esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### DT Applications and Benefits  
DTs are more commonly used in applied sciences due to their practical applications and direct implementation in various industries [8; 13]:  
* Manufacturing--used to improve production efficiency, reduce downtime, optimise the flow of materials and resources, and enhance product quality;
* Aerospace--utilised for monitoring, weight measuring, the exact specification of climatic factors, flying time measurement, and fault diagnosis in aircraft;
* Automotive--used to simulate the behaviour of various components and systems, allowing for the early detection of potential problems and reducing the need for physical prototyping. Nowadays, it can be used to develop autonomous vehicles and support the development of new technologies such as electric powertrains and advanced driver assistance systems;
* Agriculture--enables farmers to create virtual representations of their crops, allowing for the real-time monitoring and optimisation of growth and yield;
* Healthcare--utilised to model and simulate the human body. Doctors can test various therapies and forecast outcomes before actually providing them by creating a virtual clone of the patient;
* Construction--allows architects and engineers to create virtual representations of buildings, which can be used to identify potential problems and optimise performance before construction even begins;
* Energy--used to simulate and optimise power generation and distribution systems;
* Education--allows for the creation of holographic replicas of actual things and systems, resulting in a more engaging and participatory learning environment for actual native digital students.  
DTs have experienced significant growth in recent years, both conceptually and practically. As DTs evolve, they are expected to create a \"cyber-physical continuum\" that blurs the distinction between the virtual and the real [14; 15]. With the introduction of autonomous decision-making, the applications of DTs will only continue to grow, creating new opportunities for innovation and progress.  
DT technology has many advantages to offer enterprises, including increased reliability, productivity, reduced risk, lower maintenance costs, faster production, and better customer service. DT also creates new business opportunities and provides insights into product performance throughout its lifecycle. When looking at the DT benefits, it is possible to see that this technology positively impacts several aspects of the manufacturing industry and the supply chain as a whole. Some of the benefits of introducing digital twinning technologies within the company include improved and the research, design, and development of new products and services, greater process efficiency and productivity, reduced waste, increased supply chain visibility, and more efficient unit-level traceability, reduced risk of counterfeit and adulterations, and increased product safety [16]. Access to more data about a product's journey to the end user and customer information is also a significant benefit of DT technology.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 2 Landslide-Related Data Inventory in Hong Kong  
Data are a fundamental component to forming a landslide prediction model. The GEO has been collecting a spectrum of landslide-related data in Hong Kong over the past 40 years. Several geotechnical data inventories have been formed, maintained, and continually enhanced in light of technological advancements (e.g., improved instrumentation and data acquisition technology). Depending on the nature and purpose of the landslide prediction model, a pertinent database is identified for feature engineering and the data are used to construct, train, or evaluate the machine learning models (see Figure 1). The data inventories considered in this pilot study are briefly described in the following figure.
## 3 Results and Discussion
### Simulation Results and Comparison with Conventional Glazing Solutions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Glazing** & \begin{tabular}{c} **Heating Energy Demand** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Cooling Energy Demand** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Lighting Energy Use** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} \\ \hline \multirow{3}{*}{HELSINKI} & LOW-E DGU & 51.9 & 0.7 & 7.9 \\  & LOW-E TGU & 47.4 & 0.6 & 8.2 \\  & AER-DGU & 48.3 & 0.8 & 8 \\ \hline \multirow{3}{*}{TURIN} & LOW-E DGU & 13.2 & 10.2 & 6.8 \\  & LOW-E TGU & 12.1 & 9.7 & 7 \\  & AER-DGU & 11.2 & 10.8 & 6.9 \\ \hline \multirow{3}{*}{PARIS} & LOW-E DGU & 16.9 & 3.5 & 7 \\  & LOW-E TGU & 15.5 & 3.2 & 7.2 \\ \cline{1-1}  & AER-DGU & 14.9 & 3.9 & 7.1 \\ \hline \multirow{3}{*}{ROME} & LOW-E DGU & 1.7 & 16.9 & 6.5 \\ \cline{1-1}  & LOW-E TGU & 1.7 & 15.8 & 6.7 \\ \cline{1-1}  & AER-DGU & 1.1 & 17.8 & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Heating, cooling, and lighting energy demand for the investigated glazing systems when considering WWR2 (100%).
