esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VN Allocation and Enforcement Methodology
### Fair Allocation and Enforcement Methodology  
#### 3.5.1 Fair Allocation Methodology  
A VN should be delivered and mapped to the underlying physical network first. The redundant bandwidth of a link should be allocated to one VDC according to the number of tenants using the link and the total excess bandwidth which this link can provide. For simplification, the influence of the flow direction is not considered. By using  
\[map(ve,e)=\begin{cases}1,\text{if }ve\text{ is }\text{ mapped }\text{ to }e\\ 0,\text{otherwise}\end{cases} \tag{3}\]  
whether the logic link is mapped to the physical link can be determined. To obtain the number of tenants on the same link, we have:  
\[tenantsOnEdge(e)=\sum_{VN}[\sum_{ve\in VE}[map(ve,e)]\geq 1] \tag{4}\]  
The total redundant bandwidth on the link can be allocated according to:  
\[R(e)=C(e)-\sum_{VN}\sum_{ve\in VE}C(ve)[map(ve,e)] \tag{5}\]  
Thus, we obtain the per-VDC fair allocation of redundant bandwidth:  
\[F(e)=\frac{R(e)}{tenantsOnEdge(e)} \tag{6}\]  
To a path \(p_{u,v}=\{e_{1},e_{2},\dots,e_{l}\}\), the most effective blocking link determines whether the bandwidth should be allocated. Hence we have:  
\[allocatedBandwidth(p_{u,v})=\frac{min}{p_{u,v}}\{F(e)[e\in p_{u,v}]\} \tag{7}\]  
Our fairness embodies the even bandwidth allocation in the hierarchy of layers. The top layer is the even bandwidth allocation on each link among tenants, one situation should be noted, in which several VM-to-VM flows belonging to one VDC overlap on the same link, and we argue that the bandwidth allocated to these VM-to-VM flows should not be more than that of other VDCs with less VM-to-VM flows. The bottom layer is that the allocated bandwidth is evenly allocated among the VM-to-VM flows belong to one tenant. In Figure 4, tenant A and tenant B will get 0.5X of the bandwidth each--tenant A has three flows, thus each VM-to-VM flow of tenant A gets 0.13X of the bandwidth in average. Let \(f_{u,v}\) be the flow from \(u\) to \(v\), we define:  
\[mapflow(f_{u,v},e)=\begin{cases}1,\text{ if }f_{u,v}\text{is mapped to }e\\ 0,\text{ otherwise}\end{cases} \tag{8}\]  
Thus, we have \(n_{VDC}(e)\) representing the total flows belong to one tenant on the link:  
\[n_{VDC}(e)=\sum_{(u,v\in VM)\wedge(u\
eq v)}mapflow(f_{u,v},e) \tag{9}\]  
Therefore, the bandwidth \(b_{f_{u,v}}\) to each VM-to-VM flow gets:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & \(E\) & \(T\) & \(O\) & \(K\) \\ \hline \multirow{2}{*}{\(E\)} & (1,1,1) & (0.81,1.30,3.07) & (0.43,0.78,1.97) & (0.43,0.62,1.77) \\  & (0.33,0.77,1.23) & (1,1,1) & (0.26,0.43,1.06) & (0.23,0.34,0.90) \\ \(O\) & (0.51,1.28,2.31) & (0.94,2.31,3.81) & (1,1,1) & (0.71,0.79,2.37) \\ \(K\) & (0.57,1.62,2.31) & (1.11,2.93,4.41) & (0.42,1.27,1.41) & (1,1,1) \\ \hline Fuzzy weights & (0.09,0.21,0.73) & (0.05,0.14,0.42) & (0.11,0.29,0.86) & (0.10,0.37,0.79) \\ \hline Crisp weights & 0.247 & 0.147 & 0.304 & 0.302 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of factors (\(CCI=0.012\)). The fuzzy weights of the indicators were determined using Equation (15). After defuzzification of these values using Equations (17) and (18), the final weights were obtained according to Equation (16). Table 11 shows the final ranks. The Weights column presents the final weights and the local weights of the indicators (relative to the individual factors that they describe) in parentheses. The local weights represent the first term in Equation (15). Their fuzzy values are shown in Table 10. Indicator ranks were determined based on the final weights. Further analysis involved the consideration of different scenarios. These scenarios represented certain shifts in organisation priorities caused by internal needs or changes in the environment. These priorities were defined by different values of the factor weights (Table 12), describing a more competitive environment (Scenario \(S_{1}\)), significant technological advancements (Scenario \(S_{2}\)), organisational improvements (scenario \(S_{3}\)), or stronger focus on workers, their knowledge, and abilities (Scenario \(S_{4}\)). Other scenarios (\(S_{5}\) to \(S_{10}\)) presented simultaneous changes in the two factors. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Indicators** & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{4}\) & \(S_{5}\) & \(S_{6}\) & \(S_{7}\) & \(S_{8}\) & \(S_{9}\) & \(S_{10}\) \\ \hline \(E_{1}\) & 7 & 19 & 19 & 18 & 15 & 20 & 19 & 15 & 14 & 19 \\ \(E_{2}\) & 2 & 6 & 7 & 9 & 3 & 6 & 9 & 3 & 2 & 9 \\ \(E_{3}\) & 4 & 16 & 14 & 14 & 7 & 17 & 15 & 9 & 9 & 17 \\ \(E_{4}\) & 1 & 3 & 6 & 6 & 2 & 5 & 7 & 1 & 1 & 6 \\ \(E_{5}\) & 6 & 18 & 17 & 16 & 11 & 19 & 17 & 12 & 13 & 18 \\ \hline \(T_{1}\) & 10 & 9 & 10 & 10 & 16 & 16 & 20 & 20 & 20 & 14 \\ \(T_{3}\) & 10 & 2 & 10 & 11 & 4 & 4 & 12 & 10 & 11 & 3 \\ \(T_{4}\) & 3 & 1 & 3 & 5 & 1 & 1 & 3 & 4 & 4 & 1 \\ \(T_{5}\) & 18 & 7 & 18 & 17 & 12 & 12 & 18 & 19 & 18 & 13 \\ \(T_{8}\) & 17 & 5 & 16 & 15 & 9 & 10 & 16 & 18 & 17 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ranks of the KIs in different scenarios.
