esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 1 Introduction
### Discarded Hardware  
In addition to the natural resources on other planets (Section 1.3), recycling hardware is an important source of raw materials. In fact, an ESA study [39] examined the material distribution of the relevant hardware (Table 3). Obviously, most of the materials brought are light metal alloys.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Material Nature** & **Mass** & **Selection Arguments 1** \\ \hline \multirow{3}{*}{Al-alloys} & \multirow{3}{*}{44\%} & -Medium strength at low density; \\  & & -Cryogenic capability; \\  & & -Weldability; \\  & & -Corrosion resistance. \\ \hline \multirow{3}{*}{Ti-alloys} & \multirow{3}{*}{15\%} & -High strength-temperature resistance; \\  & & -Corrosion resistance-reduced density. \\ \hline Steel & 5\% & -High strength. \\ \hline Super-alloys & 2\% & -Highest strength. \\ \hline Cu-alloys & 1\% & \\ Polyurethane & 1\% & \\ Silicone & 5\% & \\ Epoxy & 4\% & \\ \hline \multirow{3}{*}{CFRP} & \multirow{3}{*}{15\%} & -Highest strength achievable; \\  & & -Low density; \\  & & -Corrosion resistance. \\ \hline GFRP & 2\% & \\ Polyimide & & \\ Linear Polyesters & \multirow{3}{*}{1\%} & -Special applications. \\ Ceramics & & \\ Others & & \\ \hline \hline \end{tabular}  
* Not necessarily applicable for the whole material group.  
\end{table}
Table 3: Material composition of typical spacecraft [39].  
Figure 3: Distribution of surface ice at the Moon’s south pole (**left**) and north pole (**right**). Reproduced with permission from [36], NASA, 2018.  
If future developments are included in this consideration [39], their share will increase, with a reduction in the number of alloys expected (Table 4). Consequently, it can be assumed that these materials are available as construction materials.
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 3 Experiments and Discussion
### Performance Analysis  
In this section, we compare the radar target attribute recognition network DynseNet, which is based on dense information dynamic redistribution, with other networks. Overall, DynseNet demonstrates superior performance in enhancing the effectiveness of detection algorithms compared to the four comparison methods.  
From the experimental results in Table 3, The bold text represents the best-performing algorithm under the current dataset. It can be observed that when the data is not padded, the proposed model's attribute recognition accuracy on D1 data exceeds that of the comparison algorithms by 27.46%, 3.95%, 1.33%, and 1.18%, respectively. For D2 data, the proposed model's accuracy exceeds the comparison algorithms by 19.32%, 3.32%, 2.48%, and 0.48%, respectively. When the data is padded, the proposed model's attribute recognition accuracy on D1 data exceeds that of the comparison algorithms by 22.16%, 6.68%, 3.04%, and 1.60%, respectively. For D2 data, the accuracy exceeds the comparison algorithms by 10.19%, 2.12%, 2.02%, and 1.21%, respectively. It can also be observed that both the proposedmethod and the comparison algorithms show improvements on D1 and D2 data after padding, with the proposed method improving by 5.69% and 14.41%, respectively, which demonstrates the effectiveness of our preprocessing for radar echoes.  
To more intuitively demonstrate the superiority of our proposed model, we performed visualization processing on the detection results from different models applied to single-frame radar data. The resulting comparison is shown in Figure 5. It is evident that in the comparative algorithms, there are instances where land is misidentified as sea targets or sea targets are misidentified as land, leading to some false detections. In contrast, the detection results from our proposed model are almost consistent with the actual targets displayed.
## 2 Materials and Methods
### Laboratory Setup
#### 2.1.3 CompactRIO 9075  
The CompactRIO 9075 integrated system consists of a real-time processor and a reconfigurable FPGA (Field-Programmable Gate Array) on the same chassis, enabling control and monitoring capabilities, Figure 1. The CRO-9075 integrates a 400 MHz industrial real-time processor with an LX25 FPGA and includes four slots for NI C Series I/O modules.  
The modules used in the experimental setup are:  
* NI 9402 (LVTTL, bidirectional, 4-channel, 55 ns digital input-output module)--for measuring motor speed after the gearbox, based on the frequency of the pulse train from the optical encoder;
* NI 9403 (5 V/TTL, bidirectional, 32-channel, 7 us digital input-output module)--for controlling the activation of the corresponding relays;
* NI 9205 (32-channel, \(\pm\)200 mV to \(\pm\)10 V, 16-bit, 250 kS/s voltage input module)--for measuring the armature voltage of the motor and the armature current (by measuring the voltage across an additional resistor);
* NI 9263 (4-channel, \(\pm\)10 V, 16-bit voltage output module)--for supplying the control voltage to the amplifier.
###### Abstract  
By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.  
