esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation
### Edge Side  
#### 4.2.1 Reference Image Generator  
To produce a reference image, the original image was first converted with grayscale and then the background modeling introduced in Section 3 was performed. To do this, the Gaussian Mixture-based background extraction of OpenCV was used [23]. To make one background image, a series of previous 100 input images were referred and the threshold value was set to 16. Figure 4 illustrates a result of the Reference Image Generator.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Side** & **Component** & **Specifications** \\ \hline \multirow{3}{*}{Edge Side} & OS & Raspberry Pi OS \\  & CPU & Cortex-A76 CPU \\  & GPU & VideoCore VII GPU \\  & RAM & LPDDR4X 8 GB \\ \hline \multirow{3}{*}{Cloud Side} & OS & Windows 11 \\  & & Intel(R) Core(TM) \\ \cline{1-1}  & CPU & i7-13700K \\ \cline{1-1}  & GPU & Nvidia RTX4090 \\ \cline{1-1}  & RAM & DDR5 32 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware specification.  
Figure 3: Visualization examples of a portion of the dataset: The dataset consists of images and annotations, with classes and bounding boxes labeled for vehicles. In this image, magenta color boxes represent truck labeling, while green color boxes represent car labeling.  
Compared to Figure 3, Figure 4 shows that moving objects such as vehicles have been removed. This result occurs because the reference image generator applies a statistical model based on Gaussian distribution, selecting representative colors that show temporal consistency across multiple frames. At each pixel location, the color value that appears with the highest frequency is determined as the background, which becomes a reference model that effectively segments areas of moving objects when compared with new input images.
## 3 Results
### Particle Size Dependence of NO and NO\({}_{2}\) Emission  
The influences of coal particle size on transformation behaviors of fuel nitrogen in coal are still ambiguous, especially the possibly different particle size dependence of NO and NO\({}_{2}\). Figure 7 illustrates the effect of particle size on the NO\({}_{x}\) release from synthetic coal \"M6\" with pyridinic nitrogen under the oxy-fuel condition of 1200 \({}^{\circ}\)C. It can be observed from Figure 7a that with the increase of particle size, the conversion ratio of NO from fuel nitrogen first increases and then decreases with the maximum conversion ratio present at a particle size of 64-98 \(\upmu\)m. This can be explained by the competition between char-O\({}_{2}\) reactions and char-NO reactions on particle surfaces. On surfaces of larger particles, the char-O\({}_{2}\) reactions are further enhanced which gives rise to a decrease in NO formation [29]. The particle size dependence of the NO\({}_{2}\) conversion ratio from fuel nitrogen of pyridinic form differs greatly from the case of NO. As depicted in Figure 7b, the NO\({}_{2}\) conversion is increased with the particle size in the range of \(<\)150 \(\upmu\)m. The transformation of nitrogen-containing functional groups in coal is strongly affected by the specific surface area of the coal particles. Compared with \"M6\", the synthetic coal \"M5\" shows a similar relationship between particle size and NO\({}_{x}\) emission.
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
## 2 Types of Organic Solar Cells  
Organic photovoltaic cells are classified into two types based on the molecular framework, namely; small molecule-based and polymer-based OPVs [19]. Small donors have a well-defined molecular weight, are simple to synthesize and purify, and have small batch-to-batch variability [43]. Improved crystallization, as well as the capacity to achieve high phase purity and modify crystal alignments, enables the development of small-molecule solar cells with high charge mobility and low energy losses [44]. Polymer-based solar cells (PB-OSCs), on the other hand, have quickly emerged as one of the most promising low-cost solar cell alternatives, due to the ability for large-scale and flexible solar cells fabrication via solution processing [45]. Small molecule-based solar cells have increased performance in terms of efficiency from 0.001% in the 1990s [19] to more than 14% in 2018 [46] which is a significantly higher efficiency than any OPV (before these findings the maximum PCE of OPVs achieved by PB-OSCs, was approximately 13%). Below, we compare these two classes which are also divided into two subclasses namely, fullerene and nonfullerene. We are focusing specifically on recent advances made in (i) nanostructures, (ii) synthetic pathway, and (iii) operating mechanism towards improved performance efficiencies for innovative applications.
