esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 2 Random Access Procedure in LTE-A
### Access Class Barring  
As described above that IoT devices choose one of \(R\leq 64\) preambles to transmit and the number of IoT devices may be large. The probability of the event that two or more devices select the same preambles is quite large and thus as a result collisions may happen. This results in loss of time and frequency resources. Now, ACB process works as follows. Along with \(R\) preambles, the eNodeB also broadcasts the ACB factor. Before transmission of RA preamble to eNodeB, each IoT device generates a random number. The device transmits if the generated random number is less than ACB factor and refrains from transmission otherwise. Thus by adopting ACB, the devices that send RA preamble to eNodeB becomes less and manageable. So the ACB factor affects the Msg 1 only and the rest of RA messages remain the same. As mentioned earlier that the ACB factor needs to be optimal in order to overcome the under usage of resources and large number of collisions. The effect of choosing ACB factor is demonstrated in Figure 3. We can see that by choosing ACB factor to be small the number of idle preambles becomes large, whereas the number of collided preambles becomes large if ACB factor is chosen to be too large. In both the cases the number of success preambles is small. However when optimal ACB factor is selected, the number of success preambles become larger than idle and collided preambles.  
Figure 2: Random Access Procedure.
## 2 Background
This analysis used crash data from an Italian highway and found that the EB method outperformed all other approaches investigated in this study. A study conducted by the Federal Highway Administration (FHWA) [15] evaluated four methods of network screening aimed at identifying areas with the highest potential for safety enhancements. The methods evaluated in this study are crash frequency, crash rate, EB expected crashes, and EB excess expected crashes, considering both fatal and non-fatal injury severity levels. The study involves the safety management process from network screening to economic analysis using the intersection data from New Hampshire. This study analyzed the overall economic benefit and benefit-cost ratio for each of the four techniques. The study findings revealed that the EB excess expected measure produce the list of sites with most significant economic benefit and the highest return on investment was generated using the EB expected measure. [PERSON] [16] conducted review of current methodologies for identifying safety improvement sites on roadways. The study findings concluded that the Empirical Bayes (EB) method is the most reliable method for network screening. Furthermore, through simulation experiments [10] and the application of innovative robust evaluation criteria [17], [PERSON] and [PERSON] demonstrated that the EB method stands out as the most consistent and reliable technique for identifying sites with potential for safety improvements. Although the EB method was found to better identify sites requiring safety improvement, it may be subject to several limitations under certain conditions, especially for local agencies. The EB method requires detailed data for roadway geometry and roadside characteristics besides technical expertise that is often inaccessible by local agencies (counties, towns, tribal governments, etc. ).
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures  
In this section, the services and the corresponding network architectures of NTNs are reported. Moreover, it is also analyzed the standardization process of NTN within the 3 GPP organization.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Cyanobacteria
#### 2.1.2 Proteins and Bioactive Peptides from Microbiota  
Microorganisms, including _Chlorella_, _Dunaliella_, _Microatinium_, _Spirulina_, _Oscillatoria_, _Scenedesmus_, _Chlamydomonas_, and _Euglena_, are considered high-quality protein sources [31]. Moreover, these microalgal species are considered alternative protein sources for food applications due to their favorable amino acid profiles. In particular, _S. platensis_ is identified as a major source of bioactive proteins as more than 75% of the protein nitrogen could be extracted from hexane-defatted biomass [5]. Furthermore, several studies, based on compositional analysis of microalgal proteins, have revealed that they could be used directly as supplements or for formulating other health products such as nutraceuticalids [31]. This is mainly due to their enormous diversity and metabolic versatility. In particular, marine microorganisms have attracted considerable attention as a resource for new enzymes as they are more stable and active compared to enzymes from plant or animal origin [32]. Numerous studies have identified a wide range of enzymes with special functions from marine bacteria, fungi, actinomycetes, and other marine microorganisms. These microbial enzymes are being utilized in numerous industries, such as food, leather, textile, and animal feed, as well as in bioconversion and bioremediation [33]. The complexity of the marine environment and extreme environmental conditions, including low temperature, high salinity, and extreme pressures, may determine the differences between enzymes originating from marine microorganisms and enzymes derived from terrestrial sources [31] (Table 2). In addition to this, bioactive peptides derived from marine organisms possess numerous therapeutic activities, including antihypertensive, anticancer, anti-inflammatory, antioxidant, antimicrobial, and immunomodulatory activities, mainly due to their amino acid composition. Moreover, they are often used in the pharmaceutical industry because of their significant therapeutic potential [31; 34]. For example, protein hydrolysate and peptides obtained from _Porphyridium_ sp. showed the potential to prevent inflammation, hypertension, and pain. In particular, protein hydrolysates were incorporated into food carriers (jelly candies) and found their antihypertensive effects in spontaneously hypertensive rats [35].