Internet of Things; named data networking; information centric networking; fog computing; smart environments; service orchestration +
Footnote †: journal:  
_Future Internet_2019, 11, 222; doi:10.3390/611110222
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 5 Discussion  
From the experimental results, it was found that a DM can be used to correct 55 eigenmodes' aberrations, and 69 residual Zernike mode aberrations are compensated through the LC. Without decoupling correction, only the fourth element of the sixth group can be distinguished. After decoupling correction, the fifth element of the fifth group becomes visible, and the image achieves diffraction-limited resolution. The resolution is 1.2 times better than the result obtained using the Zernike mode decomposition method presented in [13], indicating that the proposed method can be effectively used to correct aberrations in the 700-1700-nm waveband and achieve diffraction-limited resolution. The proposed method can simultaneously control the LC and the DM, which is better than the two-step method used in [12] and can cope with turbulence with faster changing speed.
## 3 Results
### The Analysis of Catechins and Caffeine Content Between Different Grades of TSH  
Catechins, as the primary non-volatile components in tea, are classified into ester-type catechins and non-ester catechins. Ref. [33] ester-type catechins are the main contributors to the bitter and astringent taste of tea. In this study, we identified and quantified five catechin compounds, including two ester-type catechins (EGCG and ECG) and three non-ester catechins (C, EC, and EGC) (Table 1). Notably, ester-type catechins accounted for nearly 80% of the total catechin content, with ECG levels higher than EGCG. As the grade of TSH decreased, the overall content of ester-type catechins showed a declining trend, and significant differences in ECG content were observed among different TSH grades. Among non-ester catechins, the content of C decreased with lower grades, while EGC increased. EC content generally decreased with lower grades but showed a slight increase in TSH-3 before declining again. Caffeine also contributes to the bitter taste of tea. The results revealed that caffeine content was highest in TSH-1 and lowest in TSH-2, with fluctuations across grades but no significant differences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound/mg/g** & **TSH-0** & **TSH-1** & **TSH-2** & **TSH-3** \\ \hline EGCG & 56.60 \(\pm\) 4.01 a & 47.81 \(\pm\) 1.13 b & 50.28 \(\pm\) 1.07 b & 49.99 \(\pm\) 0.80 b \\ ECG & 84.30 \(\pm\) 5.09 a & 68.56 \(\pm\) 2.13 b & 64.60 \(\pm\) 3.76 b & 58.14 \(\pm\) 2.45 c \\ C & 11.12 \(\pm\) 0.66 a & 10.95 \(\pm\) 0.25 a & 10.82 \(\pm\) 0.28 a & 10.52 \(\pm\) 0.40 a \\ EC & 8.47 \(\pm\) 0.42 a & 8.06 \(\pm\) 0.50 a & 8.39 \(\pm\) 0.69 a & 7.60 \(\pm\) 0.76 a \\ EGC & 3.49 \(\pm\) 0.20 b & 3.94 \(\pm\) 0.55 b & 5.97 \(\pm\) 0.65 a & 6.57 \(\pm\) 0.36 a \\ Caffeine & 27.90 \(\pm\) 1.45 a & 28.32 \(\pm\) 1.62 a & 25.38 \(\pm\) 0.63 a & 28.07 \(\pm\) 0.69 a \\ \hline \hline \end{tabular} Note: Different lowercase letters (a, b, and c) represent significant differences at \(p<\) 0.05.  
\end{table}
Table 1: Abundance (mg/g, DW) of catechin and caffeine in relation to different grades of TSH.  
Figure 1: Sensory evaluation of different grades of TSH. (**A**) The appearance characteristics of four grades of TSH samples and their corresponding tea infusions prepared according to national standard evaluation methods. (**B,C**) Radar charts illustrating the aroma and taste attributes of the four grades of TSH samples, respectively.  
In summary, the content of catechins, particularly ester-type catechins, varied significantly among different TSH grades. The esterified catechin content in TSH-0 was significantly higher than in other grades, aligning with the sensory evaluation results that higher-grade TSH exhibits a more robust and intense taste.
## 3 System Model  
This section discusses the designed sensor system and network based on low-power communication with data transmission. Here, the proposed model is classified into two modules. The first module includes node clustering and selection of the cluster with data transmission by adaptive clustering-based locative data transmission (ACLDT). The second module involves low-power communication using swarm intelligence integrated with multi-hop communication (SIMHC). The proposed model is represented in Figure 1.
