esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Optimization Criteria  
Setting the correct optimization criteria or objectives for a scheduling problem is not always an easy task as they are diverse, convoluted, and often conflicting. Plenty of scheduling problems have been studied considering several criteria. The most considered are makespan (\(C_{max}\)), total flow time, total tardiness, maximum tardiness, and number of tardy jobs. Makespan and total flow time seek the effective utilization of the manufacturing resources by reducing the elapsed time between the start and the completion of a sequence of operations in a set of machines, while the remaining criteria are related to job due dates. In fact, makespan minimization is significantly important in order to upsurge the utilization of the production system. However, in today's competitive environment, focusing on makespan minimization without meeting the due date is of no use for an industry since meeting customer deadlines is crucial. According to [PERSON] and [PERSON] [32], when a task is not completed before its due date, some penalties are incurred, such as potential loss of customers, damaged reputation, loss of market competitiveness, penalty clauses if there are any, as well as expediting (the job is assigned quickly to the processing machine at the possible cost of extra setups, double handling of material, inefficient use of workmenand machine), etc. Hence, scheduling problems with tardiness objectives have attracted increasing attention from managers and researchers. Table 1 provides a summary of several significant studies that focus on the tardiness objective. The \"Constraints\" column contains the various constraints that were taken into account in these studies. The \"/ m\" column refers to parallel machines, the \"\(ST_{sd}\)\" column pertains to sequence-dependent setups, the \"\(d_{i}\)\" column represents due date constraints, the \"\(w_{i}\)\" column represents waiting time, and finally the \"\(r_{i}\)\" column represents release date constraints.
# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
## 3 CNN and LSTM for Sequence Recognition
### Long Short-Term Memory Neural Networks  
Long Short-Term Memory Neural Networks (LSTM) are a special kind of recurrent neural network (RNN), specialized in processing sequential data. LSTMs have recurrent connections, allowing the learning of long-term dependencies and, consequently, remembering information for long periods of time. The LSTM network core is based on a special unit known as the memory block [35], which is controlled by three structures (or gates): the forget gate \(f_{t}\), input gate \(i_{t}\), and output gate \(O_{t}\).  
Figure 1 shows a basic memory block.  
Each memory block has two sources of information at each unit time \(t\), the current sample \(x_{t}\), and the previous memory block state \(h_{t-1}\). This information is processed by the forget gate (a sigmoid layer) to decide what information is ignored from the previous cell state \(C_{t-1}\). The next step is deciding which new information will be stored in the current cell state. This process is performed at the input gate using the previous state and new data.  
Subsequently, the information passes through two functions: the sigmoid activation function \(i_{t}\) and the \(tanh\) function \(\overset{\sim}{C_{t}}\). These two partial results are next multiplied. Currently,  
Figure 1: The memory cell and the gates of an LSTM memory block.  
the previous state \(C_{t-1}\) is updated to a new state or cell state \(C_{t}\). Finally, the output of the LSTM network \(h_{t}\) is composed of the product of the output gate \(O_{t}\) and the cell state \(C_{t}\) that passes through the _tanh_ activation function. The overall process in a memory block is summarized as follows:  
\[\begin{array}{rcl}i_{t}&=&\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_{i} ),\\ f_{t}&=&\sigma(W_{xf}x_{t}+W_{h}f_{h-1}+W_{cf}C_{t-1}+b_{f}),\\ C_{t}&=&f_{t}C_{t-1}+i_{t}\widetilde{C}_{t},\\ O_{t}&=&\sigma(W_{x0}x_{t}+W_{h0}h_{t-1}+W_{c0}C_{t-1}+b_{O}),\\ h_{t}&=&O_{t}\tanh(C_{t}),\end{array} \tag{4}\]  
where \(\widetilde{C}_{t}=\tanh(W_{x0}x_{t}+W_{hC}h_{t-1}+b_{C})\), \(W_{\{\}}\), and \(b_{\{\}}\) denote the weight matrices and bias terms, respectively.