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 3 Methodology  
### Network Structure  
The structure of the proposed YOLOv7-UAV network is shown in Figures 2 and 3. The proposed network mainly consists of three parts: the input, backbone, and head. Different  
Figure 1: Visual prediction graph. (**a–c**) The input images are shown to be down-sampled to \(5\times 5\), \(10\times 10\), and \(15\times 15\), respectively.  
from the traditional YOLOv7, the prediction results for the YOLOv7-UAV network originate from the three prediction heads: P2, P3, and P4.  
In order to extract features from the input images in the backbone structure, we first adopt the multiple convolution blocks, E-ELAN, and MP structures of the conventional YOLOv7 network. As shown in Figure 2, there are four different scale sizes of the feature maps extracted by the backbone network: C2, C3, C4, and C5. C2 represents the feature map generated in the second stage of the backbone network, where the input image resolution is halved two times. Similarly, C5 represents the feature map generated in the fifth stage of the backbone network, where the resolution is halved five times. C2 is a newly introduced shallow feature map with high resolution that facilitates the detection of small objects with detailed information in UAV images. Second, comparable network units from previous prediction heads are similarly used by the new P2 prediction head and its associated network. Third, the traditional P5 prediction head and its associated network units are eliminated. Finally, a BiFPN-like feature fusion network is added, and the edge nodes from C2 are retained.  
As shown in Figure 3, the YOLOv7-UAV network mainly has CBS, ELAN, MP, SPPC-SPC, and RepConv modules. First, the CBS module consists of convolution (Conv), batch  
Figure 3: The modules of YOLOv7-UAV.  
Figure 2: Overall architecture of YOLOv7-UAV.  
normalization (BN), and a sigmoid linear unit (SiLU). Different colors represent different kernel sizes and step sizes of convolution: e.g., (\(k=1\), \(s=2\)) means that the kernel size \(k=1\) and the step size \(s=2\). Second, the ELAN module is composed of several CBSs and uses expand, shuffle, and merge cardinality to achieve the ability to improve the learning ability of the network without destroying the original gradient path [24]. Third, the MP module mainly consists of max pooling and CBS, which improves the ability of the model to extract and merge features. Fourth, the SPPCSPC module enriches the feature information by obtaining different receptive fields through multiple max pooling operations and merging them with the previous feature maps through the concat operation. Finally, the RepConv module uses a special residual structure to support training. In the prediction process of the network, \(3\times 3\) convolution is used for the output to reduce the complexity of the network and make it easier to deploy. Based on these efficient network structures, YOLOv7-UAV can obtain rich feature information to detect small objects in UAV images.
# The Use of Volcanic Powder as a Cement Replacement for the Development of Sustainable Mortars
## 2 Materials and Methods
### Mechanical Strength Test  
The compressive and flexural strengths were tested on each mortar type according to the UNE-EN 196-1 standard [31], using a compression testing machine model Servosis MEM-101/10A (Servosis, Madrid, Spain). Three different prismatic samples with sizes of \(4\times 4\times 16\) cm were used at 28, 200, and 400 hardening days.