## 6 Conclusions  
A series of X-ray images shows different jet formation groups, depending on jet particle distribution pattern and density. This includes a high-density regular jet (Jet Group A), a low-density irregular jet (Jet Group B), and random jet particles. Because these groups form at different times and at different stages of liner collapse, one can conclude that the liner collapse condition controls the jet grouping.  
Each jet group penetrates a given UHMW target at a different speed and depth. That is, the high-density regular jet shows better penetration performance in terms of depth and speed and accounts for approximately 40-50% of effective penetration. The low-density irregular jet shows efficacy, with depth of penetration of around 30-40% of the entire penetration after 165 \(\upmu\)s, although the speed of penetration is slower than that of the high-density regular jet. Random jet particles and a large slug exhibit minimal penetration, and it is excluded from this study.  
This series of observations regarding penetration performance is compared to a theoretical analysis, which indicates that there is a unique collapse condition change near 4-5 \(\upmu\)s after detonation. This is driven by the liner deformation rate and the detonation gas pressure. The liner deformation rate depends on the detonation gas expansion behind the liner, followed by rarefaction intrusion into the center of the liner. The detonation gas expansion followed by liner deformation affects the liner collapse-point angle, and this will be the main driver varying the jet particulation patterns and jet grouping.  
Due to the technical limitation of the data collection during the X-ray experimentation, the direct comparison between the theoretical calculation and the experimental observation was not accomplished. However, based on simple assumptions regarding the origination of the jet driven by the liner collapse and the jet penetration starting/ending point in the target, the penetration performance behavior per each jet segment was estimated.  
Conceptualization, S.L.; methodology, S.L.; validation, S.L. and P.B.; theoretical analysis, S.L., P.B.; Experimentation, S.L. and P.B.; data analysis, C.R. and P.B.; writing--original draft preparation, S.L.; writing--review and editing, C.R. and P.B.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Office of Naval Research grant number N00014-12-1-0377, and The APC was funded by New Mexico Tech.  
Not applicable.  
The authors declare no conflict of interest.
# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Physically Impaired
#### 2.3.2 Human Activity Recognition Systems  
A _RFID-enabled data sensing tool_ that uses machine learning _for human activity recognition_ in ambient assisted living is described [35]. Human activity recognition is essential to assist a person with disability by understanding the context and adapting to the circumstances (emergency-related healthcare, wellbeing services, etc.). Passive RFID tags can recognize sequential and concurrent activities; a multivariate Gaussian algorithm for machine learning, that uses maximum likelihood estimation to classify and predict the sampled activities, is developed.  
A _deep learning model_ named ST-DeepHAR [36] has also been designed for _human activity recognition_ in the Internet of Healthcare Things. It uses a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for fine-tuned temporal feature selection with a convolutional residual network for the spatial fusion of sensor data. Furthermore, a _deep architecture based on the combination of convolutional and LSTM recurrent layers_ performs activity recognition from wearable sensors [37]. The proposed model, which can perform sensor fusion naturally, does not require expert knowledge in designing features. It explicitly models the temporal dynamics of feature activations. It outperforms previous results in the OPPORTUNITY dataset of everyday activities by 4% on average and by 9% in an 18-class gesture recognition task. A _lightweight model_ of a _deep neural network with CNN-LSTM for human activity recognition_ is also proposed [38]. It extracts the features in an automated way and categorizes them according to some model attributes. It is highly robust and, with an accuracy of 97.89%, it provides better activity detection capability than traditional algorithms.