## 1 Introduction
To implement this process, the study used a detection filter that can analyze the lesion image by pixel unit, which would precisely analyze the lesion shape by identifying the line segment type and then counting the lines. The model's measurement accuracy was improved using the singularity of the lesion as the training data through precise analysis. To combine the fast detection speed of the YOLO model and the high accuracy of the Line-Segment Feature Analysis-Recurrent Neural Network (LFA-RNN) model in classifying breast cancer, as confirmed in previous studies, this study configured the new method as a dimensional reduction and classification model to reduce the amount of computation to quickly detect and diagnose a tumor from a breast ultrasound image. After extending the channel of the breast ultrasound image to improve the performance of the detection model, the study used the YOLO model, which had excellent object detection performance, to detect lesions present in the breast ultrasound. To quickly diagnose the detected tumors, compressed data were formed using LFA, an image dimension reduction algorithm, and was applied as an input to the RNN model to determine whether the detected lesion is benign or malign. The proposed method can detect the lesions fast through the RNN model using the low-dimensional standardized data processed thanks to the YOLO model's fast and excellent detection performance and by reducing the dimensionality of the image data. This paper examines the prior studies on medical image analysis using deep learning in Section 2. In Section 3, it demonstrates the method of detecting breast cancer in breast ultrasound images based on the YOLO model and the diagnosis method through the input data generation and model of the RNN model using dimensionality reduction. This study verifies the performance of the proposed method in Section 4 by comparing it with the experimental results of the previous studies, and it finally ends with conclusions and follow-up future research.
## 4 Results
### Networks & Regression Models
M2-T produced estimates for the like and messaging networks that were strong by rank (first) followed by attractiveness and ethnicity (second) and finally, age (third). This ranking of parameter estimates for M2-T is identical to M1 with excepting the messaging network rank which is not applicable in the case of the M1 model. The order of contribution was divergent however; While the like network was the strongest predictor of a match in the M1 regression model by (positive value, by order 1 st), the like network was the weakest predictor of a match in the M2-T model (negative value, by order 5 th). Agents in M2 could not rely on 'liking' alone to generate a match--in fact, liking many profiles resulted in less matches overall. This is a direct result of the additional multiplicity of the model, i.e., the addition of the messaging feature. Interestingly, attractiveness (by order, 2 nd) held its order position in both models. Age was more relevant in M2 (by order, 3 rd) than in M1 (by order, 4 th) and messaging did not exist in M1 but was ranked 4 th in M2, by rank order. The third component of our findings was the likelihood function of parameter estimates describing the ensemble of networks permuted by MRQAP. Figure 7 summarizes those results through a direct comparison of the 3 models. The intercept likelihood sub-Figure 7 uniquely represents the divergence of Tinder-like applications and Hinge-like applications with intercept estimates for the M2 dating system far exceeding M1. This indicates that M2 agents initialize with a higher likelihood of matching--holding all else equal--but due to negative parameter estimates for liking and messaging must be more tactical with liking and messaging decisions than M1 agents. This hypothesis is uniformly confirmed through an increased variance of estimated parameters for all M1 (Tinder-like model) variables and increased certainty of estimates in both M2-F (false Hinge-like model) and M2-T (true Hinge-like model) estimates. In general, M2 parameter estimates for the 100 permuted networks under consideration were more likely and in some cases (age and ethnicity) were twice as likely as their M1 counterparts. The increased likelihood and less variant distribution of parameter estimates for the M2 system specifies a narrower corridor of conditions that must be met in order for matches to occur and as a consequence agents should employ specialized strategies in messaging. This finding is robust despite that agents in either simulation models are zero-intelligence (see [87]). Figure 7: Likelihood functions for each parameter estimate by standardized scores drawn from Quadratic Assignment Procedure regression permutations.