## 2 Materials and Methods
### Ground-Based Spectrum Measurements
To identify the best observation time period for each data, the in situ measurements were categorized into three groups: 'Morning', 'Afternoon' and 'Whole-day', each representing a 1-day resolution with the mean values recorded within the respective time periods. Specifically, 'Morning' corresponds to the time frame between 7:00 AM and 12:00 PM, while 'Afternoon' encompasses the period from 12:00 PM to 5:30 PM.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 5 Conclusions  
Information and Communication Technologies (ICT-based) solutions are improving the productivity of all industrial sectors, even agriculture and livestock. They should also improve the well-living of the people across the World. Nevertheless, in many cases, the success of one of these solutions is linked to the availability of communication infrastructures that could provide support for the \"C\" of ICT. In developed countries, where we could assume that communication infrastructures should have a high degree of deployment, agriculture and livestock are usually related with rural areas where these infrastructures begin to loss their capacities. Unfortunately, in the rest of the World, this lack of communications capacities begins even in the big cities.  
If we put the focus on extensive livestock, we can find that, in Europe, it is usually restricted to remote areas, where the communications infrastructures are not capable to provide any kind of services for deploying ICT solutions. In other countries, we can find extensive livestock also near the big cities, but this does not guarantee the availability of communications services (or they are but a high price).  
This paper has presented a solution for dealing with this problem of lack of stablished communication infrastructures for controlling the movements of animals from extensive livestock farms. It is based on the development of an animal wearable that is equipped with inertial sensors, GNSS and wireless communications. It also includes enough computation capacity for making adaptive analysis and VQ compression that provides high compression ratios with good peak signal-to-noise ratios, and allows for real-time monitoring and logging of cattle's position and activities. This wearable has been evaluated in real scenarios, such as mountains and forest, in the context of a project that aims to monitor animals in extensive farming in the north part of Spain (in the Pyrenees area, near the border with France). This project includes the necessity to track the movement of the animals to evaluate their impact on the plant biodiversity, as well as the intention to virtually reduce the distance between the animals and the shepherds.  
Conceptualization, R.C. and F.J.Z.-S.; methodology, F.J.Z.-S. and T.B.; hardware and software, R.C., A.H. and A.M.; design, T.B.; validation, R.C., A.H. and T.B.; writing, review and editing, All; project administration and funding acquisition, R.C. and F.J.Z.-S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Aragon regional Government thought the program for R&D groups (T27_20R and T59_20R) and by the Rural Development Program in conjunction with FEADER from EC (GCP2019004100). The work of [PERSON] and [PERSON] has been partially supported by the Spanish Government, program Torres Quevedo (PTQ2017-09481 and PTQ2018-010045, respectively).  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Aspect Extraction  
The next step of the proposed model pipeline is automatically extracting semantic aspects (which are also called topics) from the pre-processed textual corpus. In this paper, a modified LDA model, called Seeded-Aspects LDA (SA-LDA), is proposed. It has an unlabelled pre-processed textual corpus that contains opinion units of a specific domain and an aspect specification as an input. An aspect specification is known as predefined aspects (seed words). In basic LDA, the model tends to only detect the most obvious aspects of a text corpus which may not cover the expected and desired aspects. Thus, we proposed a modified LDA model by providing seed words (seed aspects) to guide the model to only generate words from analogous seed aspects as presented in Figure 2.  
Figure 1: The architecture of the proposed framework.  
The SA-LDA at its basis comprises an LDA-based topic modelling, and it is extended with biased topic modelling hyper-parameters (\(\beta\) and \(\alpha\)) that are based on continuous word embeddings. The number of aspects (\(k\)) is set based on the number of unique main aspects needed. Each review is modelled by an aspect and contains a sentence. The proposed model in plate notation is illustrated in Figure 2, where the generative hypothesis algorithm is described in Algorithm 2.  
