epochs using the ETD (shown in Table 4). The second emotion model, known as the QARiB-emotion-600k (QE6) Model, was trained by continuing the training from the last checkpoint of the QE3 model using the same dataset ETD. The QE6 model trained for an additional 300 k steps (600 k in total including the QE3 training steps) and around 31 epochs. Table 5 shows that there was no improvement in the training results of the QE6 models when compared to the QE3 models. In fact, there was an increase in QE6 validation loss and perplexity. According to Table 5, the QSRT model has the lowest validation loss and perplexity score. Furthermore, we can see that the sentiment models outperform the emotion models in terms of validation loss and perplexity score. Afterwards, each of the three sentiment models was evaluated by fine-tuning using various sentiment datasets. Additionally, the emotion models were tested by fine-tuning them using various emotion datasets to examine the effectiveness of the within-task adaptation approach.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 2 Adaptive Modulation Overview  
The data rate at the receiving end hugely depends on the measure of SNR, in the case of an atmospheric disturbance. Mathematically, in view of the LOS nature of the link, at larger SNR, the capacity of the network with respect to SNR can be shown as  
\[C(\text{SNR})=d\cdot\log(\text{SNR})+\mathcal{O}(\log(\text{SNR})) \tag{1}\]  
where \(d\) is the Degrees of Freedom (DoF) communication system.  
Despite the advantages provided by drones, the exact unique position of drones makes them susceptible to attenuation from environmental factors, and the major one among them is rain [31]. The attenuation occurs due to the scattering and dielectric loss. Apart from the height at which the drone operates, the frequency of operation also makes rain an essential parameter while designing a drone-based communication system [32; 33; 34; 35; 36]. This is because attenuation is dependent on the size of the scatterer. Since the mathematical analysis of the effect of rainfall is a tedious process, ITU has made an empirical model where the attenuation per km (\(\gamma\)) is modeled using rainfall rate (R) and operating frequency (f), where R is mentioned in (mm/h) [37]. In essence, the sum rate and reliability of the drone communication experience considerable degradation due to rain [32; 33].  
Adaptive Modulation (AM) is an approach in the physical layer for optimizing the performance of a single wireless connection over flat fading channels. AM helps the communication system to achieve the optimal data rate and error rate by adapting the modulation order. One advantage of AM is that the system offers communication even when there is a disturbance in the channel. Another advantage is that the overhead required in the communication is reduced, which would have to take a significant part of the communication [20]. Similarly, the latency is also reduced, as a guaranteed communication is offered by the system [38].
# Revisiting the Role of Knee External Rotation in Non-Contact ACL Mechanism of Injury
## 3 Results  
For the FE meshes used, a convergence study was not conducted, although different meshes were tested. Since the obtained results are in accordance with the literature, it can be assumed that the meshes used were reasonably refined. The maximum values of stress and strain obtained for the different structures of the ligament are shown in Figure 5 and Supplement 2. The whole ligament comprises the AM and PL bundles covered by the soft connective tissue. Although a large value of strain occurs on the connective tissue, this is not critical for the ligament rupture.  
Figure 5: Output of peak stress and strain for the whole ACL ligament and AM/PL bundles.  
The peak stress (11 MPa) and strain (4.8%) were obtained at the AM bundle with the combined knee valgus (50 Nm abduction moment), 20-degree of knee flexion during landing (4000 N axial load) and with tibial ER (15 Nm and 20 Nm), corresponding to simulations #2 and #6. Under the same loading conditions but with a tibial IR instead of an ER, the simulations showed lower peak stress and strain values. The anterior tibial load had a minor role in both ACL stress and strain. The ACL stress and strain values were consistently higher when the axial load was 4000 N (simulation of landing) as compared to 700 N (simulation of walking).  
The location of peak stress and strain was consistently at the AM bundle and always at the midportion of the ACL (Supplement 3). At the PL bundle, the location of peak stress and strain was also at the midportion when tibial ER momentum was applied; however, when the tibial IR momentum was applied coupled with 700 N axial load, the peak stress and strain were located at the distal and posterior portions of the PL bundle.  
Figure 6 displays the location of the ACL strain evolution comparing the ER and IR under the same boundary conditions, #6 and #14 simulations, respectively. The highest
## 6 Conclusions  
To sum up, this work considers a vehicle model combined with a tyre wear model in order to optimise tyre and suspension parameters to minimise tyre wear, enhance comfort and improve stability. In addition to the search for the optimum tyre and suspension design, the optimisation is also used as the tool to identify any conflicting or not relations between the design variables and the optimisation objectives.  
