esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Hybrid DRLCC Framework
#### 3.2.1 Module 1: State Extraction  
Module 1 performs two tasks: it keeps an in-memory digital twin (DT) database that mirrors every job and Autonomous Mobile Robot (AMR) on the shop floor, and compresses this rich state into a fixed-length vector consumed by the PPO agent.  
1. Digital Twin database: Two object classes are maintained: * Job (job_id, priority {0, 1, 2}, task_list, arrival_time, load_weight, feasible_amrs); * AMR (amr_id, capacity, battery_level (0-1), charging_flag, current_job, dynamic_ceiling). SimPy processes create and update these objects: a job arrival process appends job instances, battery and charging processes update AMR attributes, and task handlers bind jobs to robots.
2. Context extraction: At every decision epoch, the helper function get_state (env, amrs, job_queue) builds a vector of length 4N + 8 (here 4 is for the number of AMRs, which can be changed if the number of AMRs increases): * AMR features (per robot): busy flag, priority of current job (-1 if idle), raw battery level (0.0-1.0), current PCP ceiling; * Queue statistics: queue length 1Q, counts of jobs at priorities 0-2, wait-time statistics: * Mean \(\overline{\omega}=\frac{1}{|Q|}\sum(t_{now}-t_{arrival})\) ; * Maximum delay \(\max(t_{now}-t_{arrival})\) ; * Standard deviation \(\sigma_{w}\); * Mean priority \(\overline{p}\) of waiting for jobs.
* Normalization: The concatenated array is z-scored (mean 0, std 1) before being passed to the agent. Figure 4 represents the whole architecture of our proposed method, DRLCC.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 6 Potential Privacy Issues with Power Profiling
### Socioeconomic Inferences and Profiling Risks  
Energy consumption data can also be used to infer social and economic status based on appliance usage patterns. Households with higher energy consumption may be associated with wealthier residents, while frequent usage of certain appliances can indicate lifestyle choices (e.g., cooking habits, entertainment preferences, or work-from-home routines) [74]. Moreover, power load profiling can inadvertently reveal demographic details such as household size, building type (e.g., condo vs. single-family home), and even income levels [78]. If improperly handled, this information could be exploited for targeted advertising, discriminatory pricing, or surveillance by third-party entities.
## 4 Simulation and Experimental Results and Discussion
### Simulation and Experimental Environment Tools  
The analysis environment for the physical model and the plotting tools in this study was implemented using Python [74]. The numerical foundation for the data-frame structure and computations utilized the NumPy library [75]. Further analysis was conducted using the Pandas [76] and SciPy libraries [77]. The NetCDF4 library facilitated operations such as learning, reading, writing, and modifying the multidimensional NetCDF output from the WRF. Experimental visualizations were generated using plots, graphs, and charts derived from the Matplotlib library [78]. Machine learning models were constructed using the Keras framework using TensorFlow and the Sklearn library for classification and preprocessing [79; 80]. Statistical tests were performed using SciPy libraries, JMP, and Excel.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 6 Experimental Results
### RQ3: Impact of Different Components
#### 6.3.3 Triple Sampling Method in Triplet Learning  
We observe a significant improvement in the effectiveness of triplet learning when using distance-weighted sampling in Table 6. We attributed this improvement to the fact that distance-weighted sampling allowed the model to select samples with varying degrees of similarity based on the distances between them. This included selecting challenging samples (those with high similarity between positive and negative instances), as well as normal samples (those with low similarity). The selection of these samples was crucial for model training as it enhanced the model's discriminative capabilities.  