```
1 For each aspect \(k=1\ldots\ldots\) \(K\),  Choose seed aspect \(\mathcal{O}_{k}^{A}\ \sim\ Dir(\beta)\).
2 For each review \(d\),  Choose \(\theta_{d}\ \sim\ Dir(\alpha)\).  For each token \(w_{n},\ n=1\ldots\ldots\) \(N_{d}\),  Sample \(\pi_{d,n}\ \sim\ MaxEnt\big{(}\lambda_{x_{2u_{n}}}\big{)}\).  Draw a topic \(Z_{n}\ \sim\ Multi(\theta_{d})\).  Draw an indicator \(y_{d,n}\sim\ Bern(\pi_{d,n})\)  if \(y_{d,n}=\Lambda\):  Sample a word \(w_{n}\ \sim\ Multi\Big{(}\mathcal{O}_{Z_{n}}^{A}\Big{)}\).
```  
**Algorithm 2** Algorithm for the generative hypothesis  
We provided the model with several seed words for each main aspect as shown in Table 2. After feeding in unique aspects and seeded words for each dataset, each review sentence becomes ready for the next phase of the sentiment analysis task as described in the next subsection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Aspect** & **Seed Words** \\ \hline \multirow{3}{*}{Movie} & ACT & performance, actor, play, character, role, scene \\  & PLOT & story, script, sequence, scenario \\  & SOUNDTRACK & sound, audio, music, playlist, effect \\ \hline \multirow{3}{*}{Restaurant} & FOOD & taste, dish, dinner, appetizer, menu \\  & SERVICE & internet, parking, delivery, location, seating, staff \\ \cline{1-1}  & PRICE & payment, price, discount, cost, pay offer \\ \hline \multirow{3}{*}{Domestic Saudi Airlines} & FLIGHT & meal, internet, entertainment, seat, cleanliness, drink \\ \cline{1-1}  & SERVICE & lounge, ticket, baggage, upgrade, punctuality \\ \cline{1-1}  & STAFF & crew, captain, pilot, service, steward \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aspects and seed words for each domain.  
Figure 2: The proposed model in plate notation.
# Biomethane Potential of Sludges from a Brackish Water Fish Hatchery
## 3 Results and Discussions
### Biochemical Methane Potential (BMP)  
The Biochemical methane potential value obtained with the different applied I/S ratios was significantly different, with values gradually decreasing from the highest inoculum to substrate ratio (IS50) to the total absence of inoculum (S100). BMP mean values are reported in Figure 3. The highest obtained BMP (564.2 \(\pm\) 16.9 NmL CH\({}_{4}\)/g VS, in IS50) was comparable to the yields of 396.8-779.8 NmL CH\({}_{4}\)/g VS obtained in previous tests on thickened sludge from trout production in fresh water [4; 5] and to the highest yield of 460 L/kg VS, reported by [PERSON] and [PERSON] [32] for effluents from trout in freshwater.  
Figure 1: Cumulative methane production from automatic methane potential test system (AMPTS) reactors during the biochemical methane potential (BMP) tests in relation to the different applied inoculum/substrate ratios, based on volatile solid content: IS03 is 3:1 ratio; IS30 is 30:1 ratio; IS50 is 50:1 ratio; S100 is only substrate (without inoculum).  
Figure 2: Daily methane production from AMPTS reactors during the BMP tests in relation to the different applied inoculum/substrate ratios, based on volatile solid content: IS03 is 3:1 ratio; IS30 is 30:1 ratio; IS50 is 50:1 ratio; S100 is only substrate (without inoculum).  
