esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Result Analysis
#### 4.4.3 Comparative Experiment  
In this experiment, three classic and commonly used time-series forecasting models are selected: LSTM, Transformer, and Preformer. These models have wide applications and show good performance in the field of time-series forecasting. To comprehensively evaluate the performance of the proposed STL-DCSInformer-ETS model, this study compares it with the three classic models. The aim is to conduct an in-depth analysis through experimental comparisons, exploring the accuracy and effectiveness of each model in handling time-series data.  
The Figure 8 shows the performance comparison of different models in the medium and long-term sales forecast of fast-moving products. The performance comparison of different models for medium- and long-term sales forecasting of fast-moving products is shown in Table 6. From the table, it is evident that the STL-DCSInformer-ETS model outperforms all other models across all evaluation metrics, demonstrating superior capability in capturing sales trends and fluctuations. Specifically, the MSE of the STL-DCSInformer-ETS model is 0.2092, and the Mean Absolute Error (MAE) is 0.2211, both significantly lower than other models. Additionally, its RMSE and MAPE are 0.4574 and 13.33%, respectively, highlighting its outstanding accuracy and reliability. Furthermore, the IoA score of 0.9313 emphasizes its exceptional consistency with the observed data. The Preformer model, as seen in the third row of Table 6, achieves competitive results, with an MSE of 0.2130, an MAE of 0.2350, and an RMSE of 0.4615. Its MAPE is 13.56%, and it attains an IoA of 0.9011. While the Preformer model demonstrates strong performance, it falls slightly behind STL-DCSInformer-ETS in all metrics, particularly in capturing subtle sales fluctuations. The Transformer model, as shown in the second row of Table 6, achieves moderate results with an MSE of 0.3265 and an MAE of 0.3182, indicating limited effectiveness in medium- and long-term forecasting tasks. As seen in the first row of Table 6, the LSTM model exhibits the weakest performance, with an MSE of 0.4449 and an MAE of 0.3835. This shows that compared with the advanced model, its ability to capture sales trends and fluctuations has great limitations.  
In summary, the STL-DCSInformer-ETS model demonstrates significant advantages in forecasting medium- and long-term sales trends and capturing complex seasonal variations.  
Figure 8: Comparison of ablation and comparative experiment prediction results.
## 2 Materials and Methods
### RNA Extraction, Library Construction, and Sequencing  
The cells were grown to an exponential phase (OD\({}_{600}\) = 0.5) and treated with ENR (sub-inhibitory concentration) over a period of 15 min. Total RNA extraction of _PmS_, _PmR_ and _PmHR_ was performed using an RNAiso Pure RNA Isolation Kit (TaKaRa, TaKaRa Biotechnology (Dalian) Co., Ltd., China), according to the manufacturer's instruction. The quality and quantity of RNA was assessed, and the library construction and sequencing were performed at Biomarker Technology (Beijing, China) using the Illumina HiSeq 2500 platform. The genome sequence and annotation information of _Pm_ were obtained from the NCBI database (Accession No. NC_014259.1). Quality-filtered reads were aligned to the reference genome sequence, using the CLC Genomics Workbench 4.0 (CLC Bio, Qiagen, Dusseldorf, _GER_).  
To identify DEGs (differential expression genes) among the three different samples, the expression level for each transcript was calculated using the fragments of RPKM (read per kilo base per million mapped reads) method. edgeR ([[https://bioconductor.org/packages/release/bioc/html/edgeR.html](https://bioconductor.org/packages/release/bioc/html/edgeR.html)]([https://bioconductor.org/packages/release/bioc/html/edgeR.html](https://bioconductor.org/packages/release/bioc/html/edgeR.html)), accessed on 11 November 2021) was used for differential expression analysis. The DEGs between the two samples were selected using the following criteria: (i) the logarithmic of fold-change should be greater than 2 and the false discovery rate (FDR) should be less than 0.05. To understand the functions of the differentially expressed gene, GO functional enrichment and KEGG pathway analyses were carried out by Goatools ([[https://github.com/tanghaibao/Goatools](https://github.com/tanghaibao/Goatools)]([https://github.com/tanghaibao/Goatools](https://github.com/tanghaibao/Goatools)), accessed on 27 November 2021) and KOBAS ([[http://kobas.cbi.pku.edu.cn/home.do](http://kobas.cbi.pku.edu.cn/home.do)]([http://kobas.cbi.pku.edu.cn/home.do](http://kobas.cbi.pku.edu.cn/home.do)), accessed on 27 November 2021), respectively. DEGs were significantly enriched in terms of GO and metabolic pathways when their Bonferroni-corrected p-value was less than 0.05. Then, with the increase in drug resistance, differentially expressed genes were screened.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 1 Introduction
(1) By studying knowledge distillation for training networks, a simple and effective CD module is presented in the paper, which aims to model channel correlation and remote dependency in order to improve student network segmentation accuracy. (2) An effective knowledge distillation architecture is provided for brain tumor segmentation. Experiments on the public dataset BraTS2018 showed the feasibility of the method.