# Fire Behavior of Wood-Glass and Jute-Glass Hybrid Laminates Manufactured by Vacuum Infusion
## 2 Materials and Methods
### Characterization  
The fire behavior of hybrid laminates and pure resin was assessed using a NOSELAB ATS s.r.l. (Bovisio-Masciaco, Italy) cone calorimeter apparatus in accordance with ISO 5660. Compared to the ISO 5660 standard, the sample size was adapted to dimensions of 50 \(\times\) 50 \(\times\) 2-4 mm\({}^{3}\). These samples were securely positioned within an aluminum sample holder and exposed to irradiation at 35 kW/m\({}^{2}\) using a horizontal configuration setup.  
The cone tests were conducted three times for each laminate, and standard deviation values were calculated for the following parameters: ignition time (TTI, s), peak heat release rate (pHRR, kW/m\({}^{2}\)), time to peak (TTP, s), total heat release (THR, MJ/m\({}^{2}\)), total smoke release (TSR, m\({}^{2}\)/m\({}^{2}\)), optical transmittance percentage (%), and carbon monoxide (%) and carbon dioxide (%) presence in the exhaust air flow.  
The UL 94 test standard is a globally accepted method to assess the flammability of plastic materials. It evaluates how materials react to small flame sources, measuring aspects like flame spread and dripping. Crucial for industries like electronics and construction, this standard ensures product safety by guiding material selection based on fire behavior. Our study, following the UL 94 HB test for horizontal burning, provides insights into the burning rate of laminate samples, aiding in understanding their fire performance. Horizontal burning tests on 125 \(\times\) 13 \(\times\) 2-4 mm\({}^{3}\) samples were carried out in accordance with UL 94 HB in a NOSELAB ATS s.r.l.(Bovisio-Masciaco; Italy) cabin to obtain the burning rate (V). One sample per group was evaluated for this test. Before the flammability and combustion tests, the samples were conditioned in a climate chamber at 23 \(\pm\) 1 \({}^{\circ}\)C and 50% relative humidity for a minimum of 48 h, as suggested by standard ISO 5660.
###### Abstract  
Various formulations for electroless deposition, to obtain continuous nanometre-sized and micrometre-sized films of palladium on copper, were compared. We deposited ultrathin films using displacement plating formulations. We obtained continuous films with an equivalent thickness between 6 and 22 nm, measured by exploiting the K-ratio method with SEM-EDS of Pd layers. The Pd films obtained in this step of the work represent a cost-effective catalytic substrate. As a second step, we selected chemical plating as the procedure to obtain palladium films with a thickness in the micrometre range. An ammonia-based Pd chemical plating bath represent one of the most effective chemical plating formulations. To prevent copper substrates from being damaged by ammonia, displacement plating with palladium was also applied as a pre-treatment to make the use of these plating baths a viable way to obtain thicker palladium coatings. Palladium films showing good adherence, compact morphology, and a thickness over 1.5 \(\upmu\)m were obtained, proving that the combination of two different electroless techniques was the key to develop a sustainable procedure for micrometre-sized palladium coatings, which could substitute electroplating of Pd in galvanic industry for decorative applications.  
palladium; electroless deposition; thin films; sustainable catalysts +
Footnote †: journal: Applied Sciences
## 8 Conclusions  
AHLs serve as cell-to-cell signals both in the recognition of \"self\", i.e., intraspecies signaling, and recognition of other species, interspecies signaling. This situation suggests that AHL-mediated signaling is involved in complex, dynamic signaling between multiple partners in its natural habitat--the rhizosphere. However, most of the research cited here has been limited to monocultures. Understanding AHL-signaling will require firstly a robust knowledge of the signaling mechanisms and secondly how these mechanisms perform in multi-species mixtures. Key to the perception and control of AHL signaling are the LuxR-like regulators, and this is apparent not only from studies on _S. meliloti_, but also the other rhizobia. Multiple studies have revealed that while several of the LuxR-like homologs play a pivotal role in the perception and production control of AHLs, others are regulated by AHLs but do not directly interact with AHLs as the activating ligand, and still others appear to have no relationship to AHLs. One possibility accounting for this situation is that some LuxR-like proteins perceive non-AHL signals from other species, e. g., plant hosts, and integrate these into the control of transcription. Thus, future research aimed at understanding signaling behavior in the rhizobia should include a focus on the LuxR-like proteins, their activating signals and their regulons. Also unknown is whether AHLs have an impact on survival independently of the LuxR-like proteins. One of the best known LuxR-like regulons is that of ExpR, which controls up to 9% of the transcriptome of _S. meliloti_. However, the vast majority of the regulated genes have no known function. So far, most of the research has been limited to EPS production and motility, which probably represents a small part of the ExpR regulon. Hence future research should also focus on the regulatory targets of LuxR-like proteins and their role during survival of free-living _S. meliloti_ in the rhizosphere and during symbiosis with the plant hosts.  