Moreover, the highest BMP yield of this study resulted higher than a previous literature outcome of 270-330 NmL CH\({}_{4}\)/g VS, reported by a research carried out in an AMPTS system with sludges of turbots fishes (_Scophthalmus maximus_) bred in brackish water with 15.2 g/L salinity [20]. These authors, however, used additives like glycine betaine and trehalose to improve biogas production rates, while in the present study no additives were used. Other research studies have been carried out with different laboratory methods, which have not always allowed to reach the determination of BMP value, but gave useful indications for a comparison with the present study. With the same fish species (_Sea Bream_), for example, methane yields of 0.45 L CH\({}_{4}\)/kg input sludge have been determined in brackish conditions and with a continuous load AD process [8]. [PERSON] [16] obtained methane yields of 160-241 NmL CH\({}_{4}\)/g VS with _Atlantic Salmon_, reducing the saline content from 35 to 17.5 g/L respectively. Finally, [PERSON] and colleagues [33] found methane yields of 203 mL CH\({}_{4}\)/g COD with a semi-continuous digester operating with sludges from a _turbot_ RAS. The inoculum used in these studies was municipal sewage sludge and cow manure [16] or digestate from AD of various fish waste adapted to saline conditions [33; 34; 35]. From this analysis, it emerges that the current experimentation gave in any case good methane yields, even without the use of a specialized inoculum or a community already adapted to saline conditions, provided that a sufficiently high inoculum/substrate ratio is maintained. These basic indications from the batch BMP assays can be of extreme interest for successive experimental works at larger scale or on continuous mode.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 4 FAPEC Tailoring for the KMALL Data Format  
### Preliminary Data Analysis  
In order to design a new pre-processing stage, we need to know the structure of our data. In this case, we have decided to narrow it down to the datagram level. That is, we perform a preliminary analysis to find the most relevant datagrams in a.kmal (or.kmwcd) file. We have proceeded as follows:  
1. Implement a basic decoder of the KMALL format in Python.
2. Implement a Python class that acts as top level API for the KMALL files.
3. Write a Python script that takes a directory and an extension and returns the percentages of every datagram type on each file.  
Figure 6: The FAPEC data compressor structure.  
The distribution of the datagrams strongly depends on the captured scene. That is, on the water column and seafloor characteristics, number of beams, resolution, ping rate and other physical properties of the echosounder. In our dataset, the result is that in.kmal files, MRZ datagrams percentage oscillates between 70% and 95% of the total file size. The remaining space is mainly occupied by SKM datagrams. In.kmwcd files MWC, datagrams are more than 99% of the file size. Finally, in files containing MRZ and MWC datagrams, these occupy between 89% and 92% of the total size, with the remaining being mainly SKM datagrams. More detailed information about the used dataset is available in Section 5.  
Clearly, the most interesting datagram types are MRZ and MWC, although in some situations SKM, datagrams must also be taken into account. Hence, we propose three different algorithms, one for each datagram type.  
Even though this paper is mainly focused on the KMALL data format, the algorithms presented in the following sections can be applied to similar data structures from other formats or vendors. Before continuing, we must pay attention to the FAPEC data chunking and the datagram identification. The former is a useful feature, although it might be risky because it may result in splitting a datagram into different chunks, hence making it difficult to read. For this reason, we will automatically reduce the actual chunk size (with respect to the value given by the user) to ensure an integer number of datagrams per chunk. With this approach, the observed efficiency in the identification and handling of complete datagrams in a chunk is between 74% and 100%, with an average of 95%. With this constraint, in order to identify a datagram type, we just need to read the corresponding field in the header. Then, if it is an MWC, an MRZ or an SKM datagram, we will proceed with one of the following algorithms. Otherwise, samples are directly sent to the entropy coder.
# Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology
## 1 Introduction  
Industry and society benefit greatly from autonomous robots that are able to perform manipulation skills on complex objects. Recent work shows that it is possible to learn grasping [1; 2] and manipulation [3; 4] skills on everyday objects. Although prior work has been undertaken [1] including deformable objects in the set of daily household objects, most robotic manipulation tasks are limited to objects with considerable stiffness, such as rubber ducks. Highly deformable objects--for example, cables or clothes--are often left out of the set due to the many challenges associated with handling the infinite amount of configurations these objects can be in [5].  
Prior work [6; 7; 8] has focused on scripting solutions for the robotic manipulation of deformable objects. However, the engineering effort to make this work is considerable, and the designed solutions often break down when the environment changes. Recent work [9; 10] has leveraged learning methods that are able to generalize to a wider range of deformable objects. Often, these methods require the definition of the task in simulation in order to generate rollouts in the environment. Unfortunately,simulating cloth is both challenging and computationally expensive. Furthermore, there is no guarantee that the acquired skills in simulation are transferable to the real world.  