# Cyber-Storms Cone from Clouds: Security of Cloud Computing in the IoT Era
## 3 Background: Cloud Computing Paradigm  
Nowadays, Cloud computing is a well-known paradigm. However, for the sake of readability and self-containment of the paper, we consider relevant to recap basic notions of Cloud computing. This also allows us to define a common terminology that is going to be used throughout the rest of this paper. For these reasons, background notions about Cloud computing are provided in this section.  
NIST [35] defines Cloud computing as \"a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction\".  
Figure 2 depicts the NIST Cloud computing reference architecture [36]. It provides a high-level overview of the Cloud and identifies the main actors and their role in Cloud computing. Each actor is an entity, i.e., a person or an organization, that either takes part in a transaction/process or performs some tasks in Cloud computing. There are five main actors:  
* _Cloud Provider_: an entity that provides a service to interested parties;
* _Cloud Consumer_: an entity that uses a service from, and has a business relationship with, one or more _Cloud providers_;
* _Cloud Broker_: an entity that mediates affairs between _Cloud providers_ and _Cloud consumers_, and that manages the use, performance, and delivery of Cloud services;
* _Cloud Carrier_: an intermediary that supplies connectivity and delivery of Cloud services from _Cloud providers_ to _Cloud consumers_;
* _Cloud Auditor_: a party that conducts independent assessments of the Cloud infrastructure, including services, information systems operations, performances, and security of the Cloud implementation.  
In terms of interactions, there are several possible scenarios [36]. Generally, a Cloud consumer may request a Cloud service from a Cloud provider, either directly or via a Cloud broker. A Cloud auditor conducts independent audits and may contact other actors to collect the necessary information.  
The NIST defines the Cloud by means of five essential characteristics, three service models, and four deployment models [35].
## 3 Methods
### In-Plane Heatwave Thermography
#### 3.1.1 Local Gradient Method  
The authors suggest extracting features like cracks or loose rivets from the recorded videos by analyzing the local gradient of the in-plane heat diffusion. The so-called local gradient method processes each image of the thermal video separately and extracts features from each frame independently. As a first step, a horizontal and vertical mean filter with a window size of three pixels is applied to every image \(I_{(x,y),t}\) at the time \(t\) containing the pixel values \(i_{x,y}\).  
\[s_{(x,y)}=\frac{i_{x-1,y}+i_{x,y}+i_{x+1,y}+i_{x,y-1}+i_{x,y+1}}{5}. \tag{1}\]  
From the filtered frame \(s_{(x,y)}\), the spatial gradient in \(x\)- and \(y\)-direction are computed using second order central differences approximation. Summing their absolute values results in a new frame \(g_{x,y}\) and video \(G_{(x,y),t}\), in which every pixel represents the local gradient at a specific time \(t\) in the original data.  
\[g_{(x,y)}=\frac{1}{2}\times\big{(}|s_{x-1,y}-s_{x+1,y}|+|s_{x,y-1}-s_{x,y+1}| \big{)}. \tag{2}\]  
The different steps of this calculation are shown in Figure 1 where one frame of a thermal video shows the rivet that has a lower temperature than the surrounding plate due to the thermal barrier caused by the gap between rivet and plate.  
