esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 2 Background and Related Work
### The Disaster Situation Awareness Problem  
The disaster situation awareness problem is a special case of the general situational awareness problems discussed in the previous section. Immediate and accurate detection of disaster situations is crucial for an effective and efficient response. Unfortunately, depending on the kind, size, and intensity of disaster situations, emergency control centers may have difficulties in obtaining the necessary information as needed. We termed this problem the disaster situation awareness problem. There have been a considerable number of research publications on earthquake prediction, but unfortunately, on-time prediction is still a difficult problem [23]. Although this article focuses on the data sources necessary for after an earthquake period, naturally, the necessary data fusion system must be planned, designed, and installed before a disaster occurs.  
When a disaster occurs, emergency control centers try to gather information from all kinds of sources such as victimized persons, existing sensors, telephone calls, authority reports, UAVs, and satellite images [24; 25; 26]. There are a considerable number of publications on disaster/earthquake management [2; 8; 9; 14; 26]. To increase accuracy and efficiency of disaster situational awareness, data obtained from more than one data source must be fused and interpreted, preferably in an automated way.  
One of the main problems in enhancing disaster situation awareness is the high investment costs. Firstly, to improve the awareness, it may be necessary to install many kinds and numbers of data sources within a large geographical area. Secondly, designing such a large-scale data fusion system is a challenging task due to the topological complexity of urban areas, and the multiplicity and variability of the available data sources. Therefore, automated tools are needed.
###### Abstract
However, relying on intuition is a big risk for the enterprise and can lead to huge inventory-management costs. Machine-learning-based demand-forecasting models, which have already proven effective in many cases, are expected to be introduced in SMEs to help reduce their risk significantly. Several studies have already been done on the design of predictive models. However, there are few studies that can be applied to actual SMEs. One of the most important points in the design of the predictive model is that each company must have enough historical data. However, in most SMEs, systematic data management is not under way, and the needed data is either missing or has a large error in the recorded value. Poor data collected without a systematic data-management system can adversely affect the performance of the predictive model and render the predicted results meaningless. Therefore, a prediction system that can indicate the reliability of the data together with the output of the predicted results by means of machine learning is needed for the SMEs. In this paper, we first design a demand-prediction model based on LSTM (Long-Short-Term Memory). Next, we quantify the variability of the data as a function of the 2D kernel mill, and the error range is visualized by calculating the maximum and minimum errors of the prediction result according to the reliability. Finally, we propose a demand forecasting model that minimizes the cost by adjusting the predicted amount to reflect the cost incurred if there is insufficient prediction compared to the actual value and the excess cost. Briefly, we examine the usefulness of 2D KDE-based interval prediction techniques with various advantages over cost-effective and general point prediction-based demand forecasting using inventory data from volatile and difficult to handle SMEs. The main contributions of our paper are as follows. * Mix of point and interval presentations based on LSTM and 2D kernel density function's algorithmic utility test and application through cost-effective functions;
* The composition of a demand forecasting framework and utility test that can cope with various rapidly changing situations using actual SMEs in Korea, forwarding data for five years. This paper is organized as follows. In Section 2, we introduce a previous study on demand forecasting by means of machine learning. Then, we introduce the LSTM technique and the kernel-density estimation technique, which are mainly used in the proposed algorithm. Section 3 presents the overall framework of the demand forecasting proposed in this paper and the algorithm for each part. In Section 4, we examine the effect of the proposed algorithm on randomly generated data and actual demand data of SMEs. Finally, Section 5 summarizes the results of the study and concludes by introducing some future research directions.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
###### Abstract  
Because of the specific characteristics, like high vehicular mobility, unstable topology, and interruption of inter-vehicle wireless communication, it is hard to make a perfect decision on packet forwarding in highly dynamic topology VANETs. Especially in a sparse urban environment, the poor connectivity of nodes will mostly cause problems such as data packet loss and routing redundancy. Therefore, how to choose the best relay node becomes a key challenge in the design of fast and reliable routing protocols. This paper presents real-time effective information traffic routing (RTEIT), which provides an optimal route for forwarding the data packets toward their destination when choosing the relay node. RTEIT introduces a new network parameter named effective information traffic which can estimate the connectivity of nodes by the path that has been successfully created. Moreover, for avoiding unexpected communication interruption, we propose a new formula to evaluate the status of the links via considering the speed, direction, and location information. Finally, the node utility, as the criterion of routing decision, is calculated by effective information traffic and link status. We use the simulator of SUMO and NS-3 platform to evaluate RTEIT, and the results are compared with GPSR MOPR, and MM-GPSR. The evaluation results demonstrate that RTEIT outperforms in terms of packet loss rate, end-to-end delay, and network yield.  
routing protocol; vehicular ad hoc networks; connectivity; information traffic +
Footnote †: journal:  
0000-0002-4701-5000/0000-00002-47018
# Assessment of Batteries' Contribution for Optimal Self-Sufficiency in Large Building Complexes
## 3 Results
For certain prices of 1000 EUR/kW and 250 EUR/kWh of PV power and storage capacity, the CAPEX is calculated and a correlation between it and the decision variables is sought within Figure 15.  
