esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of a Curved Heel Shape in a Running Shoe on Biomechanical Variables and Comfort
## 3 Results
### Multiple Regression Analysis  
Figure 5 and Table A4 show the results of the multiple regression analysis for shoe comfort using the biomechanics variables. The coefficient of determination indicating the explanation power, with an R2 of 0.277, and Durbin-Watson value of 1.437, as well as a statistically significant model fit, was shown at F (6, 53) = 3.387, \(p\) = 0.007. The estimated multiple regression equation was as follows: running shoe comfort = \(-\)1.074 \(\times\) (VLR) \(-\)1.740 \(\times\) (dorsiflexion angle at HC) + 1.021 \(\times\) (ROM of dorsi-plantarflexion) + 0.179 \(\times\) (inversion angle at HC) \(-\) 0.169 \(\times\) (maximumeversion angle) \(-\) 1.007 \(\times\) (ROM of inversion-eversion) + 114.619. The dorsiflexion angle at HC (\(\beta\) = \(-\)0.382, \(t\) = \(-\)3.102, \(p\) = 0.003) had a statistically significant negative effect on running shoe comfort. The ROM of dorsi-plantarflexion (\(\beta\) = 0.271, \(t\) = \(-\)2.138, \(p\) = 0.037) had a statistically significant positive effect on running shoe comfort.  
Figure 4: Inversion at toe off (left) and ROM of inversion–eversion during the stance phase of running (right). * indicates a significant difference between the shoes at \(\alpha\) = 0.05.  
Figure 3: PVGRF (left) and VLR (right) during the stance phase of running. PVGRF: peak vertical ground reaction force, VLR: vertical loading rate, NS: normal shoe, RHS: rounded heel shoe, RLHS rounded lateral heel shoe. * indicates a significant difference between the shoes at \(\alpha\) = 0.05.
## 4 Discussion
### Volatile Organic Compounds
In our samples, the identified pyrroles (1-ethyl-2-pyrrolecarbaldehyde and 2-formyl-1-methylpyrrole) were responsible for the roasted aromas. Their amounts ranged within 0.2-4%, with clear differentiation between the studied samples. The average values of aldehydes and alcohols were 4 and 5%, respectively. Low contents of aldehydes were identified in the C2 samples. Aldehydes are formed either through the oxidative degradation of amino acids during their interaction with sugars at high temperatures or through the interaction of amino acids and polyphenols in the presence of polyphenol oxidase at normal temperatures [46]. Benzaldehyde was identified in the highest amounts in C1, C2, C4, and C6 samples (\(\geq\)1.5%) and is often associated with the oxidation of unsaturated fatty acids through the breakdown of hydroperoxide intermediates. Their presence was not surprising, as coffee beans are known to contain lipids and proteins. In the literature, the total lipid content is around 13% in Arabica coffee and around 10% in Robusta, and of these contents, nearly half are represented by linoleic acid (C18:2). In the volatile flavor compounds formed by the autoxidation of linoleic acid and methyl linoleate, hexanal was identified most frequently. According to our experimental data, hexanal represented less than 1%, in accordance with the results reported by [PERSON] et al. [34] and [PERSON] et al. [47]. Benzaldehyde and 2,3,5,6-tetramethylpyrazine may be considered markers of defective beans [36]. Generally, aldehydes participate in the staling of roasted coffee in the presence of atmospheric oxygen. Stale notes in roasted coffee were correlated with the generation of hexanal after long-term storage [36]. Guaiacol, a common phenolic compound in coffee samples, is produced by carbohydrate catabolism and is connected with the spicy and smoky notes of aromatic coffee [19; 21]. This aromatic compound was detected in the C1, C4, and C6 samples. The volatile acids in coffee are responsible for the flavor constituents, while the less volatile or non-volatile ones contribute to the taste quality [43]. Acetic acid was the only carboxylic acid identified with high contents, especially in the C1 (5.8%) and C2 (7.3%) coffee samples. Roasting temperature, as well as roasting duration, control the development of the aroma [22]. The roasting conditions, as well as coffee variety, climate, and terror, highly influence the volatile compounds in coffee [20]. Generally, the differences between Arabica and Robusta coffee are evident due to the different compositions of aromatic precursors and the comminution processes determined by the different hardness of beans [19]. The differences in roasting profiles and the more aggressive roasting conditions employed for Robusta than for Arabica result in reduction of volatile organic compound intensity in Robusta [19].
## 3 Results
### Implementation Results  
As previously elucidated, the chosen topology for this particular endeavor was the five- and seven-level CHBMIL, with a resistive load at the output stage. The designed module is shown in Figure 9, where every nuanced detail of the inverter's configuration is presented. It is essential to underscore that the realization of this inverter's seven-level output configuration necessitated the judicious replication of the module mentioned above three times. The inverter features the STGW20 NC60 VD Insulated Gate Bipolar Transistors (IGBTs) as the switching semiconductor. The switching angles were implemented in the microcontroller PIC16F877A; this option has various advantages, such as low implementation cost and ease of program.  