In Table 6, we present the impact of the triplet sampling methods in triplet learning. For the triplet learning strategy used in TCCCD, we conducted experiments with two different sampling methods: random sampling and distance-weighted sampling. We compared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F1** \\ \hline Random sampling & 0.94 & **0.92** & 0.92 \\ Distance-weighted sampling & **0.96** & 0.91 & **0.93** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Triple sampling method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F1** \\ \hline Without fine-tuning & 0.50 & **1.00** & 0.67 \\ Fine-tuning & **0.85** & 0.95 & **0.90** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fine-tuning of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F1** \\ \hline MSE & 0.85 & **0.95** & 0.90 \\ Contrastive learning & 0.94 & 0.91 & 0.92 \\ Triplet learning & **0.96** & 0.91 & **0.93** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distance metric learning.  
their respective effects on the model's effectiveness. In TCCCD, the distance-weighted sampling method was more effective in identifying code pairs as triplets, outperforming the random sampling method in terms of the precision and F1-score.
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 4 Result and Discussion
### Reason for MobileNet Outperforming Other Models  
Numerous elements that worked together to make the MobileNet model in this study perform better than other models were responsible for its success in detecting tiredness from facial cues. First, MobileNet is especially well-suited for image-based applications like facial analysis because it is excellent in learning hierarchical and spatial properties from images. The CNN and VGG16, despite being powerful architectures, are not able to capture these complex facial patterns as well. Second, the numerous convolutional and pooling layers and parameters present in the MobileNet model allow for the extraction of complex characteristics at different levels of abstraction. Due to the fact that the CNN and VGG16's architectures are comparatively shallower, MobileNet may detect small drowsiness-related facial cues such drooping eyelids and subtle changes in facial muscle tone.  
Moreover, the diversity and quantity of the training dataset can also be credited for the effectiveness of the MobileNet model. The model was trained on a large dataset that included a variety of drowsy-related facial expressions and changes, which helped it generalize more effectively to new data. Due to their limited parameter sets and potential lack of exposure to a variety of facial expressions during training, the CNN and VGG16 might have had trouble capturing these subtleties. Additionally, the convolutional and pooling layers of the MobileNet model's design integrate built-in weight sharing and feature reuse. This characteristic enables MobileNets to learn and recognize spatial patterns across several facial regions, which is essential for reliable sleepiness detection.  
The MobileNet model's performance can be illustrated by its capacity to pick up complex spatial information, as well as depth and exposure, in a variety of training datasets. Due to its superior ability to recognize tiny drowsiness-related facial cues, MobileNet is the best option for this crucial role of driver safety. MobileNet stand out as the best option for drowsiness detection from facial features in this study, underscoring the significance of choosing an architecture that is matched to the particular requirements of a task.
# Space Weather Effects on Satellites
## 4 Results
### A Comparative Analyses on the Starlink Failure Event  
Among the set of events from Table 2, the case of Starlink failure coincides with a preceding SF. Despite the weak flare strength in SXRs (C1.1 class ending one day prior the launch), the terrestrial ionosphere could still be disturbed due to an increased UV emission. Such preconditioning coincides with the arrival of an IP disturbance with southward \(B_{z}\), the development of dual minor-to-medium GSs, and a subsequent flux of energetic electrons (but not protons). No other events in Tables 2 and 3 have such a set of SW phenomena. The visual inspection of the Dst profiles of the other 14 GSs also singles out the SpaceX storm. Due to the limited event sample, we could only speculate whether a spacecraft launch that occurs between GSs (and accompanied by an ICME, IP shock and negative \(B_{z}\) beyond certain threshold) in addition to an SF and particle radiation component is the right mix of preconditions that could lead again to a spacecraft failure in the future. Increased atmospheric drag and compromised electronics on board due to radiation exposure by increased particle precipitation could be an additional cause for the failure.
## 5 Simulation
### Simulation Results and Analysis
#### 5.2.3 Robustness Experimental Research  
Experiments were conducted to evaluate the robustness of the stabilized platform control system under variations in rotational inertia \(J\) armature resistance \(R_{a}\), and external disturbance \(F_{n}\). The simulation results are presented in Figure 10, and Table 3 shows the maximum error of the control system for the stabilized platform, as shown in Figure 10.  
Figure 10: _Cont._  
Figure 9: (**a**) Tool face tracking response of control system with frictional disturbances and (**b**) tool face tracking error of control system with frictional disturbances.  