By performing the calculations above for every frame in the original video, a new video \(G_{(x,y),t}\) is obtained with the local gradient results. To reduce the dimension of the data, one single image should be calculated from the video and is to be used for an assessment of loose rivets. For this, different methods like temporal averaging, variance or peak-finding can be used to transform each pixels temporal signal \(g_{(x,y),t}\) to a single value \(\hat{g}_{(x,y)}\)A comparison showed that the variance of the temporal pixel gradient results in easy to interpret and meaningful images.
## 2 NOMA System Model
### Superposition Coding (SC)  
NOMA uses SC to transmit the data; SC can encode a message of a low channel condition user at a low data rate, and then superimpose the message of a user with better channel conditions on it. SC is considered one of the essential structures in coding systems due to its ability to attain the capacity region of binary channels (BCs) and Gaussian binary channels [2; 11; 35].  
The BS transmits the superimposed message of \(N\) users after assigning the power coefficients as:  
\[s=\sum\
olimits_{i=1}^{N}\sqrt{p_{i}}\;x_{i}, \tag{1}\]  
where \(N\) is the total amount of user equipment (UE), \(p_{i}\) is the allocated power coefficient of user \(i\), \(x_{i}\) is the coded and modulated pilot data of user \(i\), and \(s\) is the superimposed transmitted signal.  
Initially, each UE is assigned a random power coefficient under the condition:  
\[\sum\
olimits_{i=1}^{N}p_{i}=1. \tag{2}\]  
Algorithm 1 provides fair power assignment in conventional PD-NOMA. In this algorithm, users with a low SNR are allocated more power (high power coefficients) than those with a high SNR (better channel conditions).  
```
1:Channel Matrices \((H_{1},H_{2},\ldots H_{N})\) for each UE from the stage of channel estimation (CE)
2:Loop I = 1: N
3:\(SNR_{i}=|H_{i}|^{2}\) (3)
4:End loop
5:Sort SNR values in ascending order
6: Assign the power coefficients according to the SNR values subject to \(\sum\
olimits_{i=1}^{N}p_{i}=1\) given by Equation (2).
7: Sort the power coefficients, \(p\), in descending order.
8: Update and retransmit the super-positioned sequences with the new power coefficients.
9:Power coefficients \((p_{1},p_{2},\ldots,p_{N})\) re-assigned for each user's transmitted message
```  
**Algorithm 1** SNR comparison and Power Re-Allocation for UEs  
The new power coefficients (fraction of total power) are assigned to the users after each stage of channel estimation, such that their summation is always equal to the total available power at the BS. In previous work, the power coefficients were adaptively chosen, for example, in [20; 36; 37].
## 7 Real World-Use Cases
### Clinical Research  
ShinyAnonymizer is currently helpful in clinical research in anonymizing patient data. Without risking patient privacy, researchers can incorporate electronic health records, use modern anonymization techniques, and conduct comprehensive analyses. The tool's ability to work with various forms of data ensures that it can be utilized for multiple experimental scenarios.
## 3 Results  
### Performance of NiO and the PTAA/NiO-Based PSCs  
To investigate the performance and stability of the perovskite device using the PTAA/NiO bilayer HTL, we fabricated a p-i-n device with the Ag/SnO\({}_{2}\)/C\({}_{60}\)/perovskite/PTAA/NiO/ITO structure shown in Figure 2. Specifically, Ag, SnO\({}_{2}\), C\({}_{60}\), perovskite, PTAA, NiO, and ITO were used as back metal, electron transport layer, C\({}_{80.175}\)FA\({}_{0.750}\)MA\({}_{0.075}\)Pb(\({}_{0.880}\)Br\({}_{0.120}\))\({}_{3}\) based photoactive layer, bilayer HTL, and front transparent electrode, respectively.  