# Interdependent Transcription of a Natural Sense/Antisense Transcripts Pair (_SLC34A1_/Pfn3)
## 1 Introduction
In particular, ADAR, MDA-5 and PKR have recently been shown to exert a protective function against endogenous dsRNA formed in the mitochondria and the nucleus from the transcription of repetitive elements [19; 20]. NATs-related dsRNAs have not been investigated in this context, but are unlikely to reach a level high enough in somatic cells to trigger an antiviral response. Similarly, endogenous siRNAs (Dicer products) have been sequenced and mapped without significant enrichment in sense/antisense complementary regions [21]. The situation is different in male germ cells, where NATs as well as endo-siRNAs were recently shown to be linked to loci that form dsRNA [16]. These findings are in line with reports of bi-directionally transcribedloci that give rise to endo-siRNAs in testis, one of which is the _SLC34A1/PFN3_ gene (Solute Carrier 34A1/Profilin 3) [22; 23; 24]. The _SLC34A1_ gene encodes an epithelial sodium-dependent phosphate transport system crucially involved in balancing whole body phosphate levels. It is expressed almost exclusively in renal proximal tubules, and the major hormones known to maintain phosphate homeostasis (parathyroid hormone and fibroblast growth factor 23) regulate its presence in the brush border membrane. These hormones acutely affect protein localization and stability rather than significantly impacting on transcription, which is stimulated in response to low phosphate levels [25]. The _PFN3_ gene is localized immediately downstream of _SLC34A1_ in opposite orientation, and alternative splicing generates a noncoding, overlapping antisense transcript (Section 2.1) [7]. The various facets of NATs, some emerging only recently, have shaped the perception of antisense RNA biology over time. In the early days, NATs were considered a family of transcripts on their own, with spatially restricted modes of action in conjunction with related sense transcripts and genes. Moreover, features were established that applied to NATs in general, such as expression levels, expression patterns, \(\mathcal{X}\)-chromosome bias, phylogenetic conservation and intron size [3; 26]. With the ever-increasing sequencing depth and emergence of thousands of long non-coding RNAs (lncRNAs) that share certain features of NATs, the general conception of antisense RNAs has shifted and they are now considered as lncRNAs, without taking the distinct features of NATs into account. We have studied NATs in a genomic context and _SLC34A_-related antisense regulation in various model systems. For example, we have investigated antisense transcripts at a genome level in cell lines (HEK293 cells), somatic tissues and germ cells [16; 21; 22]. Moreover, the generation of endo-siRNAs from _Slc34a_ sense/antisense transcripts was characterized in _Xenopus_ oocytes, though we could not detect _SLC34A_-derived short RNAs in HEK cells [21; 22]. Here, we investigate the interplay between the _SLC34A_ sense/antisense pair, including the epigenetic state of the locus, and extrapolate the findings to suggest common principles of gene regulation by NATs.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 4 Diamond-Based Electrodes  
Boron-doped diamond (BDD) electrodes are increasingly employed in bioanalytical applications because of their unique electrochemical properties, such as a low and stable baseline current [38] and wide potential window of water decomposition [39; 40]. Chemical stability is a key factor: while most of the commonly employed electrodes cannot be used in corrosive, acidic, or basic media, the chemical inertness of diamond electrodes allows for their use in every type of media [41]. Another benefit is the 'anti-fouling' behaviour, i.e., their poor adsorption of most types of organic molecules [42], which ensures a very good stability against the degradation of the electrochemical signal over time due to surface passive layer build-up [43; 44].  
Diamond-based electrodes are reported to detect a variety of biogenic amines, including neurotransmitters such as dopamine [13; 34; 35; 36; 47; 45], adrenalin [46], noradrenalin [46], serotonin [48], and histamine [48]. Detection of this class of molecules is typically based on their oxidation reactions, in which the alcohol moieties of the catechol are transformed into ketones. Electrodes described in this section are typically not functionalised for target-specific analysis, and their sensitivity is rather attained by finding the oxidation (or reduction) peak voltage of the desired analyte and then operating in amperometric mode; that is, fixing the voltage at the previously determined optimal value. Increased sensitivity can be achieved with simple coating procedures, such as using an oxidised polypyrrole polymer to repel anionic contaminants [35], or by reshaping the electrode surface with nano-grains to narrow the redox peak of a target analyte [13]. Specificity can be increased by coupling the electrode with a separation method such as capillary electrophoresis [46; 47] or flow injection analysis [45].