On the other hand, Figure 10 presents a detailed depiction of the prototype setup engineered to facilitate the generation of seven distinct voltage levels. This setup serves as a flexible platform capable of accommodating various level requirements, exemplified by the process of module removal, which is employed to tailor the output voltage waveform precisely to the desired configuration. The modular nature of this setup allows for adaptability, ensuring that the synthesis process can be seamlessly adjusted to attain specific voltage levels as necessitated by the application. Furthermore, within the context of this comprehensive exposition, it becomes evident that the visual representation encapsulated in Figure 8 not only conveys the structural intricacies of the prototype setup for the five and seven-level synthesis but also provides a discerning glimpse into the instrumental arsenal meticulously harnessed to scrutinize, monitor, and evaluate the characteristics of the output voltage waveform.  
Figure 8: Seven-level CHBMIL harmonic spectrum in Matlab.  
Figure 7: Five-level CHBMIL harmonic spectrum in Matlab.  
Table 2 provides an overview of the key parameters employed in the cascaded multilevel inverter configurations for both the five-level and seven-level setups.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Voltage sources (five-level CHBMIL) & 90 V each inverter \\ Voltage sources (seven-level CHBMIL) & 60 V each inverter \\ Peak voltage & 180 V \\ Load & R = 250 \(\Omega\), R = 100 \(\Omega\) and R = 100 \(\Omega\) with an \\ AC motor detailed below \\ Frequency & 60 Hz \\ Power & 122.5 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main five- and seven-level CHBMIL parameters.  
Figure 10: Seven-level CHBMIL prototype.  
Figure 9: CHBMIL module.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
###### Abstract  
Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3 GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements.  
5G NR-V2X; LTE-V2X; C-V2X; Mode 2; Mode 4; 3 GPP; analytical model; SINR; BLER; vehicular communications; multiple access interference; platooning +
Footnote †: This paper is an extended version of our conference paper published in [PERSON], [PERSON], [PERSON] and [PERSON], Analytical Modeling of Multiple Access Interference in C-V2X Sidelink Communications. In Proceedings of the 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece, 5-8 September 2022; pp. 215-220.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 2 System Description  
We consider a typical data-gathering sensor network where one sink gathers environmental data from multiple normal nodes that are randomly distributed. We assume that all nodes have the same transmission coverage. From the aspect of network topology, the network is tree-shaped and the sink is located at the top, as shown in Figure 1. The _hierarchical level_ denoted by an integer starting from 0 (highest level) for the sink is incrementally assigned to each level. The connection is made only between adjacent levels \(i\) and \(i+1\), where the nodes at \(i\) and \(i+1\) are called the _parent_ and _child_ of each other, respectively. The nodes associated with the data flow from the lowest level up to the sink compose a _branch_, where the bottom node is called a _leaf_ and nodes at higher and lower levels are called _ancestors_ and _descendants_, respectively. _Neighbors_ are defined as those within the communication range, including parent, child, and possibly several nodes of other branches. Each node keeps the information about neighbors in the so-called _neighbor table_. For medium access control, we employ ALOHA protocol [25], which is well suited to the underwater channel due to its simplicity and insensitivity to large propagation delays [26]. In addition, since ALOHA does not require time synchronization between nodes, it is assumed that all nodes are not synchronized and are homogeneous in terms of capability and physical specification.  
In this paper, a node is called a _dead end_ when it has no more valid parents other than the current parent, if any. Moreover, a node is called _isolated_ when it is completely disconnected from any other nodes. A node becomes isolated necessarily through the state of a dead end. Similarly, a group of nodes is called _isolated_ when the group has no connection leading to the sink, even though the nodes inside the group are connected.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 3 Proposed Method  
This study aims to improve the performance of machine learning regression algorithms in air quality prediction by hyperparameter optimization with particle swarm optimization (PSO). Two main approaches are proposed in this study. In the first approach, the dataset is preprocessed. This preprocessing process includes min-max normalization and removal of missing (null) data from the dataset. Then, the preprocessed dataset is prepared for use in both approaches. After this stage, prediction is performed using XGBoost, support vector machine (SVM), linear regression, and random forest (RF) regressors. The performance of each algorithm is evaluated using metrics such as RMSE, MSE, MAE, and R2 and the results are reported. Additionally, statistical analysis is performed using t-test. In the second approach, hyperparameter optimization is performed for each regression algorithm using PSO. MSE (mean squared error) is used as the objective function of PSO. For this purpose, the parameters of PSO (number of particles, learning rate, etc.) are empirically determined and optimized to improve the model performance. Then, the same metrics (RMSE, MSE, MAE, R2) are calculated for these optimized models and the results are compared. In terms of the model validation process, the dataset is separated by the holdout method in both approaches. It is ensured that no data leakage occurs between the training and test data, thus the generalizability of the model is tested more robustly. This method avoids the risk of overfitting and increases the reliability of the results. Finally, SHAP analysis is performed to show which features are effective in the best performing model and to what extent. This analysis is used to explain the predictions of the model and to visually assess which inputs are more important. The general pipeline of the proposed method is illustrated in Figure 2.