Based on this structure, the outputs of PSCs and various electrical and optical performances were analyzed. The current-voltage (J-V) curves and external quantum efficiency (EQE) of the best NiO and PTAA/NiO bilayer-based PSCs are shown in Figure 3. As presented in Figure 3, the PTAA/NiO bilayer-based PSC significantly improved PCE from 17.29% to 19.05%, and V\({}_{\text{OC}}\), J\({}_{\text{SC}}\) and FF all increased from 1.10 V to 1.15 V, from 20.93 mA cm\({}^{-2}\) to 21.69 mA cm\({}^{-2}\), and from 74.73% to 76.47% %, respectively, as listed in Table 1. As shown in Figure 3, the optimized PTAA/NiO bilayer exhibited higher EQE values compared to NiO devices, which supports that devices with PTAA/NiO bilayer promote efficient charge extraction and higher Voc [40]. The device performance improvement was consistent with the J-V and EQE spectra of NiO and PTAA/NiO bilayer-based PSCs. The J\({}_{\text{SC}}\) calculated by integrating the EQE curves (20.30 mA cm\({}^{-2}\) for NiO-based and 21.08 mA cm\({}^{-2}\) for PTAA/NiO) showed a small deviation (3%) from the J\({}_{\text{SC}}\) obtained from the J-V curves, thus indicating that the measurements are reliable.  
Figure 4 summarizes the changes in all output parameters: PCE, fill factor (FF), open circuit voltage (V\({}_{\text{OC}}\)), and short circuit current (J\({}_{\text{SC}}\)) using corresponding representative J-V curves for devices manufactured with different HTLs. The PSCs that had the PTAA  
\begin{table}
\begin{tabular}{c c c c c} \hline
**HTL** & **V\({}_{\text{OC}}\) (V)** & **J\({}_{\text{SC}}\) (mA cm\({}^{-2}\))** & **Fill Factor (\%)** & **Efficiency (\%)** \\ \hline NiO & 1.10 & 20.93 & 74.73 & 17.29 \\ PTAA/NiO & 1.15 & 21.69 & 76.47 & 19.05 \\ \hline \end{tabular}
\end{table}
Table 1: Device performance of the NiO monolayer and PTAA/NiO bilayer-based PSCs under AM 1.5 G illumination at 100 mW cm\({}^{-2}\).  
Figure 2: Structural layout of PTAA/NiO-based PSCs.  
layer inserted performed significantly better than the device without the PTAA layer in all parameters. The PTAA/NiO bilayer-based PSCs showed higher output performances than NiO-based PSCs.
# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 3 Neural Network Theory
### BP Neural Network Theory
#### 3.2.3 Propagation Process  
(1) Forward propagation  
The information enters the network from the input layer and passes through each layer in turn and enters the process of obtaining the final output layer result.  
The calculation process mainly involves matrix multiplication, using the value of each layer multiplied by the corresponding weight + bias variable (activation function).  
From the input layer to the hidden layer:  
\[\alpha_{h}=\sum_{i=1}^{d}v_{ih}x_{i} \tag{1}\]  
The activation function through the hidden layer is:  
\[b_{h}=f(\alpha_{h}-\gamma_{h}) \tag{2}\]  
Among them, the weight between the first neuron of the input layer and the first neuron of the hidden layer is the threshold of the first neuron of the hidden layer.  
From the hidden layer to the output layer:  
\[\beta_{j}=\sum_{h=1}^{q}w_{hj}b_{h}+\theta_{j} \tag{3}\]  
Among them, the weight between the first neuron in the hidden layer and the first neuron in the output layer is the threshold of the first neuron in the output layer.  
In the machine learning model, the parameters we start with are random, so the calculation results may have a large error with the real values. In order to reduce this error, we need to adjust the parameters according to the error, and this process requires the back propagation algorithm of the model.  
(2) Backpropagation  
The core idea of the backpropagation algorithm is to calculate the error between the output layer and the expected value, then propagate the error back to each layer of the network, and then adjust the parameters in the network. In this process, each neuron adjusts its own weight and bias according to the error so that the output of the network is closer to the expected value. This process is an iterative process, which needs to be repeated until the error reaches the minimum or meets the preset stop condition. The error is usually measured by calculating the square sum of the difference between the true value and the neural network training result. Of course, other calculation methods can also be selected. This error value will be used to guide the parameter adjustment in the back propagation process so that the network can better fit the real data.  
Figure 3: Working process diagram of BP neural network.