[PERSON], [PERSON], and [PERSON] wrote the paper.  
Work of researchers at EEZ (Spain) was supported by ERDF-co-financed grant BIO2013-42801-P from the Spanish Ministry of Economy and Competitiveness (MINECO). NCM was supported by an FPU fellowship from the Spanish Ministry for Education and Science. MM was supported by the LOEWE Excellence Initiative of the State of Hesse, Germany and the German Research Foundation Be 2121/6-2.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Dataset  
The dataset used for this research includes the Tomato Leaves dataset, which is publicly available in [23]. This dataset comprises over 20,000 images categorized into ten different diseases and a healthy class, as shown in Figure 2. The images were collected from two distinct environments: controlled lab settings and real-world, in-the-wild scenes. This diverse dataset provides a comprehensive representation of tomato plant conditions, making it suitable for training and evaluating plant disease detection models.  
Figure 1: On-site image processing for real-time tomato plant disease detection: the optimized CNN-RNN model deployed on IoT devices processes images locally, eliminating the need for cloud servers and enabling rapid feedback to user devices for timely intervention.  
Figure 2: Images of tomato leaves with the ten different diseases.  
The dataset, which includes 11 distinct classes, is split into training, validation, and test sets in a specific ratio of 70%, 20%, and 10%, respectively. This particular division ensures that each class is adequately represented across the three subsets, maintaining a balanced distribution of data for model training, validation, and evaluation. The training set, encompassing 70% of the dataset, serves as the foundation for model learning, allowing for it to discern intricate patterns and relationships within each class. Subsequently, the validation set, constituting 20% of the dataset, is instrumental in fine-tuning model hyperparameters and monitoring performance during training iterations, thereby mitigating the risk of overfitting and ensuring robust generalization capabilities across all classes. Finally, the test set, comprising 10% of the dataset, provides an impartial benchmark for objectively evaluating the model's performance on unseen data from all eleven classes, thus validating its efficacy and reliability across diverse real-world scenarios.  
The primary approach in this research involves a hybrid model combining a CNN and an RNN. The process begins with image preprocessing, which includes resizing images into a unified size and augmentation techniques such as rotation, brightness adjustment, and contrast enhancement to simulate real-life conditions and augment the dataset. Following preprocessing, the images are fed into the hybrid CNN-RNN model for classification. This model leverages a CNN for extracting high-level features from the images and an RNN for capturing temporal dependencies, ensuring accurate and efficient disease detection.
## 2 Materials and Methods
### Transmission System  
Industrial robots play a key role in automated manufacturing processes, performing a wide range of tasks-from material handling to assembly operations and precise machining [18]. A fundamental component of their design is robotic arms, which execute the required movements in space using powerful actuators combined with gear mechanisms. These gear systems, most commonly harmonic or planetary, ensure the conversion of speed and torque, enabling precise, smooth, and repeatable movements of end-effectors. Additionally, they contribute to increased mechanical efficiency and controlled motion dynamics, which are crucial for achieving high accuracy and reliability in robotic systems under demanding operating conditions. Proper selection and maintenance of these gear systems are therefore critical factors for the long-term functionality and performance of industrial robots.  
Therefore, attention has been focused primarily on the most commonly used types of gear mechanisms, specifically, the high-precision gearbox, which is based on a new reduction mechanism.  
Figure 2 shows the gear mechanism used in addressing the given issue, which represents a gear reducer for power transmission, integrating highly precise trochoidal gears and a radial-axial bearing into a single compact unit.  
