esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Artificial Neural Network (ANN)  
An artificial neural network (ANN) is composed of basic processing units called neurons which interact with one another [36]. These neurons process input data and derive logical outputs, passing information between neurons according to their weights, leading to an output similar to organic/biological brain networks. The learning phase of the ANN involves adjusting these weights to enhance overall performance [37]. The input layer receives raw data and then transmits it through the network to the output layer. There are many types of ANNs that exist, categorized by their learning algorithms and architectures, such as multi-layer perceptron (MLP) and feed-forward neural networks. In this study, an ANN model with an MLP architecture trained using the Levenberg-Marquardt backpropagation method was utilized, owing to its proven efficiency in hydrological forecasting [38].  
MLP is one of the most widely employed forms of ANN for various applications. MLPs typically include one input layer, one or more hidden layers, and one output layer [39].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & & **Inputs** & **Output** \\ \hline M01 & & & & W\({}_{\text{t-12}}\) & W\({}_{  
The topology of an ANN model, characterized by signals propagating layer by layer in a forward manner through the network, is concisely specified in Equation (1) and is illustrated in Figure 4 [40].  
\[y=\left[\sum_{j=1}^{m}w_{j}\Big{(}\sum_{i=1}^{N}w_{ji}+b_{j}\Big{)}+b\right] \tag{1}\]  
where  
* \(m\): Number of neurons in the hidden layer.
* \(N\): Number of samples in the input data.
* \(x_{i}\): The \(i\)th input variable at time step \(t\).
* \(w_{ji}\): Weight connecting the \(i\)th neuron in the input layer to the \(i\)th neuron in the hidden layer.
* \(b_{j}\): Bias term for the \(i\)th hidden neuron.
* \(\phi_{j}\): Activation function applied to the hidden neuron.
* \(w_{j}\): Weight connecting the \(i\)th neuron in the hidden layer to the \(k\)th neuron in the output layer.
* \(b\): Bias term for the \(k\)th output neuron.
* \(\phi\): Activation function applied to the output neuron.
* \(y\): The predicted \(k\)th output at time step \(t\).  
This equation describes the forward propagation process in an ANN, where the input data are transformed through weighted summations, biases, and activation functions at each layer. The hidden neurons compute intermediate representations of the input data, while the output neurons produce the final predicted value \((y)\) for a given time step.  
For additional foundational insights on ANN structures and functionality, refer to [PERSON] and [PERSON] [39].  
The calculations for the ANN in this study were performed utilizing MATLAB 2023a. The linear activation function was chosen for ANNs because of its superior performance compared to other functions, including logistic and sigmoid.  
Figure 4: The schematic of ANN architecture.
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
## 4 Conclusions and Outlook  
We investigated the impact of different front contact topography types on the optical effects of light-coupling and light-trapping in solar cells. As a well-established example, we demonstrate these effects in a-Si:H/\(\mu\)c-Si:H tandem solar cells. Here light-trapping and light-coupling are very important due to the low absorption of the NIR light and the high reflectivity of silicon with its high refractive index, respectively. The average feature diameters and roughness of the textured front contacts were characterized. Single textures with medium size features (700 nm-1000 nm) and roughness values above 80 nm yielded the best light-trapping. The back reflector interface texture plays a major role for light-trapping. Front contact features with small lateral or vertical size are effectively smoothed by the growth of the absorber layer while large lateral features of the order of absorber layer thickness become effectively flat without relevant light scattering. In contrast, smaller features (ca. 400 nm) with significant roughness around 100 nm enabled a good light-coupling thus. The maximum current density is generated from a solar cell when these two effects are combined in a single solar cell. Double textures made by a double etching step feature good light-trapping and simultaneously good light-coupling behavior. An optimized double-textured (DT) ZnO:Al yielded 12.5% initial efficiency in an a-Si:H/\(\mu\)c-Si:H solar cell with only a 1.64 \(\mu\)m total absorber layer thickness. The described effects on light-coupling and light-trapping based on interface textures and their evolution by absorber layer growth are of general interest and might influence optimization strategies in other photovoltaic technologies. The specificities of the absorption coefficient of the absorber material, layer thicknesses, and the individual growth processes have to be considered.  
Conceptualization, G.C.E.J. and J.H.; methodology, J.I.O., T.M. and T.Z.; formal analysis, investigation, visualization, and Writing--Original draft preparation, G.C.E.J.; Writing--Review and editing, supervision, data curation, and funding acquisition, J.H.  
This research was funded by BMU (contract No. 03327693A and 0325356B).  