## 3 Results
### The United States  
In the case of the US, we analyzed the data over a period of 490 days from 13 March 2020 (\(n=1\)) to 15 July 2021 (\(n=490\)). The multilogistic approximating function has the following form  
\[f(x)= \frac{1,489,667}{1+\exp(-\frac{x-42}{11})}+\frac{4,777,642}{1+ \exp(-\frac{x-135}{19})}+\frac{22,527,836}{1+\exp(-\frac{x-284}{26})}+\frac{9 96,663}{1+\exp(-\frac{x-311}{4})}\] \[+\frac{3,648,578}{1+\exp(-\frac{x-400}{16})}+\frac{1,228,591}{1+ \exp(-\frac{x-492}{17})}, \tag{27}\]  
with the error \(\text{RMSE}=59\),106.  
The scalograms with CWT coefficients and the approximating function (27) are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Day, \(n\)** & **Date** & \(y_{n}\) & \(f(n)\) & \(|y_{n}-f(n)|\) & \(|y_{n}-f(n)|/y_{n}\) \\ \hline
493 & 2021-07-17 & 5,262,604 & 5,273,915 & 11,311 & 0.00214 \\
494 & 2021-07-18 & 5,307,763 & 5,313,502 & 5739 & 0.
## 4 Decentralization in IoT Security
### Blockchain Security Flaws  
In this section, we present the security challenges associated with applying blockchain technology; the major challenges are shown in Figure 7.  
_Order Dependency in Smart Contracts:_ In most cases, miners do not accept the order in which transactions come and instead select a new order, leading to an issue with transaction order for smart contracts that rely on the current values of storage variables [134].  
_Eclipse Attack:_ The victim's incoming and outgoing connections are manipulated in an eclipse attack, which distorts the true nature of the blockchain. Some forms of assault, such as botnets, can be used to carry out such an attack [135].  
_Border Gateway Protocol Attack:_ The blockchain network's routing information can be compromised using an attack known as a border gateway protocol attack [136]. The goal of this assault is to slow down the spread of data across a network. The other miners are left behind because of the information vacuum that opens up.  
Figure 7: Blockchain security issues.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**IoT Security Challenge** & **Solutions via Blockchain** \\ \hline Interoperability & Since blockchain operates in a decentralized and automated fashion, it is the foundation upon which interoperability rests. \\ \hline Data integrity & Each node in the blockchain shares the same information and can validate it by reerencing prior records. \\ \hline Authentication & Blockchain employs asymmetric cryptography in a decentralized fashion, with each entity in the system assigned a unique hash ID that is shared publicly among all nodes, fostering confidence among the network’s nodes. \\ \hline Authorization and access control & Ethereum blockchain-based smart contracts. \\ \hline Identity management & There are many applications for the immutability and distributed ledger technology that blockchain provides. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Blockchain solutions to IoT security challenges.  
Handling Exception in Solidity:Many smart contracts are implemented in a programming language called \"Solidity\". It is possible to exploit flaws in this language due to improper exception handling for certain actions. A Solidity \"SEND\" operation, for instance, does not contain an exception handler in the event of a failure. Only Boolean values are transmitted, which, for some purposes, may be adequate. Texas, the place of origin of authorization phishing attacks, involves pretending to be the legitimate originator of a series of transactions in order to steal sensitive information.
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
## 4 Simulation Results
### Performance for Detecting Synchronization Signal with m-Sequences  
In this subsection, we evaluate the performance of the proposed method when m-sequences are used as synchronization signals instead of ZC sequences. m-sequences are commonly utilized in random access scenarios [26], making it valuable to examine the applicability of the proposed approach with different sequences. The NN architecture used in this experiment is the same as described in Table 1.  