# Swarm Intelligence Techniques for Mobile Wireless Charging
## 5 MCs Coordination and Charging Traversal Strategies
### Coordination Strategy
Using this method, an MC can simply communicate with the sink, ignore nodes that have already been chosen by other MCs, and inform the sink of the energy status of the node it has selected. The global knowledge coordination strategy is expected to outperform all other strategies exploiting local knowledge, since the assumption of global network knowledge further extends the MC's coordinating abilities. However, this strategy would be unrealistic and would not be feasible for real LS-WSNs, as it introduces a high communication overhead whereby every node has to propagate their energy status to the MC, and nodes and MCs have to communicate their status over large distances. This does not scale well with LS-WSNs. Thus, the global knowledge charger studied presents an online solution that minimizes, in each round, the product of each node's energy and its distance from the present position of the MC. Simply put, for each moving step, the global charger reduces the following [22]: \(\min\left\{\left(1+\frac{E_{\text{curr}}}{E_{\text{init}}}\right)\cdot\left(1 +\frac{d_{curr}}{2R}\right)\right\}\) where \(E_{\text{curr}}\), \(E_{\text{init}}\), and \(d_{curr}\) are the current energy, initial energy, and current transmission distances of each sensor, respectively.
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 2 Materials and Methods
### Feature Extraction  
The theoretical background of the MFCC [35] and LPC [35; 36] feature parameters is briefly described in this section. The MFCC approach is the most widely known feature extraction method for automated VPD systems. Its steps consist of preprocessing, fast Fourier transform, mel filtering, log power determination, and discrete cosine transform (DCT). Firstly, the voice signal is pre-emphasized, framed, and windowed. Then, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Imbalanced Class** & **Balanced Class** \\ \hline Number of normal voices & 687 & 1354 \\ Number of pathological voices & 1354 & 1354 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples in the experimental dataset.  
magnitude spectrum is extracted from a short-time Fourier analysis. The magnitude spectrum is obtained into a mel spectrum with an equal center frequency distribution using 24 overlapping triangular windows for the mel scale windows [24]. The square of the mel spectrum--that is, the log power of each filter bank output--is calculated [24]. Finally, the 20 th-order MFCCs are extracted via the log power by applying the DCT [35; 36].  
Vocal tract information of a given speech is successfully extracted through linear predictive analysis. That is, LPCs represent source behaviors that are periodic and steady. Linear prediction (LP) is the analysis approach most used in speech signal processing. LP is based on the theory that a speech sample can be approximated as a linear combination of previous samples [35]. Then, a unique set of prediction coefficients is established by minimizing the sum of the squared differences between the actual speech and the linearly predicted samples during a finite interval [35; 36]. Two independent components of LP analysis, vocal tract parameters and a glottal excitation, are obtained. They are referred to as LP coefficients and LP residuals, respectively. It is assumed that speech is generated by the excitation of an impulse train and random noise for voiced and unvoiced speech, respectively [36; 37]. In this research, the 20 th MFCC and LPC parameters for each speech frame (window size = 40 ms; frame overlapping rate = 50%) are computed during the feature extraction stage [38].
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 6 Conclusions  
This study has demonstrated the feasibility of using virtual environments to train posture recognition models for elderly people. The results show that while the YOLOv9 model trained solely on virtual data performs well in virtual environments, its real-world performance is severely limited due to the appearance gap problem. However, by including a small number of natural images in the training process (hybrid dataset), the performance of the model in real-world scenarios is significantly improved. According to a very recent survey [48], compared to our closest deep learning competitors in vision-based human activity recognition, our approach aligns well with their classification results in terms of accuracy.  
Future work will focus on further investigating the proportion of real-world images to be used in the training set to improve the robustness of the YOLOv9 model. In addition, we plan to explore content domain adaptation techniques to better match the characteristics of virtual and real data, thereby improving the generalisability of the YOLOv9 model across different environments. Expanding the dataset and using more datasets to include a wider variety of postures and real-world scenarios will be crucial for developing a comprehensive posture recognition model. Finally, a comparison of our proposed model with different YOLO models, such as YOLOv8, YOLOv7, YOLOX, and YOLOR, will demonstrate which is the most suitable for human posture recognition among the YOLO family.  
Conceptualisation, R.M. and L.M.B.; methodology, A.B. and L.M.B.; software, A.B.; validation, R.M., A.P. and A.F.-C.; formal analysis, A.B. and L.M.B.; writing--original draft preparation, A.B. and L.M.B.; writing--review and editing, A.F.-C.; supervision, R.M.; funding acquisition, A.P. and A.F.-C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grant PID2020-115220 RB-C21, funded by the Spanish MICIU/AEI/10.13039/501100011033 and \"ERDF: A Way to Make Europe\". This research was also supported by CIBERSAM, Instituto de Salud Carlos III, MICIU, and \"ERDF: A Way to Make Europe\". Additionally, grant 2022-GRIN-34436 was funded by Universidad de Castilla-La Mancha and \"ERDF: A Way to Make Europe\". This work was partially supported by national funds through the Portuguese Foundation for Science and Technology (FCT), I.P., under the project UIDB/04524/2020. It was also partially supported by Junta de Comunidades de Castilla-La Mancha/ESF (grant No. SBPLY/21/180501/000030).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
DNNDeep Neural Network  
YOLOYou Only Look Once