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 2 Theory  
Before introducing the theory, the basic steps of the method presented in this paper are briefly introduced. Figure 1 shows the basic steps of the proposed method. Firstly, we need to collect waveguide acoustic pressure data through a shallow water vertical array. Then, singular value decomposition (SVD) is used for mode extraction. After acquiring the accurate mode distribution \(Z\), the horizontal vector \(R\) can be obtained through Equation (1) to obtain the AMG acoustic pressure field. Finally, we used the simulated annealing algorithm to estimate the depth of the source using the AMG acoustic pressure field as an intermediate parameter.
## 5 Simulation Results
### Parameters Used in the Simulations  
In order to achieve realistic results we have used radio and consumption parameters extracted from actual implemented nodes in our laboratory [12]. The radio transceiver consumes 3.6 \(\times\) 10\({}^{-6}\) watts in sleep mode, 0.072 watts in receiving mode. The power consumed in transmission depends on the estimated distance to the receiver. Estimation is performed using the two ray ground model provided by the NS2.  
The energy used by the radio circuit is 50 \(\times\) 10\({}^{-9}\) J/bit, the bandwidth 250 kbps and the carrier frequency 868 MHz. This carrier frequency allows nodes to transmit up to 200 m at maximum power.  
The maximum transmission distance for a node is limited to 100 m with EDETA; there is no limit to transmission distance in either LEACH or Simple Star. No limits were placed in these two protocols because they assume that all nodes can reach the sink.  
All the simulations were performed using an initial supply of 8 J of energy. The sink was located at the center of the scenario and nodes were randomly distributed. We performed simulations with 100 nodes in two random different scenarios of 200 \(\times\) 200 m, 200 nodes in two different random scenarios of 500 \(\times\) 500 m. Finally, a mean of all data were extracted.  
To carry out LEACH simulations we used the implementation provided by the extensions mentioned above. And we implemented the star protocol, validated with empirical results. In LEACH, CHs are continuously retrieving data from their nodes and sending it to the sink. In star configuration, the nodes send their data to the sink every 20 s.  
Simulation results for the EDETA algorithm were found using the same parameters as LEACH and Simple Star, except for the maximum distance a node can reach while it is transmitting its data. In EDETA it was limited to 100 m. The TIME_CONFIG parameter was set to 20 s for scenarios employing 100 nodes and 50 s for the scenarios employing 200 nodes. The TIME_SUPERFAME parameter was set to 20 s.
## 6 Discussion and Conclusions  
We have proposed and fabricated the silicon-rich nitride ridge waveguides and characterized their linear and nonlinear performances at 2 \(\upmu\)m wavelengths. SRN ridge waveguides with different rib heights and widths were fabricated, exhibiting a linear loss around 2 dB/cm. Four-wave mixing experiments with a CW pump at 1950 nm were performed in the fabricated SRN ridge waveguides and revealed a waveguide nonlinear parameter of \(\sim\)3-6 \(\mathrm{W^{-1}m^{-1}}\) around 2 \(\upmu\)m. With the measured and extracted parameters which characterize the fabrication quality and the material property, optimal design of the SRN ridge waveguides was carried out and a maximal FOM of 0.0804 \(\mathrm{W^{-1}}\) was found among ridge waveguides with various dimension configurations at 1950 nm. Meanwhile, the ridge waveguide could also be designed to achieve the FWM conversion with a superbroad bandwidth (518 nm) and little compromise of the nonlinear FOM. First, these results show that a stripe silicon nitride waveguide may not be the best choice under some fabrication quality and material properties when moving the operating wavelengths from the conventional telecommunication band to longer wavelengths like the 2 \(\upmu\)m band. Second, although the nonlinear conversion efficiency (-51.1 dB) achieved in our fabricated SRN ridge waveguide was much smaller than that of a silicon waveguide (-10 dB [24]) and an SRN waveguide (-18 dB [39]) pumped with pulses, it is indeed the first time, to the best of our knowledge, the nonlinear properties of a silicon nitride waveguide in the 2 \(\upmu\)m spectral window have been revealed using a CW pump. Last, the proposed design method for optimal nonlinear performances, in terms of either FOM or the bandwidth, is expected to open new avenues for achieving better on-chip nonlinear waveguides for applications involving longer wavelengths like the 2 \(\upmu\)m band.  