Figure 7 shows the detection error rate of the proposed method compared to the baseline approaches under varying SNR levels when m-sequences are employed. The results show that the proposed method maintains a lower detection error rate than the baseline methods. This suggests that the proposed approach can effectively adapt to different synchronization sequences while preserving reliable detection performance. These results suggest that the proposed method can be applied to different types of synchronization sequences while maintaining consistent detection performance. This flexibility highlights its potential usefulness in various random access scenarios where different preamble designs are employed.
## 1 Introduction
### Preview Control  
[PERSON] [19], who first introduced the principle of preview information in vehicle suspensions, suggested that the use of preview information can effectively enhance their performance. Preview information from the road disturbance is used in the control approach, before the road disturbances act on the vehicle body. This approach can decrease the controller and the actuator response times, and, hence, improve the suspension performance [20; 21].  
There are two approaches to preview control: \"look ahead\", where the road disturbances are sensed as preview information to the controller of the active suspension, before these disturbances act on the vehicle and \"wheelbase\" controllers where instead of acquiring preview information from the road disturbances, they are sensed from the dynamic changes of the front wheels and later used as preview information to control the active rear suspension. The advantages and disadvantages of each approach are explained in more details in the review paper of [20].  
The motivation of this work is to develop a novel control approach for an active seat suspension based on the principle of feedforward or 'preview' information, as well as available and measurable feedback states. The preview information that is used in this active seat controller is based on the dynamic changes in the vehicle secondary (spring/damper) suspension, together with feedback states obtained or derived from available measurements. The use of a preview signal can provide a significant improvement in the active seat suspension performance by preparing the controller to cancel the effects of disturbances in advance of their action on the seat and occupant.  
In a previous study by [PERSON] et al. [22] the concept of using such preview information in an active seat suspension was investigated experimentally. The results showed that this approach significantly reduces the vertical vibration at the driver's seat when compared with a passive system. In addition, in a recent simulation based study by the authors [23], this concept was successfully applied to an active seat in a multi-degree of freedom full vehicle model incorporating the roll and pitch modes.  
In the paper presented here, a further five simple and novel active seat suspension controllers using preview information from the vehicle suspension states, as well as available feedback states from the seat are proposed. The performance of these active suspension controllers is demonstrated initially through simulation and is then validated through experimental tests.
## 3 Simulation and Experimental Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (s)** & **Id (a)** & **Id (b)** & **Torque (a)** & **Torque (b)** \\ \hline
0.01 & 1.20 & 0.05 & 9.84 & 1.31 \\
0.05 & \(-\)0.27 & \(-\)0.003 & 2.12 & 0.04 \\
0.1 & 0.16 & \(-\)0.002 & 0.78 & 0.03 \\
0.3 & \(-\)0.023 & 0.001 & \(-\)0.60 & 0.01 \\ \hline
0.55 & 0.50 & 0.002 & \(-\)2.16 & \(-\)0.04 \\
0.6 & 0.013 & \(-\)0.001 & \(-\)1.57 & \(-\)0.01 \\
0.8 & 0.72 & \(-\)0.002 & 2.02 & 0 \\ \hline
1.05 & 0.60 & \(-\)0.01 & 6.03 & 6.21 \\
1.1 & 0.25 & 0.003 & 7.49 & 6.08 \\
1.3 & 0.09 & 0.002 & 6.32 & 6.01 \\ \hline
1.55 & 0.39 & 0.006 & 1.68 & \(-\)0.19 \\
1.6 & 0.62 & 0 & 0.64 & \(-\)0.09 \\