This type of gear mechanism was chosen for the following reasons:  
* Backlash-free motion: Due to minimal backlash, the movements are precise and repeatable, which is essential for accurate operations in robotics.
* High rigidity and durability: The combination of a robust bearing and reducer allows for the transmission of high torques while maintaining stability and resistance to shocks, which is critical for applications like exoskeletons or robotic arms.
* Compact and lightweight solution: The compact size and low weight are advantageous for mobile robotic systems, where every gram counts.
* Versatile use in various fields, such as robotics, automation, engineering, navigation systems, industrial applications, and more.  
Figure 2: The gear mechanism used in the test rig.  
The utilized reduction gearbox operates with a gear ratio of i = 123, a nominal torque TR = 130 (Nm), a maximum starting torque Tacc = 260 (Nm), and a nominal input speed nR = 2000 (rpm).
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 4 Experimental Setup
### Baselines  
For intrinsic evaluations, we compare our proposed method to the following two baseline models.  
**Scratch--**This model does not utilize the knowledge from \(L_{1}\) and is trained from scratch using data from \(L_{2}\). To match the number of parameters with our proposed method, two additional encoder layers are added to this model.  
**Adapters--Instead of using ITL, Adapter modules are added as in [43] in the encoder layers. This setting is similar to MonoTrans [44], except that the entire model is post-trained without freezing to maximize the performance in \(L_{2}\).**  
In addition to the aforementioned baselines, we consider the following models as baselines for the extrinsic evaluations.  
**mBERT--The massively multilingual version of BERT, trained on the Wikipedia dump for the top 100 languages with the largest number of documents.**  
**XLM-R--Another massively multilingual language model, which is based on RoBERTa and trained on the cleaned Common Crawl dataset [53] with 295 billion tokens covering 100 languages.**  
**KoBERT--Publicly available monolingual BERT trained from scratch using Korean corpora [54]. The training corpora consists of Korean Wikipedia dump with 54 million tokens and Korean news dataset with 270 million tokens.**
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.3.3 Interaction Latency

Table 14: Results of the normality test for average interaction latency for each subject. an average of 12.1%) with the proposed method compared to the one without the proposed method. As a result of the experiment, we confirmed that the interaction latency in the 'With proposed method' case (1.3815 s) was further reduced by 12.1%, compared to the 'Without proposed method' case (1.5718 s), and through this, we examined that the proposed method is effective for reducing interaction latency.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 2 Background
### Rpl  
The Routing Protocol for Low-power and lossy networks (RPL) [10] is a gradient-based distance vector routing protocol for WSNs supporting diverse link layers. RPL uses a Destination-Oriented Directed Acyclic Graph (DODAG) as a routing topology. The DODAG is a directional graph in which all edges are directed at the root node. An RPL node could transmit packets towards the root node using the DODAG topology. In RPL, nodes generate and manage routing information using the RPL control messages such as the DODAG Information Object (DIO) and Destination Advertisement Object (DAO). The node in the DODAG periodically advertises the DIO messages, which include configuration attributes that all RPL nodes should have. The RPL node receiving the DIO message advertised by the other node participates in the DODAG. At this time, the RPL node selects the source node of the DIO message as the parent. When an RPL node participates in a DODAG, it transmits the DAO messages to its parent. The RPL node receiving a DAO message generates the downward routing information for the source node of the DAO message and then transmits the message to the parent. This DAO message transfer is repeated until the DAO message reaches the root node, which creates a downward path from the root node to the source node of the DAO message.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### The Response of Vegetation to Climate Change
#### 3.3.2 The Correlation Between Vegetation and Precipitation
In contrast, southern Xinjiang was characterized by arid deserts and lower precipitation, resulting in less vegetation improvement, highlighting the importance of water availability in controlling vegetation growth. Our study hypothesized that increased precipitation due to climate change enhanced water supply, favoring plant growth. However, in southern Xinjiang, variations in vegetation responded less to precipitation,  
Figure 9: The correlation between precipitation and different vegetation indicators (**a**) for NDVI, (**b**) for LAI, and (**c**) for GPP. suggesting that even with additional precipitation, water availability remained a limiting factor due to high evapotranspiration rates and the arid climate.
# Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application
## 4 Experimental Estimation of the ATT Material's Properties  
To achieve the proposed goal, material properties are essential for input data for FEM analyses. The mechanical properties were evaluated experimentally in the laboratory using a tensile test. In a tensile test, a test specimen is stretched, usually to rupture, with progressively increased uniaxial tensile loads to determine its resistance to force. The samples were made of the same material as ATT (15 CrMoV6).  
A 3D model of the sample with its basic dimensions is shown in Figure 3. For the experiment, flat samples with diameters of 12.5 mm, a measurement length of 50 mm, and a measurement width of 4.9 mm were prepared, as shown in Figure 3. Four samples are shown on the right side of the figure specimens for testing.  
The Young's Modulus (E) and Poisson's ratio (\(\upmu\)) are crucial parameters in FEM analysis; therefore, these material constants were calculated from the data obtained from the experiment, and the results are represented in Table 1. The constants were estimated based on well-known formulas [48]. These formulas define the relationship between tensile stress and axial strain (\(\upvarepsilon\)) in the linear elastic region of the material. From the changes in the dimensions of the specimen, the length (l\({}_{0}\)) in mm, and the length after the test (\(\upDelta\)) in mm, the strain was calculated [49] and the stress (\(\upsigma\)) was calculated using the force (F) in N measured during the experiment [48]. According to these calculations, the Young's Modulus (E) and Poisson ratio (\(\upmu\)) were calculated. All the calculated and experimentally estimated data are summarized in Table 1.  
Figure 3: Tensile-test-specimen description.  
The Young's Modulus estimated in Table 1 had a mean value of 212.086 GPa and the average value for the Poisson's ratio was 0.323.  
During the braking process, the brake assembly, especially the braking discs, can generate heat up to 350 \({}^{\circ}\)C due to friction. An aircraft makes 5-6 landings per day. The landing-gear compartment has no thermal isolation, which means that the braking system in flight is cooled down to minus 50 \({}^{\circ}\)C; the temperature during the landing reaches 250 \({}^{\circ}\)C, so the range covers 300 \({}^{\circ}\)C and is overcome in a few seconds. This steel alloy can be heated in a short time and mechanically loaded at the same time. This means that the braking assembly is mainly affected by the temperature during the landing, so it is necessary to consider the properties of the material with respect to the temperature for the specific braking phase. The Young's modulus vs. the temperature was plotted in MATLAB software for the particular alloy from which the ATT was made, and it is shown in Figure 4.  
The experimentally estimated material properties are used in the next part of the manuscript for the FEM calculations of the ATT stress in the ANSYS software.
# Therapeutic Applications of Halloysite
## 4 Recent Trends in HNT Research
### HNTs and 3D Printing  
HNTs and 3D printing have also supported tissue regeneration of bone defects. For example, [PERSON] et al. [81] used PLA and gentamicin-doped HNTs to print a range of medical devices. For example, [PERSON] et al. [82] used doped HNTs and PLA to 3D print customized bioactive and absorbable surgical screws, pins, and bone plates for localized drug delivery.  
HNTs and 3D printing have also been used to support tissue regeneration of bone defects. Illustration, [PERSON] et al. [83] in 2019, used HNTs for coating on 3D printed scaffolds to guide human mesenchymal stem cells (hMSCs) orientation. Polylactic acid scaffolds consisting of different patterns were printed and functionalized by a polydopamine. Using field emission scanning electron microscopy (FE-SEM), the researcher found HNTs successfully coated on various patterns. Because of HNT addition, the scaffolds' surface roughness and hydrophilicity properties improved, and scaffolds could induce cell orientation. Moreover, the adhesion and proliferation of cells increased because of this supportive cell coating (Figure 5).  
Figure 4: SEM image of pure HNT (**a**), surface of HNT/PVA (**b**), cross section of HNT/PVA (**c**), surface of HNT/PVA/MC, Schematic of drug loading, surface modification, and synthesis of HNT/PVA nanocomposites (**d–f**). Reprinted by permission of the publisher.