In summary, the following conclusions are extracted.  
* Comfort illustrates the same conflicting relation with wear as with vehicle stability. This means that the increase of the suspension travel, hence degradation of handling, leads to an increase in wear but to an improvement in comfort.
* Wear could increase up to 21% in different road profiles, while the appropriate tyre design could provide only 2% increase if the road roughness changes from Class A to B. The appropriate tyre design could be extracted using the optimisation method proposed in the current work.
* For the same tyre design, two pressures could optimally combine comfort, wear and stability in two road roughness cases but also maintain wear at the same levels in these two cases. The recursive feasibility of this outcome should be tested, but it is an interesting remark for the current case study.
* Regarding suspension types, according to the results, the type and their control algorithm should be selected with regards to tyre wear damage. In the current case study, SH-2 and SH-ADD-2 seemed to be able to provide the best wear performance. After the selection of the control algorithm, the tuning of the suspension parameters, i.e., stiffness and damping coefficient, should take place mainly with regards to comfort and vehicle handling. This is because according to the results it seems that different configurations do not affect wear to a great extent.  
Further work is in progress to develop active suspension systems using novel control algorithms to decrease wear. At the same time, tyre and suspension design should be investigated in depth with regards to the tyre wear using more detailed wear and vehicle models. Finally, their experimental validation and investigation is an aspect that should be considered further.  
Conceptualisation, G.P., J.J. and L.D.; methodology, G.P.; software, G.P.; validation, G.P., J.J. and L.D.; formal analysis, G.P., J.J. and L.D.; investigation, G.P.; resources, G.P., J.J. and L.D.; data curation, G.P., J.J. and L.D.; writing--original draft preparation, G.P.; writing--review and editing, J.J. and L.D.; visualisation, G.P.; supervision, J.J. and L.D.; project administration, J.J.; and funding acquisition, J.J. and L.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by TRENoP (Transport Research Environment with Novel Perspectives) at KTH Royal Institute of Technology and the Centre for ECO2 Vehicle Design, which is funded by Vinnova (the Swedish innovation agency) through grant number 2016-05195.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Frequency Analysis of Accident-Inducing Rainfall  
It is important to determine the rainfall amount that causes the most traffic accidents to be able to respond adequately to an accident. The sources of rainfall data also determine the accident-inducing rainfall. In this study, AWSs and radar were used to obtain rainfall data at the time of traffic accidents, and the rainfall frequency of each rainfall data source was checked.  
Figure 3 shows a significant number of incidents with zero measured rainfall in all rainfall data sources, unlike the data recorded as rain conditions in the police reports. These data are based on the judgment of the police officer handling the accident at the accident location at that time, which might differ from the actual observed data of different data sources. The 14,388 cases among 24,797 traffic accidents (58%) under rainfall conditions according to the police accident report corresponded to no-rain conditions in the data of the AWS nearest to the accident location. In the case of the interpolated rainfall data from the distributed AWSs, the 9290 accident cases were also considered to cause traffic accidents in no-rain conditions (Figure 3b). The radar rainfall data for 6249 cases of traffic accidents represented the no-rain condition, which was lower than the cases of AWS observations. Most traffic accidents under rainfall occurred at rainfall of 0.1-5 mm per hour, with the exception of when the rainfall was observed as zero. The number of accidents is more strongly related to the range of rainfall frequency, that is, higher frequent rainfall ranges contained a larger number of accidents. Figure 4 shows the same frequency analysis of accident-induced rainfall with only fatal and severe accidents of 9039 cases among 14,388 accidents; however, similar results were presented for all rainfall data sources. The results indicated no differences in accident-inducing rainfall amounts based on the type of rainfall data; however, a high possibility of the rainfall conditions on the road being mismatched according to the type of rainfall data was confirmed. Therefore, as presented in the next sections, the difference between the spatial distribution of rainfall and accidents according to the types of rainfall data must be examined qualitatively based on the specific rainfall event and confirmed by quantitative indicators such as RAR.  
Figure 2: Distance between accident location and (**a**) nearest AWS location and (**b**) 250 m radar grid point.  
Figure 3: Number of accidents during 1 h accumulated rainfall measured by (**a**) rain gauges nearest to the accident location, (**b**) distributed rain gauges, and (**c**) radar.  
Figure 4: Number of fatal and severe accidents during 1 h accumulated rainfall measured by **(a)** rain gauges nearest to the accident location, **(b)** distributed rain gauges, and **(c)** radar.