# Investigating a Toolchain from Trajectory Recording to Resimulation
## 1 Introduction
A comparison of macroscopic properties of Radar-recorded and simulated trajectories within SUMO is presented in [21]. A similar approach is followed by [22] for an inner-city scenario and statistical simulations with SUMO. A comparison of SUMO and Aimsun is performed via statistical simulations for linear traffic in [23]. Resimulation of real-world scenarios based on screenplay without considering behavioral models is presented in [24]. This paper seeks to assess the realism of behavioral models in traffic flow simulation environments. To exemplify this investigation, the open-source simulation framework openPASS and its associated behavioral models are employed to compare real-world trajectories with simulated ones from the same scenario. Due to the ability to depict critical scenarios and accidents in the simulation environment, the open source nature of the software, and the involvement of leading OEMs and Tier 1 suppliers in the development, the openPASS environment was chosen to realize the simulations. Therefore, the setup of a semi-automatic toolchain is implemented from the recording of traffic scenarios and the classification using ML-based methods to the re-simulationusing the agent-based, microscopic traffic flow simulation openPASS [25] and its behavioral models. The recorded and simulated trajectories are compared, also considering VRUs. The term scenario is used synonymously with concrete scenarios, according to [PERSON] et al. [26]. In the work presented in this paper, we study traffic situations at a T-junction in Freiburg, Germany, involving cars and VRUs. The remainder of this paper is organized as follows: First, in Section 2, the T-junction scenario is described; the recorded data, the extraction of trajectories, and the traffic simulation setup are presented. In Section 3, the simulation results are compared with the recorded data. Further, in Section 4, the results are discussed with respect to limitations in the simulation approach. Finally, Section 5 summarizes the findings and provides a brief outlook.
## 2 Related Works
### Data Collection Schemes Based on Mobile Data Collector
#### 2.1.3 Mobile Data Collectors with Controlled Mobility  
In some WSNs data collection is performed only when events of interest occurs. In this case, controlled mobility is desired since it allows triggering mobile nodes on demand to visit sensors and avoiding sensor buffer overflows. For instance, in [9] the problem is proved to be NP-complete and a heuristic solution called Earliest Deadline First (EDF) and its two variants are presented. With the EDF solution, the next node to be visited by the mobile node is chosen as the one that has the earliest buffer overflow deadline.  
[PERSON] _et al._ proposed a scheme in [27], in which multiple resource-rich mobile cluster heads (CHs) are employed to prolong network lifetime and ensure delay requirements of real time applications are met. In this case, mobile cluster heads cooperatively collect data from different network segments and deliver it to a central Sink. All CHs move in a manner ensuring their connectivity with the base station while covering most interested areas at the same time. The authors propose three different movement strategies for CHs in order to reduce multi-hop communication and enhance network lifetime.  
Similarly, authors in [7] used different controlled mobility approaches to optimize the speed of mobile nodes while collecting data in a sensor network. The first approach is called stop and communicate because as the mobile node enters the communication range of a static node that has some data to send, it stops there until all buffered data has been collected. The duration of the stop depends on the data generation rate of the source node. The second way to optimize speed is called adaptive speed control in which the speed of mobile node is changed according to the number of encountered nodes and the percentage of collected data with respect to buffered messages. Different groups of nodes are made according to the amount of data collected from them (low, medium or high). The mobile node moves slowly in a group with a low collected data percentage, while it moves faster when it is in the communication range of nodes with a high percentage of collected data.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Machine Vision Systems for Bottling Quality Inspection
#### 4.3.4 Future Directions and Innovations  
The future of machine vision systems in the bottling process is promising, with ongoing advancements aimed at further enhancing their capabilities. Innovations such as 3D vision systems, hyperspectral imaging, and advanced machine learning algorithms are expected to improve defect detection accuracy and expand the range of detectable defects.  
For instance, 3D vision systems can provide more detailed inspections of bottle surfaces, identifying defects that may be invisible in 2D images. This technology enables more precise quality control, ensuring that only the highest quality products reach consumers [106].  
Hyperspectral imaging can detect chemical inconsistencies in labels and packaging materials, ensuring that all products meet stringent quality standards. This innovation enhances the ability of machine vision systems to detect a broader range of defects and maintain high-quality standards in the bottling process [97].  
Additionally, the integration of machine vision systems with AI-driven predictive maintenance tools can help identify potential equipment failures before they occur, reducing downtime and improving the overall efficiency of the bottling process. This proactive approach to maintenance ensures that bottling lines operate smoothly and consistently, further enhancing the reliability and quality of wine production [107].