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 5 Simulation  
This section simulates examples and compares the performance of PL-MMSE, PLKF, BC-PLKF, IVKF and the corresponding algorithms with state constraints for moving targets under linear or nonlinear constraints with Gaussian mixture noise. To clarify, the combination algorithm of PL-MMSE and the mean square method is defined as PL-MMSE-C \((W=I)\). Similarly, PL-MMSE combined with the estimation projection method is set to PL-MMSE-C \((W=P^{-1})\). PL-MMSE incorporated with the linear approximation and second-order approximation methods are defined as PL-MMSE-L and PL-MMSE-S, respectively. Other constrained algorithms are named in the same way as above. Each simulation result is generated in \(M_{0}=1000\) Monte Carlo experiments with \(N=200\) sampling time scans for each run.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology
### Conceptual Model
#### 3.1.1 Update  
The update storage service modifies a field in a page of a notebook based on a request from the producer. Thus, the note with the request must provide the notebook's name, the page's identifier, the topic of the field, and the new data to replace the previous one. However, the editor accepts not all update requests but only those that fulfill the condition given in the request. The update is applied on the indicated page when the condition is missing. In any case, only updates with different data from the current one are accepted. The events of receiving update requests trigger editing actions into the notebook contents. Such modifications must always end eventually, leaving the notebook in a stable state.
## Materials and Methods  
### Device Fabrication  
Figure 1 shows the fabrication process flow diagram of an organic complementary integrated circuit with a top-gate bottom-contact (TGBC) structure. First, a parylene film (diX-SR, KISCO, Tokyo, Japan) was deposited on a glass substrate (Eagle XG, Corning Inc., Corning, NY, USA) with a thickness of 200 nm, using a Specialty Coating Systems Lab Coater 2 (model PDS 2010, SCS Coatings, Indianapolis, IN, USA), as a base layer. This base layer controls the wettability of the silver nanoparticle ink during ink-jet printing. Next, the silver nanoparticle ink (NPS-JL, Harima Chemical, Inc., Tokyo, Japan) was patterned using ink-jet printing (DMP-2831, FujiiFilm Dimatix, Santa Clara, CA, USA) to form the source/drain electrodes. Subsequently, the substrate was heated at 120 \({}^{\circ}\)C for 30 min in air. The resistivity of the printed electrode was 9.5 \(\Omega\) cm and the work function was 4.9 eV [20]. Next, a hydrophobic bank for controlling the printing area of the semiconductor inks was formed by a dispenser using a Teflon solution (Image Master 350 PC, Musashi Engineering, Inc., Tokyo, Japan). Subsequently, to increase the adhesion between the hydrophobic bank and the lower layer, the substrate was heated at 100 \({}^{\circ}\)C for 30 min. Next, the source/drain electrode was surface treated with 4-methylbenzenethiol (4-MBT) through an immersion process. In the case of the immersion process, the 4-MBT was coated on the source and drain electrodes of p-type and n-type OTFTs. The p-type and n-type semiconductor inks were printed using a dispenser system with a stage temperature of 60 \({}^{\circ}\)C. After the printing of semiconductor ink, the devices were annealed in a nitrogen atmosphere at 150 \({}^{\circ}\)C for 30 min. As a gate-insulating layer, parylene (diX-SR) was formed with a thickness of 300 nm. Finally, silver nanoparticle ink was patterned as a gate electrode by ink-jet printing and then sintered at 120 \({}^{\circ}\)C for 30 min. In this study, the electrode shape was not controlled but a relatively thin gate dielectric can be used. This is because the parylene film used for the gate dielectric is uniformly deposited along the surface shape. If a printable gate dielectric is used, a technique to suppress the coffee ring effect [20; 21; 22] of the printed electrodes will be useful and improve the OTFT characteristics and yields.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 6 Conclusions and Future Work  
In this work, an analytical model is proposed to estimate the performance of the unsaturated IEEE 802.11p broadcast networks over an imperfect channel for intra-vehicle communication. Markov chain model is adopted to describe back-off mechanism for broadcast, and M/G/1/K theory is adopted to describe queuing process in MAC layer. Specifically, analytical expressions for delay and packet dropping rate are derived accordingly. Experiment results are shown to prove the perfection of the  
Figure 8: Vehicle dynamic characteristics: (**a**) acceleration; and (**b**) spacing error.  
analytical model and to investigate the effect of bit error rate, packet arrival rate, and platoon size on the performance of intra-vehicle communication. The following conclusions can be drawn from the results. There is no benefit to provide a large bit error rate and packet arrival rate. Communication performance is gradually weakening with the increase of platoon size. We can limit the platoon size, the bit error rate, and the packet arrival rate to satisfy the reliability and timeliness requirements of information transmission for platoon.  