A typical approach for the robotic folding of textile relies on the use of vision in order to detect grasping points and to perform texture segmentation and pose estimation [7; 8; 11], as well to estimate the state [9] and--in the case of reinforcement learning (RL)-based approaches--the reward function [12]. However, while vision is instrumental for recognizing and localizing objects, touch and force measurements become important once contact occurs, and the object is explored using the end-effector [13]. Recent work [14] has illustrated this idea by applying a touch-based control method for a ball repositioning task, rolling a dice and the deflection of a stick. In line with other authors [14; 15], we believe the use of tactile input and its fusion with other sensory information is crucial to learn complex robot manipulation tasks in an efficient way.  
In this work, we deviate from the wider focus on using vision for state and pose estimation. Instead, we propose a cost-effective solution for the robotic folding of textile. Our approach is based on the use of the reinforcement learning of a shallow neural controller for a low-cost setup of a dual robotic arm, visible in Figure 1. Instead of engineering the reward function, we developed a smart textile piece that is able to detect whether it has been folded.
## 2 Hair Loss  
There are various forms of hair loss. Androgenetic alopecia (AGA) is the most common form. It is highly heritable, and genetic susceptibility loci have been identified [9; 10]. However, its onset is not congenital. AGA is found in about 50% of men and 40% of women around the age of 70 [11; 12]. In this context, AGA is also known in women as female pattern hair loss and in men as male pattern hair loss.  
In men, the typical receding hairlines and baldness at the back of the head develop, while in women, a clearing of the mid-crown is seen initially. In AGA, the anagen/telogen ratio is altered, i.e., the telogen phase is prolonged, and the anagen phase is shortened [13]. At the same time, the size of the hair bulb is also affected by the hair thickness [14]. Possible causes of AGA are different variants of the androgen receptor as well as an increased sensitivity to 5 alpha-dihydrotestosterone (5a-DHT). Therefore, inhibition of 5a-DHT significantly ameliorates AGA.  
Classification systems are available to determine the stage of hair loss in men and women. Here, the Hamilton Norwood system and the Ludwig system are the most commonly used [15].  
The Hamilton Norwood system for male genetic hair loss was first created by [PERSON] in 1951 and modified by [PERSON] in 1975. Generally, the stages of hair loss are divided into two categories--(a) scalp that is not bald (types I-III) and (b) scalp that is bald (types IV-VIII).  
A similar scheme for androgenetic alopecia in women was developed by [PERSON] in 1977 [15]. Female hair loss is classified into three grades as follows:  
Type I: Perceptible thinning of the hair on the crown, limited by a line situated 1-3 cm behind the frontal hair line.  
Type II: Pronounced rarefaction of the hair on the crown within the area seen in type I.  
Type III: Full baldness (total denudation) within the area seen in types I and II [15].  
Another type of hair loss is alopecia areata (AA), characterized by circular hair loss. The aetiology of AA is not fully understood, but there are indications for a T-cell-dependent auto-immune disorder.  
In contrast to AGA and AA, cicatricial alopecia comprises a group of hair loss disorders in which the hair follicle is irreversibly destroyed. Thus, a permanent loss of hair in the affected patches occur. Telogen effluvium is a non-scarring diffuse hair-loss resulting from the early entry of hair in the telogen phase. It is triggered among others by physiological and metabolic stress. Senescent alopecia (SA) is defined as an ageing of the follicle. This may be caused by reactive oxygen species and other endogenous and exogenous stress factors.  
Hair loss is based on various biochemical mechanisms including inflammation [16; 17; 18; 19] and oxidative stress, androgenic metabolism [20], reduction of microcirculation [21], and alteration of the cross-talk between the matrix cells and dermal papilla [22]. Thus, depending on the mechanism,cosmetic intervention with food supplements containing anti-oxidants or which aim to improve the microcirculation like green tea [23] can help with hair loss.