## 3 Experimental Flight and System Identification
### Experimental Flight Setup  
Figure 3 shows the experimental flight setup to obtain flight data for system identification. This setup consisted of a remote control, the KUBeetle, and a ground station. The remote control was used for throttle adjustment for taking off, hovering, and landing the KUBeetle. It was also used for adjusting and maintaining trim conditions during the experimental flight. The flight was conducted indoors without wind disturbance. All exper  
imental flight data were obtained and processed with an onboard control board equipped with an inertial measurement unit (MPU-9250, InvenSense, San Jose, CA, USA). Flight data were then transmitted via wireless communication (NRF24L01+, Nordic Semiconductor, Trondheim, Norway) to the ground station. Considering that the bandwidth of the longitudinal mode dynamics with the PD controller was less than 20 rad/s, the sampling rate was set to be 100 Hz. Recorded data consisted of time, pitch reference command, pitch angle, and pitch rate. In addition, the control input was calculated from the reference command and output data with known control gains.
## 6 Conclusions  
This study explores the challenges of resource allocation in 5G-NR-V2X Mode 2 networks, particularly under aperiodic traffic conditions, and proposes the SEAC technique as a solution. Our findings indicate that traditional approaches, such as SB-SPS and DS, exhibit notable limitations, especially in high-density scenarios, where resource collisions and inefficiencies are pronounced. Under high-traffic conditions, a SEAC model with N steps achieves an RRU of approximately 70.36%, representing a 60.01% improvement over SB-SPS and a 32.12% improvement over DS. SEAC also maintains a POR of around 0.85 as the vehicular density increases, which is 10-15% higher than DS and 20-25% higher than SB-SPS. Additionally, SEAC demonstrates a PRC that is 15-20% lower than DS and 20-30% lower than SB-SPS in high-density environments. The SEAC method, leveraging reinforcement learning in a multi-agent environment, shows a clear advantage in optimizing resource allocation. These results underscore SEAC's potential to enhance the reliability and efficiency of V2X communications, which are critical for the advancement of autonomous driving technologies. Future research can explore the combination of SEAC with other AI-based techniques, as well as its application in diverse vehicular scenarios, including urban environments with complex traffic dynamics. By addressing these challenges, the SEAC technique has the potential to play a pivotal role in shaping the future of ITSs.  
Figure 9: A2C model loss analysis. (**a**) Actor vs. critic loss. (**b**) Reward performance.  
**Author Contributions:** Conceptualization, S.A. and K.C.; methodology, S.A., D.K. and K.C.; validation, S.A., D.K. and K.C.; formal analysis, S.A. and D.K.; investigation, S.A. and K.C.; writing--original draft preparation, S.A. and K.C.; writing--review and editing, S.A., D.K. and K.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00521: Development of 5G-NR-V2X communication technology to support over Lv4 autonomous driving service).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates
## 1 Introduction  
Humans perceive real-world objects through the multiple senses. Semantic fusion of multi-type (multimodal) information about the object of observation received using multi-channel sensing is a natural process for the human brain. Following this natural principle, many scientific and engineering tasks also require complex semantic descriptions of an object of study based on the fusion of multimodal data received from multiple devices.  
In many cases, the object is supervised over the course of time. Such timewise observation can help in understanding the dynamics of the object's behavior and is necessary in many applications. Then, such a timewise complex description requires a collection of multimodal data that are obtained from several sensors measuring certain parameters of the object's behavior, cameras recording the appearance of the object, etc., over time, not necessarily received simultaneously. This brings the problem of correct multimodal data representation based on data synchronization and aggregation.  
Another aspect of using multimodality is its employment for object recognition. The latest advancement in this area concerns multimodal machine learning, which involves integrating and modeling information from multiple heterogeneous sources of data [1]. However, this approach reveals several challenges related to heterogeneity of the data.  
According to [2], the first fundamental challenge is comprehensive data representation that takes into consideration the complementarity and redundancy of multiple modalities. The second challenge is multimodal data mapping that enables matching data of different modalities. The third challenge is data alignment that consists of the necessity to identify direct relations between elements from different modalities. The fourth challenge is data fusion, which yields united information of different modalities. The fifth challenge is co-learning, which consists of transferring knowledge based on different modalities. As stated in [1], there are even more challenges that include reasoning, generation, and quantification, along with data representation, alignment, and transference. All these andrelated issues require correct and comprehensive multimodal data representation, which is key to overcoming challenges related to different aspects of multimodal data analysis and processing.  