In future work, our team will concentrate on relay-based multi-hop communication model for multi-platoon. In addition, how the inter-vehicle dynamic communication model influences the control algorithm of platoon should be discussed.  
Conceptualization, H.Z. and H.G.; Formal analysis, C.Y. and S.S.; Funding acquisition, H.Z.; Investigation, C.Y.; Methodology, C.Y. and S.S.; Project administration, C.Y.; Resources, C.Y. and H.G.; Software, C.Y.; Supervision, H.Z.; Validation, C.Y., S.S. and H.G.; Writing--original draft, C.Y.; and Writing--review and editing, S.S., H.G., H.Z.  
This work was supported by Major Scientific and Technological Innovation Projects under Grant 02190022117021.  
The authors declare no conflict of interest.
## 2 Material and Methods
### TEC Driver  
Operation of a TEC module for both heating and cooling requires the infrastructure for changing the magnitude and polarity of the voltage applied across it. Change of voltage polarity can be achieved using an H-bridge circuit but magnitude variation requires additional circuitry. This driver circuit, as shown in Figure 2, can be constructed using two half bridge BTN8982 device and a low-pass L-C filter [23]. This combination enables the change of current flow direction through the TEC, thus changing its operation mode. Using PWM signals to operate the driver in ON-OFF mode enables the control of current flowing through the device. However, the power loss in TEC module in such a configuration is higher than in the DC mode. To compensate this power loss, a low-pass L-C filter circuit with a cut-off frequency \(f_{c}\) equal to 11.813 kHz is implemented between the TEC and the H-bridge driver. A signal with PWM frequency of 64 kHz is generated from the microcontroller such that the voltage ripples are minimized by the filter circuit, resulting in a smooth DC voltage. Using this setup, the voltage across the TEC can be regulated from 0 V to a 12 V maximum by varying the pulse width from 0% to 100%.  
Figure 2: Electronic driver for TEC comprising an H-bridge constructed from dual BTN8982 half bridge and low-pass L-C filter for digital to analog conversion.
## 2 Leakage Model of Shield Joints Based on the Persson Model
### Persson Model
#### 2.2.4 Average Interfacial Separation  
The size of the leakage channel, which represents the sealing performance, is directly reflected by the average interfacial separation. When being pressed, the average interfacial separation will decrease, and so does the non-contact region, which further reduces the chance of penetration of the leak channel. The sealing gaskets are made of EPDM rubber, so it can be considered that the sealing material undergoes only elastic deformation when being pressed.  
The relationship of the average interfacial separation \(\overline{u}\) and squeezing pressure on them can be correlated via the elastic potential energy and can be calculated as Equation (6). The detailed derivation procedure can be obtained in [PERSON]'s papers [23; 25; 31].  
Figure 4: Simplified the contact surface model; the upper part of the figure is a rough, rigid body, and the lower portion is a perfectly smooth elastic surface. Under pressure, the rigid, rough surface will squeeze into the elastomer, resulting in a decrease in the average separation height of the contact interface.  