The authors thank [PERSON], [PERSON], [PERSON] and [PERSON] for technical assistance.  
Figure 8: EQE and cell absorption (100—cell reflection) of single-textured (ST) HCl etched sample (blue lines) and double-textured (DT) HCl + subsequent HF-dip with improved light-coupling (red lines). The calculated top and bottom cell short-circuit current-densities [mA/cm\({}^{2}\)] are indicated in the graph. Best cell on each substrate shown respectively.
## 2 Materials and Methods
### Volatile Compounds Analysis
#### 2.2.2 HS-SPME/GC-MS  
The extraction was conducted by HS-SPME, the water bath was adjusted to 40 \({}^{\circ}\)C, and the vial was equilibrated for 5 min before inserting the SPME fibre, 50/30 \(\upmu\)m 2 cm DVB/CAR/PDMS (divinylbenzene/carbosen/polydimethylsiloxane) (StableFlex, purchased from Supelco). The fibre was exposed in the headspace to capture the free volatile compounds over 50 min. The analytes absorbed in the fibre were then separated and detected using a gas chromatograph HP6890 (Series GC System G1530A) coupled to an HP model 5973 mass selective detector (Agilent Technologies, Palo Alto, CA, USA). The analytes were desorbed in the GC injection port at 250 \({}^{\circ}\)C, and the separation was performed through a Carbowax column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). For the wines, the chromatographic ramp applied was based on [PERSON] et al. [18], starting at 40 \({}^{\circ}\)C for 5 min, ramped at 6 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and maintained for 10 min. The temperatures of the injector and transfer liner were maintained at 250 \({}^{\circ}\)C and the helium flow rate at 1.2 mL/min (analytical purity 99.999%, White Martins, Sao Paulo, SP, Brazil). The parameters for the mass spectrometer were set at 70 eV, 230 \({}^{\circ}\)C (EI source temperature) and 150 \({}^{\circ}\)C (quadrupole temperature). Mass spectra were acquired over \(m/z\) 30-350 using ChemStation software. The method described above was applied to the samples by evaluating the extraction conditions (bath temperature, equilibration time of the headspace, and time to capture the compounds) and, for GC-MS, the reproducibility and sensitivity parameters. A sample chromatogram is provided in the Supplementary Materials (Figure S1).
In our research, we intend to make the following contributions:  
* Propose an efficient waste collection system utilizing QR codes affixed to trash bins for real-time data on bin contents when scanned. * Optimize waste management team efficiency by streamlining collection routes, resulting in time and fuel savings while ensuring that waste collection trucks do not attend to bins that are not completely filled. * Enhance recycling through our framework by employing QR codes to identify food waste and recyclable/non-recyclable materials and then guide these materials to the designated processing facilities. This segregation guarantees that recyclable materials remain unadulterated, simplifying the recycling process, while food waste is directed for proper processing and reuse. * Empower waste management teams with the capability to monitor the volume and composition of waste gathered from diverse locations, enhancing the transparency of the entire system. These data can serve to pinpoint areas for enhancing waste reduction and recycling endeavors and quantifying the effectiveness of waste reduction initiatives. * Engage communities in waste management efforts by employing waste bins equipped with QR codes. We enhance community involvement by implementing rewards programs that incentivize recycling when participants provide information about recyclable and non-recyclable items. * Lower the waste management cost with our proposed system by minimizing the utilization of IoT sensors for each bin, optimizing collection routes, and diminishing the requirement for manual waste sorting. * Boost environmental sustainability by limiting IoT sensor use, significantly reducing e-waste and energy consumption. This lessens toxic landfill waste, lowers resource-intensive electronics production, cuts greenhouse gas emissions, and promotes a more sustainable future. The rest of this paper is structured as follows: Section 2 reviews the literature on the role of QR technology, IoT devices, people, and data in waste management. Section 3 provides an overview of the proposed system. Section 4 introduces our framework for trash collection and discusses several factors for successfully implementing the proposed framework. Finally, Section 5 concludes the paper and discusses future work.
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
## 2 Reviews of Related Works
### Software Defect Prediction  
SDP is one of the hottest topics of software engineering in two decades, because of its predictability. Test leaders or managers want to know the defect situation in a software under test (SUT) via SDP techniques before executing ST work. There has been many researchers study on SDP issues to improve prediction performances [5; 6; 7; 8; 9; 10; 11; 12; 13; 36; 37; 38]. According to different prediction tasks, SDP can be divided into binary classification tasks (such as [14; 37; 39; 40]), multi-classification tasks (such as, [41; 42]), ranking tasks (such as [5; 38; 43] ), and numeric tasks (such as [44; 45; 46]).  