The rest of the paper is organized as follows. Section 2 presents a related work review. Section 3 formulates the requirements of temporal multimodal data processing and explains the need to employ the Algebraic System of Aggregates (ASA) for the formal specification of an object. Section 4 explains the basic concepts of the ASA. Section 5 provides a detailed presentation of the algorithms of operations on aggregates. Section 6 presents the notion of a multi-image of an object and proposes an algorithm for multi-image formation. Section 7 offers a use case and provides the discussion of ASA operations application for multi-image formation. Finally, Section 8 concludes the paper.
# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
###### Abstract
considered the question of surface crack detection in concrete structures by comparing the detection capabilities of the YOLOv8 and YOLOv5 models. They concluded that the YOLOv8 algorithm demonstrated a superior performance in terms of detection accuracy [8]. YOLOv8 is a new model in the YOLO series, known for its high detection accuracy, smaller parameter count, and model size. Currently, most deep-learning-based crack detection methods are primarily applied to modern architectural structures, such as pavement crack detection, bridge crack detection, and concrete structure crack detection. However, historical buildings constructed with fair-faced wall structures present unique challenges. These structures not only develop cracks due to their age, but the types of cracks found in these walls differ significantly from those found in other types of structures. The cracks in fair-faced walls are typically wider and pose greater risks. Therefore, this study focuses on addressing the specific challenges of crack detection in fair-faced wall structures, which is of significant importance for the preservation of historical buildings. In this study, we addressed the challenges of ineffective crack detection on fair-faced walls, particularly due to the varying crack characteristics and low detection accuracy across different environmental conditions. We proposed an improved crack detection algorithm based on YOLOv8. The main contributions of this research are summarized as follows:  
1. We reconstructed the backbone network using the ConvNeXt V2 model. This redesigned network demonstrated a superior detection performance, especially on wall surfaces with less prominent crack features. 2. The introduction of the LSKA attention mechanism within the Spatial Pyramid Pooling-Fast (SPPF) structure enhanced the model's focus on cracks, leading to an improved detection accuracy across various environmental scenarios. 3. To address the limitations of the existing open-source dataset, we supplemented it with additional data and employed data annotation and augmentation techniques, significantly expanding and diversifying the dataset.
# Decarbonizing European Industry: A Novel Technology to Heat Supply Using Waste and Renewable Energy
## 3 Energy Consumption, Heat Demand, and Industrial Processes in the EU
### Industrial Processes and Their Required Temperatures  
Different industries and applications may require heat at different temperature levels to carry out their manufacturing or processing activities. Table 1 displays industrial processes requiring temperatures up to 250 \({}^{\circ}\)C that could potentially benefit from implementing the SUSHEAT system. These industries typically operate at temperatures below 250 \({}^{\circ}\)C, as indicated in Table 1[27]. The food industry is commonly categorized as operating at low temperatures, for activities like separation and mixing, to as high as 250 \({}^{\circ}\)C for baking and frying [32]. Specifically, as shown in Table 1, in the canned food industry, the temperature requirements are typically within the range of 110 \({}^{\circ}\)C to 120 \({}^{\circ}\)C, primarily for sterilization processes [33]. In the dairy industry, the highest temperature required for drying milk powder is between 160 \({}^{\circ}\)C and 205 \({}^{\circ}\)C (see Table 1) [34]. In the water industry, the highest temperature required is in the range of 130-150 \({}^{\circ}\)C for wastewater evaporation [32]. The iron and steel manufacturing process consists of several stages, each with its temperature requirements. The temperature range for heating in this process is 180-220 \({}^{\circ}\)C, while the non-ferrous metal industry for drying processes requires temperatures of 60 \({}^{\circ}\)C to 200 \({}^{\circ}\)C (see Table 1) [31]. Nevertheless, by integrating heat pump systems with solar energy, it is possible to meet the significant heat demand required for different processes. This hybrid system can be used for various processes such as drying, washing, cooking, sterilizing, and pasteurizing [35; 36; 37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Industry** & **Processes** &
\begin{tabular}{c} **Required** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & **References** \\ \hline Food and & & & \\ Beverages & Frying and baking & Up to 250 & [32] \\ Canned food & Sterilization & 110–120 & [33] \\ Dairy & Drying & 120–180 & [34] \\ Pasteurizing and & & 60–145 & [32; 33] \\ Sterilization & & 130–150 & [33] \\ Paper & Bleach & 130–150 & [33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Potential industrial processes suitable for implementing the SUSHEAT System.  
Figure 7: The heat demand categorized by temperature range for industrial sectors in EU28 [31].