It is apparent from Figure 15 that for the average values of self-sufficiency of all hotels, the optimal solution is 5400 kW of PVs with 16,400 kWh batteries' capacity, which yields 99.001% self-sufficiency and EUR 9,500,000 of CAPEX. Repeating the same process for each hotel separately, optimal solutions that minimize the CAPEX are estimated, and correlations between hotel attributes and CAPEX are sought. In Table 5, the number of hotel rooms and the corresponding optimal solution along with the outcome are presented. The observation made in Table 5 underscores an interesting finding. It is evident that the number of rooms and the overall surface area of each hotel do not exhibit any meaningful correlation with the decision variables or the value of the objective function. This intriguing outcome can be directly attributed to the distinctive energy consumption profile of these facilities. The majority of energy consumption within these hotels is concentrated within their buildings, with a particular emphasis on energy usage for various services rather than for the individual room needs. This means that the size or capacity of the hotels, as represented by the number of rooms or the total surface area, does not significantly impact the optimization of energy-related decisions, which, for the current study, is the investment in a photovoltaic backed by a storage system. Table 6 illustrates the yearly energy requirements for each hotel, alongside the optimal PV power, storage capacity, and their corresponding capital expenditures. Additionally, it provides the calculated cost per kWh and a breakdown of this cost over a 10-year period. Figure 15: Trend line of CAPEX for a range of optimal decision variables. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Rooms** & **Surface Area (m\({}^{2}\))** & **PV Power (kW)** & **Capacity (kWh)** & **CAPEX (EUR)** \\ \hline Hotel 1 & 411 & 384,451.4 & 7200 & 19,800 & 12,150,000 \\ \hline Hotel 2 & 402 & 283,279.9 & 1110 & 2650 & 1,772,500 \\ \hline Hotel 3 & 410 & 28,327.99 & 1400 & 3600 & 2,300,000 \\ \hline Hotel 4 & 260 & 465,388.5 & 2400 & 12,400 & 2,400,2
## 2 Materials and Methods
### Thermal Simulation
The validation of the simulation process involved a thorough comparison between the actual hourly indoor temperatures recorded and those predicted by the model, based on the building's construction and the prevailing external conditions. The results, as depicted in Figure 9, illustrate the monitoring outcomes across six rooms throughout the summer months excluding night ventilation periods. Notably, the relative error in the temperature predictions is less than 5%, affirming the reliability and effectiveness of the modelling approach employed in this analysis. Moreover, Figure 10 illustrates the specifics of the validation of the room GF_R3 model over the course of ten days in September. As can be observed, the model is capable of simulating the indoor room temperature with a high degree of accuracy. The simulated indoor temperature closely aligns with the maximum, minimum, and trend of the actual measurement. The results generated by the software are precise, and it is reasonable to utilize the models. Figure 9: Validation between experimental value and estimated value. A comparison of the simulation results with the actual measured data allows the accuracy and reliability of the model to be evaluated. This serves as a point of reference for subsequent simulations, in which night ventilation will be included and its impact estimated using a variety of operational methods. The results obtained from the CFD model, when considered in conjunction with those of the overall model, ensure that its representation of the energy performance of the building is both realistic and comprehensive.