The results presented in Figure 10 and Table 5 provide a clear conclusion. Even in the presence of parameter variations, both the PID and DDPG control methods demonstrate the capability to achieve a certain level of tracking precision. However, it is noteworthy that PID control exhibits a significant increase in error, leading to a deviation from the desired accuracy in tool face angle tracking. In contrast, DDPG control, while displaying lower error rates compared to PID, faces challenges related to latency. Remarkably, the DDPG_DOB algorithm displays superior control effectiveness, highlighting its ability to mitigate parameter variations, reduce the impact of frictional disturbances, and demonstrate robust and resilient performance. Consequently, the DDPG_DOB method proposed in this study emerges as a more suitable choice for the control system of a rotary directional drilling stabilizing platform.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion  
### Tpc-Tfc  
The presence of total phenols and flavonoids in plants is an indicator to use as a tool for solving several problems starting from the human body to industrial sectors. Polyphenols are the key ingredient for both antioxidant and pro-oxidant reactions [33; 34]. For many years, the TPC and TFC of any plant have been approved as a benchmark for analyzing plant components for other economically beneficial roles. _C. tagal_ plant extract had 101.52 and 35.71 mg/g of TPC and TFC (Figure 2), which further inspired checking the antioxidant and anticorrosive efficiency, as plants with significant amounts of TPC and TFC are excellent sources for inhibiting free radicals, pathogen, and corrosion [35; 36; 37].  
The total phenol content and flavonoids, here, helps to gain a better understanding about various other crucial roles of _C. tagal._ In general, polyphenols from natural materials such as beverages, fruits, vegetables, and cereals are proven excellent sources for fighting against free radicals and diseases such as diabetes, neurodegenerative, and osteoporosis. These are compounds, either as intermediate or as precursor, are classified as phenols, flavonoids, and many more based on their structure and number of phenol rings [38]. In precise, phenols have a benzene ring attached to one or more hydroxyl groups, whereas flavonoids have two aromatic C6-C3-C6 ring structures assisting in specific and non-specific mechanisms with tremendous beneficial roles in human health [39].
## 3 The Proposed Approach
### Packet Size Evaluation and Comparisons
From Figure 3 it emerges that the most byte-expensive methods are PUT and POST and that, globally speaking and considering \(n\) equal to 2, the sizes of the CoAP packet are kept under the threshold of 2.4 kbytes. Basically, the packet size variations depend on the variation in size of the URI resource representation that occupies--except for the 4B header--the entire packet. While CoAP uses the GET, POST, PUT, and DELETE methods associated to a _resource_, the proposed model, instead, assumes the following procedures: _accessing_ a service (associable to a GET request), _subscribing_ to it (associable to a POST exchange), _modifying_ the way of delivery (associable to the PUT exchange) or _unsubscribing_ (DELETE method). Figure 4 shows a comparison between the logical exchanges in the two cases. To differentiate the SM packet, the _Code_ field is used, while the _Type_ field indicates a Confirmable packet in all of the cases shown in the figure. For each transaction, except for the _access_ case, the proposed model foresees a response that is a simple acknowledgement. Figure 3: Potential Maximum CoAP Packet sizes. In the _access_ case, the _sq_ flag is up in the request packet. The response is of the _acknowledgement plus data_ type and contains a payload. The payload may contain the sensed value (in case of _sensing_ service), a Boolean (in case of simple actuators), or the RFID tag content (in case of _identification_ service). In case of special smart objects, it may also contain more complex data according to vendor specifications but, in any case, the payload does not contain any descriptive text. To optimize the lower layer payload usage, it is desirable that the SM packet size fills the reserved space (81 bytes) in the IEEE 802.15.4 frame. The _Service ID_ code may be used as an index to check the output format of the devices (from manufacturer online-available registry). Anyway, if one considers that mandatory fields are 10 bytes-long according to the above mentioned specifications, then the manufacturers should try to limit the payload size (and hence the device returned values) up to 71 bytes. Table 2 shows the total number of IEEE 802.15.4 frames and bytes sent for each transaction according to the proposed solution. The calculation includes: (i) for each packet, the fields in Table 1 along with the 6 LoWPAN header; (ii) for each transaction, the request and the response; (iii) a 71 bytes payload for the _access_ case. In conclusion, from all the considerations in this section it emerges that the use of the Service ID code helps to considerably reduce the packet size because it identifies the constrained node's service without having to describe it (as the _URI resource representation_ does in the CoAP case). As a consequence, the fragmentation and rejection problems get solve for the case that have been illustrated in Section 2 (_large resource representation_). Figure 4: Matching among exchange methods.