Conceptualization, design and fabrication, Z.T. and X.G.; Measurement, Z.T., and D.C.; Data analysis, Z.T., S.G. and X.G.; Writing--original draft preparation, Z.T.; writing--review and editing, D.C., H.H., S.G. and X.G.; All authors have read and agreed to the published version of the manuscript. National Natural Science Foundation of China (Grant No. 61875172 and 61475138), Zhejiang Provincial Natural Science Foundation of China (Grant No. LD19F050001 and LY19F050014), Det Frie Forskningsrad (Danish Council for Independent Research) (DFF-7107-00242) and Villum Fonden (Villum Foundation) (023112, 00023316 and 15401). The authors declare no conflict of interest.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 3 Fundamental Concepts
### ICN Paradigm and CCN Approach  
Every CCN node presents three data structures: (i) A FIB (Forwarding Information Base), which is similar to a routing table and is used to forward data requests to data sources; (ii) a PIT (Pending Interest Table) to forward incoming data packets to the requesters; and (iii) a CS (Content Store) for content caching. Each piece of content are identified by a name, which is persistent, unique, and hierarchical. Figure 1 shows the CCN message engine.  
There are two CCN messages: 'Interest' and 'data'. Interest messages are broadcast over the network to retrieve information. If a node receives an interest message and stores the solicited information, it replies with a data message. As CCN was conceived for the internet, interest and data messages have a one-to-one relationship: Nodes transmit a data message only in response to an interest, consuming the interest. However, this mode of operation is not energy efficient and, besides, users may be interested in data provided by multiple nodes in the WSN. If a node is unable to answer an interest, the PIT is checked. A matching PIT entry means that the node has already received the same interest from another user or the message is duplicated, and so the interest is discarded. Finally, if there is no PIT entry, the longest prefix matching is used to select a FIB entry to forward the received interest to a potential data source. After the FIB entry is selected, a new PIT entry is created. Incoming data packets are stored in the CS and then forwarded to the requesters, according to the PIT entries. Big data gathering can create a massive number of interests. Consequently, the PIT may overflow and the network may collapse [15].  
The information requested in WSNs can be a set of data provided by a large number of nodes and CCN does not allow data collection from multiple nodes. Besides, CCN routers have to maintain tables, listing every user request they forward and next hops to content names, which incurs a considerable storage overhead. Moreover, users must express interest for each data packet. This mode of operation is unsuitable for constrained environments, and does not support alerts and emergency notifications.
## 3 Problem Formulation
### Energy Consumption Model
As the time and energy required for data transfer increase proportionally with the amount of data transmitted, the energy consumed during data transfer must be accounted for during data processing;
3. Different cloud services may collaborate in processing a specific task, requiring data exchange and transmission and resulting in additional energy consumption. It is, therefore, essential to consider both data transfer and computation between different cloud services in different clouds in a multi-cloud environment. To account for the energy consumption of file transfer between cloud services in a multi-cloud environment, we define it as follows:  
\[EC^{trans}=P^{trans}\times t^{trans} \tag{2}\]  
where \(EC^{trans}\) is the energy consumption generated by file transfer between cloud services; \(p^{trans}\) is the power consumed during file transfer between cloud services, and \(t^{trans}\) is the time required for file transfer between cloud services. We use a concrete example to illustrate: assuming we are currently at the \(i\)-th cloud service, the energy consumption generated by file transfer between the \(i\)-th cloud service and the previous service in the composition, i.e., the \(i-1\)-th cloud service, is as follows:  
\[EC_{i-1,i}^{trans}=P_{i-1,i}^{trans}\times t_{i-1,i}^{trans} \tag{3}\]  
It should be noted that if two cloud services are in the same cloud, their data transfer is relatively easy, and the time required for transfer is minimal. Therefore, when data exchange occurs within the same cloud, the time consumed for transfer can be considered as approximately 0. That is, if the \(i-1\)-th cloud service and the \(i\)-th cloud service in the cloud service composition are located in the same cloud, then \(t_{i-1,i}^{trans}\cong 0\). In summary, we can define the energy consumption of any cloud service in a multi-cloud environment as follows:  
\[EC_{i}^{total}=EC_{i}^{exec}+EC_{i-1,i}^{trans} \tag{4}\]that is,  
\[EC_{i}^{total}=t_{i}^{exec}\times EF_{i}^{exec}+P_{i-1,i}^{trans}\times t_{i-1,i}^ {trans} \tag{5}\]  
Therefore, the total energy consumption of cloud service composition in a multi-cloud environment is as follows:  
\[EC(MCECSC)=\sum_{i}EC_{i}^{total} \tag{6}\]  
This definition can help us quantify the energy consumption problem of cloud service composition in a multi-cloud environment, thus enhancing the accuracy and reliability of decision-making when making service composition decisions.