\[\overline{u}=\sqrt{\pi}\int_{q_{0}}^{q_{1}}q^{2}C(q)w(q)\mathrm{d}q \tag{6}\] \[\times\int_{p(\zeta)}^{\infty}\frac{1}{p^{\prime}}\left[\gamma+3(1- \gamma)P_{p^{\prime}}^{2}(q)\right]\mathrm{e}^{-\left[w(q)p^{\prime}/E^{\prime }\right]^{2}}\mathrm{d}p^{\prime}\]  
where \(P_{p}(q)\) represents the relative area fraction of the contact surface under the compressive stress \(p\) and at the magnification \(\zeta=q/q_{0}\), \(C(q)\) is the surface roughness power spectrum, and \(\gamma\) is the reduction coefficient of elastic potential energy generated per unit area of contact region relative to the complete contact.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 1 Introduction and Background
In [25], the author optimized multimedia service in 5G caching networks based on the semantic information of user. Content popularity is estimated by the singular value decomposition (RSVD) which is based on collaborative filtering (CF). In [26], the author optimized the cache hit ratio, and a decision matrix is formed from a huge available data. In paper [27], they proposed a caching scheme for the ultra-dense networks. The backhaul load minimization problem is solved, but not the k-Nearest Neighbors (k-NN) classification. In this article, we proposed a framework for content caching and user satisfaction ratio using cross-tier and intra-tier cooperation among the base stations. Our prominent contributions are:  
* Introducing a data caching system, where data is cached in every tier of the network, thereby, optimizing the CHR and USR utilizing classical method of collaborative filtering and K-mean clustering. * The requests for different contents are received and a popularity matrix is formed in each tier, which is highly sparse in nature. Proactive caching is done by creating clusters of base stations using the unsupervised K-mean clustering in each tier and fill half of the cache memory with the most popular contents in the cluster and rest of vacant cache is occupied by the contents that will be requested in future predicted by collaborative filtering C.F. * Presenting two novel methods for the user satisfaction rate. The first method deals with the clusters made by K-mean clustering, based on contents requests, in each tier and content caching is done by C.F while user satisfaction is achieved thorough cross-tier and intra cooperation among the different tiers. * In the second method, initially contents are cached in all tier based on C.F algorithm. Then clusters are created using K-mean algorithm while taking all the base station, irrespective of which tier they belong to. The cluster formed may have base stations of different tiers. The second method is faster one as requests do not require intra-tier and cross tier flow for the fulfillment of their content satisfaction. * Extensive simulations are conducted with different contents and their requests depends on the mode of the users to elaborate the effectiveness of the proposed algorithms. It is shown that by using learning algorithms, caching the data on base station in the different tier, considerably improved the cache ratio and the user satisfaction ratio.
## 2 Methods
### Rnn  
Compared with CNNs, RNNs are suitable for processing sequence signals. The output of the neuron of a RNN is determined by the current input of the neuron and the output of the neuron at the previous moment. Assume the input \(x=(x_{1},x_{2},\ldots.,x_{t})\), the hidden layer \(h=(h_{1},h_{2},\ldots.,h_{t})\), the output \(y=(y_{1},y_{2},\ldots.,y_{t})\), then for time \(t\):  
\[h_{t}=H(W_{xh}x_{t}+W_{hh}h_{t-1}+b_{h}) \tag{3}\]  
\[y_{t}=W_{hy}h_{t}+b_{y} \tag{4}\]  
where \(W\) is the weight value; \(b\) is the bias vector; and \(H\) is the activation function in the hidden layer.  
However, the traditional RNN has a problem called the vanishing gradient. One way to solve the problem is by using the LSTM model. The basic unit of LSTM is represented by cells, and the input, forget, and output gates control the behavior of the cells to achieve the long-term storage of memory information. Figure 1 shows the workflow of an LSTM model [14].  
According to the above workflow, we can calculate as follows:  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}) \tag{5}\]  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}) \tag{6}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\text{tanh}(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{7}\]  
\[o_{t}=\sigma(W_{x\alpha}x_{t}+W_{ho}h_{t-1}+W_{\alpha}c_{t-1}+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}\text{tanh}(c_{t}) \tag{9}\]  
where \(\sigma\) represents sigmoid function; \(i_{t}\), \(f_{t}\), and \(o_{t}\) are the input gate, the forget gate, and the output gate, respectively; \(c_{t}\) is the cell activation vector with the same length of vector \(h_{t}\) in the hidden layer; and \(W_{ci}\), \(W_{cf}\), and \(W_{\alpha}\) are the weight matrix of peephole connections.  
Figure 1: The workflow of an LSTM model.