For the issues in SDP, some research teams focus on handling defect data, such as feature selection [14; 47], instance selection [47; 48], and class imbalance processing [36; 49]. Some groups stated some novel algorithms [36; 50; 51], such as TCA+ [51], CCA [52], and TNB [50]. Some articles reported different machine learning algorithms and prediction models for binary classification tasks [37; 39; 40]. Several research scholars used historical numeric defect data to predict the defect numbers of every entity under test [44; 45; 46]. Additionally, paper [38; 43] paid attention to rank software entities to give testers some guidance for resource allocation.  
However, these techniques are aiming at predicting defect in fine-level (such as class, function, file) with static code information without execution data. For coarse-level prediction(such as system), SDP seems to be less effective.
# Assessment of Injury Risk in Professional Soccer Players: A Long-Term Study
## 2 Methods
### Running Demand Analysis  
Throughout all training sessions and official matches, the GPS device was positioned on the upper back of each soccer player. Each player used the same device during the research period. The reliability and validity of GPS systems have been reported [27; 28] and they have been used by soccer players during official matches [1; 25; 29; 30]. Total distance covered, high-speed running (14.4-19.8 km/h), very high-speed running (19.8-25.0 km/h), sprints (>25.0 km/h), and accelerations with decelerations (>2.5 m/s\({}^{2}\)) were all measured.  
The data analyzed consisted of 50 microcytes per complete one-week training block, per player and position, as outlined by [PERSON] [12]. Training days were coded as MD-6, MD-5, MD-4, MD-3, MD-2, and MD-1 within a weekly block, which concluded with the match day (MD). For a six-day microcycle, MD+1 represents recovery, MD+2 represents rest, while MD-4, MD-3, MD-2, and MD-1 are observed. In a seven-day microcycle, which includes an additional day before the official match, MD+1 is recovery, MD+2 is rest, and MD-5, MD-4, MD-3, MD-2, and MD-1 are followed. Longer blocks are also utilized. For instance, in an eight-day microcycle, MD+1 is recovery, MD+2 is rest, and MD-6, MD-5,MD-4, MD-3, MD-2, and MD-1 are incorporated, while a nine-day microcycle includes MD+1 for recovery, MD+2 for rest, and MD-7, MD-6, MD-5, MD-4, MD-3, MD-2, and MD-1. All athletes were thoroughly familiar with the experimental procedures. Data were only collected from soccer players who completed the full daily training regimen and participated in matches, while data from goalkeepers and players whose overall load was modified during the study were excluded to avoid potential fatigue or injury management issues, ensuring the study's validity and reliability. To calculate the initial value prior to the microcycle in which the player was injured, the ACWR used data from the previous four weeks, three weeks, two weeks, and the week of the injury. Initially computed as chronic workload, the immediate workload was associated with the fourth, third, second, and first weeks, respectively. The ACWR was then calculated by dividing the immediate workload by the chronic workload (i.e., the load accumulated during the injury week divided by the average load from the previous four, three, two, or one week). Values below 0.80 or above 1.3 were considered high-risk zones for injury [17]. This implies that, if the compared loads fall between 0.80 and 1.30, the injury risk is theoretically low, but if the value is below 0.80 or exceeds 1.30, the risk of non-contact injury is elevated.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 2 Related Works
### Redundant Encoding Strategies in Watermarking  
Robust watermarking often necessitates encoding strategies that introduce redundancy to ensure correct retrieval under channel degradation or intentional attacks. Several techniques are well-documented in the literature.  
QIM, as first introduced in [20], provides a mathematically grounded method for embedding bits into host features via quantization. QIM has been shown to achieve near-optimal performance in terms of robustness, imperceptibility, and payload, making it a widely used baseline in image and video watermarking [27]. Its adaptability and resilience have led to numerous variants, including combining QIM with transform-domain features (e.g., DCT, DWT) or motion vectors in video [24].  
Repetition codes represent a simple, yet effective strategy wherein watermark bits are duplicated across multiple blocks or frames, with retrieval based on majority voting [28]. While this improves robustness against localized corruption, it severely limits payload and may be ineffective in uniformly distributed attack scenarios.  
A more powerful class of encoding methods includes forward error correction (FEC) techniques such as BCH and Reed-Solomon codes. These allow structured redundancy and the correction of multiple errors without retransmission. BCH codes, in particular, have found success in both image and video watermarking systems subjected to compression or transmission distortion [11; 13]. For example, [PERSON] et al. [29] applied BCH to protect mobile video watermarking against heavy JPEG quantization, showing a substantial decrease in BER.  