## 2 Materials and Methods
### Deep Neural Network Models
#### 2.4.3 Recurrent Neural Networks  
Recurrent Neural Networks (RNNs) are deep learning models designed to handle sequential data, making them ideal for working with time-series data. In this context, inputs and outputs are interdependent and can influence the processing of subsequent data. RNNs include a recurrent unit that functions as a memory mechanism, enabling the network to learn from previous inputs. There are three primary types of RNN units available: Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), and Bilateral Long Short-Term Memory (BiLSTM).  
Two different RNNs have been built to solve the IK problem. Similar to the MLP case, the first of them consists simply of an input layer, one hidden layer, and an output layer. In this case, however, the hidden layer is not a fully connected (dense) layer but a recurrent layer. It consists of 12 fully connected neurons, where the output is to be fed back as the new input. An instance of this architecture can be seen in Figure 8. The output layer uses the tanh activation function. It will be referred to as SimpleRnn.  
Figure 7: CNN: Convolutional Deep Neural Network for learning Inverse Kinematics. It contains 2 convolutional layers, 1 pooling layer, and fully connected layers.  
The second proposed RNN is an LSTM network, built after the one presented in [12]. It consists of 5 LSTM layers and 6 fully connected hidden layers. To decrease the issue of over-fitting, a dropout of 0.2 rate is added after each fully connected layer. An instance of this architecture can be seen in Figure 9. The hidden layers use swish as their activation function and the output layer uses the linear activation function. It will be referred to as LSTM.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Runtime Consumer Interface Deployment
#### 3.8.1 Identification of Hardware Requirements  
The first step is the identification of the device type, which should include the computational power, memory, hardware type, security mechanism and operating system. This information can be used to determine whether the generation is feasible. Resource-constrained devices, for example, are less likely to execute new code unless they are adapted to their constraints. The device characteristics can also be used to determine if the performance will be sufficient to fulfill time constraints and execution requirements.
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 2 Antenna Configuration and Parametric Analysis  
For the proposed decoupling technique, the corresponding structure consists of two patch antennas with close spacing, the vertical decoupling wall with a single substrate, and a long slot in the antenna substrate to fix the relative vertical wall. After defining the antenna parameters, the parametric analyses for crucial variables are investigated.
# Durability of Superamphiphobic Polyester Fabrics in Simulated Aerodynamic Icing Conditions
## 3 Results and Discussion
### Flat Plate Configuration  
Given that the wetting properties measurement showed droplets were completely pinned to the fabric exposed to 120 m/s airflow on an airfoil, but not at 95 m/s, we wanted to study the transition between these speeds. The flat plate fabrics were therefore exposed to airspeeds between 90 and 115 m/s. The durability of the fabrics tested on a flat-plate are presented with respect to the airspeed of exposure. They were divided primarily by treatment (non-fluorinated or fluorinated), secondarily by temperature of exposure (+12.5 \({}^{\circ}\)C for warm cloud or \(-\)5 \({}^{\circ}\)C for icing), and tertiary in each chart by the angle of attack (20\({}^{\circ}\) or 45\({}^{\circ}\)).