# Physiological Profiles of Recreational Runners and Cyclists Aged 20 to 60 Years
## 3 Results
### Results in Male Runners and Cyclists
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Age Group** & **Age** & **Mass** & **Height** & **BMI** & **Body Fat** & **FFM** \\
**[Years]** & **[Years]** & **[kg]** & **[cm]** & **[kg/m\({}^{2}\)]** & **[\%]** & **[kg]** \\ \hline
20–29.9 & & & & & & \\ C (n = 42) & 25.8 (3.1) & 82.4 (12.8) & 185.3 (6.4) & 24.0 (2.5) & 11.5 (4.2) & 72.6 (8.6) \\ R (n = 50) & 26.6 (2.2) & 76.7 (8.8) & 181.8 (6.0) & 23.2 (2.3) & 11.9 (4.0) & 67.4 (6.8) \\ d-value & 0.302 & 0.528 & 0.566 & 0.334 & 0.098 & 0.678 \\ Effect size & medium & medium & medium & small & medium \\
30–39.9 & & & & & & \\ C (n = 40) & 35.0 (2.8) & 84.8 (11.0) & 183.2 (6.2) & 25.2 (2.6) & 14.5 (4.4) & 72.2 (7.1) \\ R (n = 94) & 35.0 (2.8) & 81.6 (9.8) & 181.2 (6.2) & 24.8 (2.2) & 14.6 (4.4) & 69.6 (7.9) \\ d-value & 0.007 & 0.315 & 0.323 & 0.172 & 0.023 & 0.339 \\ Effect size & small & medium & small & small & medium \\
40–49.9 & & & & & & \\ C (n = 41) & 44.6 (2.6) & 86.6 (9.9) & 180.9 (5.9) & 26.5 (2.5) & 15.9 (4.0) & 72.6 (6.5) \\ R (n = 63) & 43.8 (3.0) & 83.6 (10.7) & 182.6 (7.5) & 25.0 (2.2) & 15.5 (4.5) & 70.9 (7.3) \\ d-value & 0.281 & 0.289 & 0.246 & 0.646 & 0.093 & 0.243 \\ Effect size & medium & medium & medium & small & medium \\
50–60 & & & & & & \\ C (n = 28) & 55.1 (2.6) & 84.7 (9.6) & 180.1 (6.6) & 26.1 (2.8) & 16.3 (4.6) & 70.8 (7.2) \\ R (n = 20) & 55.3 (3.2) & 76.6 (6.1) & 179.9 (6.7) & 23.7 (2.0) & 12.4 (3.4) & 67.1 (5.2) \\ d-value & 0.07 & 0.972 & 0.03 & 0.96
# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 3 Preliminaries  
### Hypergraph  
In traditional graphs, each edge can only connect two vertices, which may not be sufficient when expressing complex relationships. Hypergraphs break through this limitation by allowing an edge (called a \"hyperedge\") to connect multiple vertices [24]. Specifically, a hypergraph consists of a set of vertices and a set of hyperedges, where each hyperedge is a subset of the set of vertices. This enables hypergraphs to more naturally represent many-to-many relationships. For example, in social networks, a group of users can be viewed as a hyperedge that contains multiple users (vertices) with complex interactive relationships between them. By using hypergraphs, we can more intuitively describe these relationships, allowing for a deeper analysis of social networks. In this experiment, we consider port information security as a complex social network and use hypergraphs to construct its intricate structure. Hypergraphs are used to accurately represent the relationships between ports and the information transmission and interaction patterns within ports. With the help of hypergraphs, we can capture higher-order correlations between more ports, which helps to comprehensively understand the complexity of port information security. This method not only improves the accuracy of port information security modeling but also provides strong support for subsequent analysis and prediction. The difference between hypergraphs and traditional graphs is shown in Figure 1.  
Hypergraph \(H\) is an ordered binary \(H=\left(X;E=\left(e_{i}\right)_{i\in I}\right)\) (\(I\) is a limited identifier set), where \(X\) is a non-empty set of vertices, called a vertex set; \(E\) is a non-empty subset cluster of \(X\), whose elements are called hyperedges.  
Therefore, if \(P(X)\) is the power set of \(X\), then E is a subset of \(P(X)/\left\{\phi\right\}\). In a hypergraph \(H\), the size of the vertex set is called the order of the hypergraph, and the size of the edge set is called the size of the hypergraph.
## 2 System Model
### NB-IoT Signal Format for the Satellite Forward Link
This rate-matching procedure is a puncturing process to obtain code rates that are higher than the one provided by the mother code (i.e., code rates higher than 1/3). However, in order to obtain code rates that are lower than the one provided by the mother code, the NB-IoT matching procedure combines block data repetition with puncturing [29]. The downlink of NB-IoT is formed by four channels, namely: Narrowband Physical Downlink Control Channel (NPDCCH), Narrowband Physical Downlink Shared Channel (NPDSCH), Narrowband Physical Broadcast Channel (NPBCH), and Narrowband Synchronization Signals (NPSS/NSSS) [32]. The first channel, the NPDCCH, is used for the control plane and provides the scheduling information for the downlink and uplink data channels. The second channel, the NPDSCH, is used for the data plane and for paging, and contains dedicated and common downlink data. The third channel, the NPBCH, contains information for the initial acquisition conveying information about the cell parameters. The NPDCCH, NPDSCH, and NPBCH channels are QPSK modulated [31]. Finally, the NPSS/NSSS signals are used to perform the cell search, time and frequency synchronization, and cell identity detection procedures, which are modulated using Zadoff-Chu sequences. In this paper, however, we focus the attention on the study of the QPSK-modulated downlink channels. Finally, the forward link of the satellite relaying system is formed by two links, namely: (i) The link from the gateway to the satellite, the so-called feeder link and (ii) the link from the satellite to the corresponding IoT terminal, the so-called access link. The satellite gateway aggregates the NB-IoT downlink channels and send them to the corresponding satellite beam of the access link. At the satellite, the received data is switched to the access beam of the target IoT device. As expected, the satellite architecture enables to increase the throughput of the system. The following section gives further details on the satellite architectural options that are here studied to forward data from the gateway to the NB-IoT devices. Figure 2: Overview of a rate 1/3 tail-biting convolutional coding with rate matching for NB-IoT [31].