1.8 & 0.22 & \(-\)0.003 & 0.81 & \(-\)0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: D-axis current and torque of experiment 1. Figure 18: Experiment 1 PMSM electromagnetic torque waveforms of the (**a**) Control scheme of combining the backstepping controller with the ESO and (**b**) Control Scheme of combining the integral backstepping controller with the ELESO. Figure 21 shows the current waveforms of d and q-axis under experiment 2. In Figure 21a, when the rotor speed changes, the current waveforms have a very obvious jump. Moreover, due to the existence of higher harmonics, the electromagnetic torque waveform in Figure 22a has a large fluctuation. In Figure 22b, higher harmonics are filtered, and the waveform is smoother. Table 5 shows the d-axis currents and torques data of the two schemes in Experiment 2. According to the data in Table 5, the steady-state error of d-axis current in Figure 21b is 0.001-0.002. The electromagnetic torque error in Figure 22b is stable at 0.01. Experiment 2 proves that the control scheme based on the integral backstepping controller and the ELESO has a faster response speed, lower parameters jump and smaller steady-state errors when starting and changing speed under the constant load. This is consistent with the results of experiment 1. In conclusion, the above experiments further verify that the control scheme based on the integral backstepping controller and the ELESO has better performance than the scheme based on the backstepping controller and the ESO. The experimental results show that the simulation results are correct. Compared with the ESO, the ELESO eliminates the interference of the higher harmonics. The backstepping controller can greatly reduce the influence of the parameter changes and the charge disturbances by introducing the integral action in each step to ensure high accuracy speed control. The scheme can be applied to places with high performance.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 3 Results
### Modeling Approaches for Learning and Deteriorating Effects
#### 3.3.2 Deterioration Effect
Figure 9a summarizes the frequency by the system configuration (two or three more common ones) according to each deterioration effect approach (in Figure 9a: _SM: single machine; PM: parallel machine; FS: flowshop_). Figure 9b shows the percentage of objective function types per each deterioration effect approach (in Figure 9b: _SC: single-criteria objective function; MC: multi-criteria objective function_). As with the learning effect, these summarized approaches are the ones most commonly employed in the literature, though some other modeling approaches have been proposed. For example, [PERSON] et al. [84] employed the step-deterioration effect model to determine the actual processing time, where the job's processing time is described by an upper-bounded piecewise function. Although most short-listed articles use the term \"deterioration effect\" referring to job deterioration, few authors have employed the terms fatigue or forgetting [49; 71; 89; 90; 91; 92; 93]. Note that there is a group of works that have simultaneously taken into consideration the effects of learning and deterioration (26.2% of short-listed papers). Most of these effects (over 70%) were studied in a single machine configuration. We can see that the learning and deterioration approaches are mainly based on the principle of position and sum-of-processing time. However, if models are mainly focused on the scheduling problem of hand-intensive tasks, they should be complemented by characteristics that address human behavior (e.g., experience, forgetting, fatigue).
## 3 Preliminaries  
### Task Formulation  
Let \(U=\{u_{1},u_{2},\ldots,u_{|U|}\}\) denote a set of users and \(L=\{l_{1},l_{2},\ldots,l_{|L|}\}\) represent a collection of locations. Every location, \(l\in L\), is characterized by a distinct geographical coordinate tuple, specifically defined as (longitude, latitude), i.e., \((lon,lat)\). For each user \(u\in U\), we extract their trajectory represented as \(\mathcal{S}_{u}=\{(l_{i},t_{i})\}\mid i=1,2,\ldots,n\}\), wherein each tuple \((l_{i},t_{i})\) signifies that user \(u\) visited location \(l_{i}\) at the corresponding timestamp, \(t_{i}\).  
Considering a specified user, \(u\), along with their trajectory sequence, \(\mathcal{S}_{u}\), the primary objective of the location prediction task is to predict top-k locations that user \(u\) is anticipated to visit in the next timestamp.
# Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
## 2 Research Object and Investigation Methodology
### Geometric Definition of the 3D Blading  
This paper employs the widely used Sweep-Dihedral Coordinates to define the 3D blading parameters [25; 29; 30]. As shown in Figure 1, the displacement of the blade section in the chordwise direction is called \"sweep\". Meanwhile, an obtuse angle between the endwall and leading-edge stacking line in the meridional plane is defined as a forward sweep. On the other hand, the displacement of the blade section in the direction perpendicular to the blade chordwise is called \"dihedral\". Similarly, an obtuse angle between the endwall and blade stacking line in the meridional plane designates a positive dihedral, and vice versa.