## 1 Introduction  
As known, vortex light beams demonstrate some potential applications such as quantum communication [1; 2; 3; 4; 5], trapping and manipulation of particles [6; 7; 8; 9] and biomedicine [10; 11; 12], due to their unique orbital angular momentum [13; 14; 15; 16; 17]. In particular, the light fields of the Laguerre--Gaussian (LG) beams [18; 19; 20; 21] contain the key Laguerre polynomials [22; 23; 24; 25], which have a property of orthogonal normalization, so the LG beams can be used to form a complicated optical mode. Generally, any vortex beam can be considered as a linear combination of LG beams. Therefore, the research about LG beams becomes a hot topic and currently is mainly focused in the vortex beam characters. Considering the situation that the beam size will be widened gradually with the propagation distance, its application has still been greatly restricted.  
A kind of convex spiral phaser array for generating convergent LG beams based on spiral phase plate (SPP) [26; 27; 28; 29] is constructed by us. The fabrication of the micro-structures is completed using 3D printing technology [30; 31; 32; 33]. According to the surface topography measurement charts, it can be seen that the micro-structures present an ideal manufacturing accuracy and a needed integrity.  
According to the measurements of their surface roughness, the processing accuracy is already about 200 nm. During the optical measurements, three lasers with different central wavelength such as 450 nm, 530 nm and 650 nm, are used. The light intensity distribution corresponding to different lasers has following characters in common: (1) the convergent LG beams being successfully generated and then converged at a distance of about 380-400 \(\upmu\)m, which is already close to the focal plane expected at \(\sim\)370 \(\upmu\)m; (2) the light intensity distribution exhibiting a clockwise vortex orientation before the focal plane of CSPA and after the focal plane being changed to a counterclockwise vortex orientation, which means a vortex reversal corresponding to the focal plane of CSPA; (3) the light intensity being significantly low in the far field. The difference is that the number of the spiral lobes corresponding to different wavelength is variable but the same as the topological charge (TC) [34; 35; 36; 37; 38] corresponding to the light wavelength. It should be noted that the method of generation convergent LG beams achieved by us means a possibility for its efficient long-distance propagation.
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## 3 Results and Discussion
### Assimilation of Fructose  
Apple juice and its concentrate contained 8.08 \(\pm\) 0.42% and 8.33 \(\pm\) 0.26% of fructose before the start of fermentation, respectively. The amount of residual fructose in cider samples fermented under different conditions is shown in Figure 2. Based on the pattern of fructose consumption the yeasts used in the study can be divided into fructphobic and fructophilic (\(p<0.05\)). The similar behavior in yeasts has been previously shown by [PERSON] et al. (2019) [PERSON] et al. (2019).  
The yeasts left behind larger amounts of residual fructose in reconstituted concentrate (up to 1.29% residual fructose) in comparison to the fermentations performed with apple juice (up to 0.32% residual fructose). This observation correlates with the differences in fermentation kinetics shown previously (Figure 1). Fructophilic yeast (Y3, Y4) consumed the same amount of fructose regardless of temperature or applied nutritional supplementation. On the other hand, fructose assimilation by fructphobic yeasts (Y1, Y2, Y5, Y6) was slightly improved with nutrient addition, with Fermaid O being more effective than DAP (\(p<0.05\)). Higher fermentation temperature was also shown to have a stimulating effect on fructose assimilation by generally fructphobic yeast used in this study. However, it should be emphasized that this might not be the case for other yeast species. For example, low-temperature fermentations have been previously reported as preferred for non-_Saccharomyces_ yeasts ([PERSON] et al., 2019; [PERSON] et al., 2021).  
Figure 2: Residual fructose concentration at the end of fermentations prepared from fresh juice or juice concentrate with different yeasts (Y1-Y6) at 18 or 30 \({}^{\circ}\)C (\(p<0.05\)). The color green represents control samples, orange—supplemented with DAP (diammonium phosphate), purple—supplemented with Fermaid O. (**A**) represents the samples fermented at 18 \({}^{\circ}\)C; (**B**)—at 30 \({}^{\circ}\)C. Red lines represent initial malic acid content either in juice or reconstituted concentrate.