## 2 Materials and Methods
### Experimental Design
#### 2.2.2 Characteristics of Multi-Feeding Training (MF)  
The multi-feeding training protocol was structured into six blocks of five minutes each. Each block was composed of alternating work and rest intervals (10 s of work followed by 7 s of rest). This structure was intentionally selected to replicate the intermittent and high-intensity demands observed during elite-level match play, characterized by short explosive rallies followed by brief pauses. The 10:7 ratio was chosen based on practical evidence and internal load patterns reported in elite competition [7], aiming to maintain a balance between physiological overload and technical execution under fatigue.  
Players were assigned to groups of three per court, a format commonly employed in high-performance environments to manage intensity and ensure continuity in training flow. Within each group, one player served as the primary measurement subject (Player W), while another acted as \"feeder,\" delivering shuttles using either hand-feeding or racquet-feeding techniques in randomized sequences. This configuration was designed to simulate chaotic game-like demands while allowing precise control over shuttle placement and intensity.  
Each block was designed to target specific tactical contexts and movement patterns commonly encountered during high-level competition:  
* Block 1: Player W executed full-court singles drills within a neutral tactical zone covering six designated areas, with shuttle feeds provided by racquet-feeding. Rest period: 60 s.
* Block 2: Player W performed a full-court singles sequence focused on a defensive tactical zone with moderate pressure, with the feeder providing shuttles via hand-feeding. Rest period: 120 s.
* Block 3: Player W engaged in a full-court doubles format, initiating an attack from the backcourt and following up with mid-court coverage, with shuttle feeds delivered through racquet-feeding. Rest period: 60 s.
* Block 4: Player W returned to full-court singles, combining an attack from the backcourt with movement toward the net, fed via racquet-feeding. Rest period: 120 s.
* Block 5: A full-court, free-form multi-shuttle drill was conducted, combining both defensive and offensive patterns. Rest period: 60 s.  
* Block 6: The session concluded with a free-form multi-shuttle drill combining defensive and counterattack sequences in full court, fed via racquet-feeding. Between the training blocks, players were allowed to drink water and 6% carbohydrate/electrolyte drink ad libitum to support hydration and energy replenishment.
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.9 Lean Product (1996)  
The first paper, _The Difficult Path to Lean Product Development_, by [PERSON] et al. [87], introduced the expression \"lean product\" in 1996 as an extension to product development. It refers to fast, efficient, and low-cost product development [88].  
The concept was created for physical goods and generated low interest among researchers until 2006. The same year, [PERSON] et al. [89] proposed the concept in order to go \"beyond manufacturing to any technical or service\" with a systemic view toward \"integrating people, process and tools\".  
The first review in 2011 [88] showed the historical links between LP and TPS while presenting a list of conceptual principles.  
Figure 11: Lean and green evolution.  
Figure 12: Lean thinking evolution.  
In 2015, [PERSON] et al. [90] introduced a systemic view that focused particularly on services as a lean product service system. This approach was recently analyzed in the latest systematic reviews on the topic [91].  
As a conclusion, lean product extends to product development in terms of both goods and services. It has generated moderate and stable interest since 2006 (Figure 13).
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 4 Engineering Application and Progress of Ultrasonic Excitation of Coal and Rock Mass
### High-Power Ultrasound Equipment  
Ultrasonic mechanical effects and cavitation effects are closely related to the ultrasonic power. As the core of ultrasonic devices, the development of the ultrasonic generator determines the application of ultrasonic technology.  
Currently the ultrasonic generator has a frequency of tens to thousands of kilohertz, and is powered from tens of watts to hundreds of kilowatts. The matching ultrasonic transducers have a frequency of 10 to 500 kHz, and the output power reaches more than ten kilowatts. Harbin Institute of Technology has developed an ultrasonic power source with a maximum power of 160 kW [25] and a 10 kW ultrasonic transducer [26] (see Figure 3).  
At the same time, the successful development of high-power ultrasound equipment further expands the scope of application of ultrasound technology. [PERSON] et al. [27] used self-developed ultra-high-power ultrasonic equipment to increase the permeability of the rock mass in the wells in petroleum engineering. It helps to further expand and connect oil channels, and at the same time, cemented asphaltene molecular bonds break to reduce the viscosity of crude oil and increase the fluidity of crude oil. After the ultrasonic treatment, the daily output of experimental wells can be increased by 100% or more.