In high-security scenarios, researchers have further explored hybrid coding schemes that combine QIM with FEC or chaotic modulation to boost tamper-resistance. These strategies, while often computationally heavier, have proven effective under strong desynchronization, geometric distortions, and heterogeneous transcoding [23; 30].  
Still, these schemes often assume a fixed or static embedding region, highlighting the need for perceptually guided block selection to complement the encoding scheme.
## 5 Evolution of HCI Mode
### Gui  
The graphical user interface (GUI) is a visible control system based on graphical symbols and other visual elements. Interacting with a surgical robot through a graphical user interface is the most common interaction method. The GUI can assist surgeons in forming an intuitive and realistic understanding of patient conditions preoperatively, thus improving preoperative planning efficiency. Currently, in related fields, the focus is on improving cognitive efficiency and user experience, focusing on the following three aspects:  
1. The integration and optimization of information content and the redesign of the existing interface: When designing the Sils robot, [PERSON] et al. integrated surgical information received by the surgeon, parameters of patient interest, machine status, and optional features into the GUI to assist surgeons in better interacting with the product [44].
2. The incorporation of display technologies such as AR and VR to change the way information is presented: [PERSON] et al. designed a 3D virtual interactive interface for ophthalmic microsurgery robots [45], enhancing visualization effects and aiding users in gaining better control over the product. The GUI designed by [PERSON] et al. based on virtual reality technology can assist surgeons in orthopedic preoperative planning, achieving virtual fracture reduction [46; 47].
3. The enhancement of image quality based on 3D reconstruction: Currently, relevant field researchers improve image quality through 3D modeling and algorithm optimization to aid surgeons in surgical planning. The medical 3D visualization software Mint Liver can help surgeons obtain more realistic, detailed, and 3D lesion information through 3D models [48]. [PERSON] et al. used 3D reconstruction for studies on type III hilar cholangiocarcinoma surgery, confirming the safety and effectiveness of related technologies [49]. [PERSON] et al. designed an endoscope specifically to provide stereoscopic views for laparoscopic surgery, building a visual anatomical environment through high-precision 3D reconstruction [50].  
However, such technology has certain limitations [51]. Its image effects can be affected by the original data and software used. When the object of reconstruction is original CT or MRI images, some data loss can decrease image clarity, causing difficulties in decision making for surgeons.
###### Abstract  
Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).  
Keywords:underwater wireless sensor networks; data forwarding; value of information; energy consumption; passive mobility; reinforcement learning +
Footnote †: journal: Sensors
## 2 Exposure
### Workplace Exposure Measurements  
A limitation on nanosafety development is the lack of information about the health of _already_ exposed and thus potentially affected populations. There was (and still is) no European system to register occupational health related to nanomaterial exposure. Consequently, occupational health reporting strategies need further exploration and harmonization [9,8]. The workplace is generally the best characterised exposure scenario due to the expected highest exposure probability and concentrations for humans. Still most of the studies conducted to date have been on general exposure or release. That is, no specific personal exposure measurements for nanoparticles at various workplaces, leading to a robust exposure assessment via inhalation or oral uptake, have been conducted so far [9,8].  
Dermal exposure to NMs has not been studied with the exception of [PERSON] _et al._[32] using a shortened version of the observational dermal exposure assessment (DREAM) to estimate the likelihood of exposure. However, the relevance of uptake via the intact skin has been demonstrated [33]. With the exception of the intended use of NMs in food, possible oral uptake following inhalation exposure has not been studied yet.  
All nano-exposure studies conducted so far are related to short-term exposures. No procedure of exposure monitoring or long term exposure assessment has been conducted to the knowledge of the authors. The current workplace investigations focus on areas where NMs are initially produced. Knowledge on use and processes with NMs in the secondary or later stages cannot currently be assessed due to lack of knowledge on their use. Labelling, which is also needed to identify possible exposure via consumer goods, is one way to address this safety research topic [34].  
Comparative assessments of different tasks and processes in the workplace should be based on an extensive data set generated in as harmonised a way as [35]. The data should come along with auxiliary contextual information that is required to interpret the measurement results for risk assessment and mediation purposes. The exposure scenarios are also needed to derive information on uptake for combined assessments of hazard and exposure potential.  
**Recommendation 4**: We recommend the early development and testing of personal devices delivering reliable results to reduce NMs exposure in the workplace. Focus should be set to personal real-time instruments that simulate uptake, e.g., deposition in the different areas of the respiratory tract.