esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry
### Unsupervised Learning: A Subset of the Machine Learning Family
Using satellite imagery from the Mohammed VI satellite, their model successfully segmented orchard rows and classified trees into categories based on canopy size, achieving an F-measure score of 0.93. This demonstrates the potential applicability of similar machine learning frameworks for orchard management in Spain, enabling precise monitoring of tree health and facilitating sustainable agricultural practices through data-driven decision-making. These advancements underscore the transformative potential of unsupervised learning in fostering sustainable and efficient agricultural practices. By enabling precise segmentation, anomaly detection, and resource optimization, unsupervised learning not only addresses current challenges but also paves the way for future innovations in the agrifood industry.
## 3 Results
### Vas  
The study compared the VAS results in groups MT and E before and after therapy (Table 3). The group results were similar before the start of tests. Results in both groups significantly improved after the end of therapy (\(p<0.01\)). The pain level significantly decreased in both groups without any significant difference between groups E and MT.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Group**} & \multicolumn{4}{c}{**Before**} & \multicolumn{4}{c}{**After**} & \multirow{2}{*}{**p\({}^{3}\)**} \\  & & **Mean \(\pm\) SD** & & **Median** & & **Range** & & **p\({}^{1}\)** & **Mean \(\pm\) SD** & & **Median** & **Range** & **p\({}^{2}\)** & **p\({}^{3}\)** \\ \hline Active & E & 100.1 \(\pm\) 22.5 & 106.4 & 19.7–141.2 & 0.02 & 103.5 \(\pm\) 22.2 & 104.5 & 26.8–139.0 & 0.04 & 0.57 \\ range & MT & 98.4 \(\pm\) 21.2 & 97.6 & 51.0–139.0 & 0.27 \({}^{\star}\) & 104.1 \(\pm\) 16.8 & 103.2 & 64.5–142.5 & 0.92 \({}^{\star}\) & 0.01 \({}^{\star}\) \\ (\({}^{\circ}\)) & C & 111.4 \(\pm\) 15.5 & 112.9 & 72.2–135.2 & 112.2 \(\pm\) 13.9 & 115.9 & 85.9–141.2 & 0.73 \({}^{\star}\) \\ \hline \multirow{2}{*}{Squat (\({}^{\circ}\))} & E & 71.5 \(\pm\) 15.3 & 73.2 & 33.2–95.8 & & 74.6 \(\pm\) 15.5 & 76.5 & 38.1–100.6 & & \(<0.01\)\({}^{\star}\) \\ (\({}^{\circ}\)) & MT & 81.1 \(\pm\) 19.2 & 80.7 & 43.1–118.5 & 0.06 & 84.0 \(\pm\) 17.8 & 81.5 & 45.9–115.0 & 0.07 & 0.12 \({}^{\star}\) \\ C & 80.8 \(\pm\) 16.4 & 82.6 & 52.7–122.8 & & 82.1 \(\pm\) 16.1 & 79.4 & 56.3–114.6 & & 0.36 \({}^{\star}\) \\ \hline \hline \end{tabular}  
* p\({}^{1}\)—intergroup (Kruskal-Wallis test or Mann–Whitney U test); p\({}^{2}\)—intergroup post-treatment comparison (Kruskal-Wallis test or Mann–Whitney U test); p\({}^{3}\)—intergroup post-treatment comparison (dependent Student’s \(t\)-test or \({}^{\star}\) Wilcoxon signed-rank test).  
\end{table}
Table 2: Test results of the range of active knee motion and squat.
## 4 Case Study of Data Collaboration
### Space-Time Travel Data Mining
#### 4.2.4 Time Travel Feature Mining  
Based on the above GPS data, we calculated the travel data within each hour to mine the travel characteristics. As shown in Figure 6, Both (a) and (b) show obvious peak characteristics at 08 o'clock and 17 o'clock, which are the morning rush hour and evening rush hour of working days. However, the duration of rush hour travel data in (c) and (d) is significantly longer than that in (a) and (b), which proved the difference in travel characteristics between weekdays and weekends.
## 4 Results and Analysis
### Simulation Results and Analysis
#### 4.1.4 Simulation Considering Tolerances  
Figure 18 shows the CM (Common-mode) currents at the receiver and the transmitter. The Blue curve represents the CM current at the receiver, and the red color means the transmitter's CM current. Though the CM current at the transmitter in the band is about 90 dB\(\mu\)A/Hz, the current at the receiver is worse. Even if the length of the cord is shorter than the distance in the LVDC (Low-Voltage Direct Current) grid system, it occurs.  
Figure 16: Comparison Regarding the grid voltage drops. (**a**) 10% drop in grid voltage. (**b**) 20% drop in grid voltage.  
Figure 17: Comparison Regarding the ramp disturbance.  
According to measurements implemented on the study site, measurements result from the differential-mode radiated EMI (Common-mode) and the CM. These disturbances, the sum of the differential mode and the CM do not meet the regulations of the standards. Because the goal is to find whether there are interferences, the interferences generated from the power electronics must be in separation status. Therefore, to achieve the present purposes, the standardization of the LVDC applications should be further.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Bioavailability of Trace Elements (TEs)
While, in another study, the CH\({}_{4}\) yield has not enhanced due to the increased amount of highly bioavailable fractions of TEs in the AD of RS. Therefore, the relationship between the higher amount of highly bioavailable fractions of TEs and enhanced CH\({}_{4}\) yield is not clear. Moreover, specifically for RS, only Fe and Co have been investigated to assess the effect of bioavailability on the CH\({}_{4}\) yield. A large fraction is not available \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Substrates**} & \multirow{2}{*}{\begin{tabular}{c} **AD Mode** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{**TE**} & \multicolumn{2}{c}{**Water-Soluble and**} & \multirow{2}{*}{**References**} \\  & & & & **Exchangeable** \\  & & & **Fractions (\%)** & \\ \hline RS & \begin{tabular}{c} Batch \\ 37 \(\pm\) 2 \\ \end{tabular} & Fe, Co & 23, 48 & [110] \\ \hline RS & \begin{tabular}{c} Batch \\ 35 \(\pm\) 2 \\ \end{tabular} & Fe & 1.7–3.1 & [109] \\ \hline MS & \begin{tabular}{c} Semi-continuous \\ 37 \(\pm\) 1 \\ \end{tabular} & Fe, Ni, Co, and Mn & 14, 29, 36, and 70\({}^{\circ}\) & [121] \\ \hline SHW & \begin{tabular}{c} Semi-continuous \\ 38 \\ \end{tabular} & \begin{tabular}{c} Ni, Co, and \\ Mo \\ \end{tabular} & 62, 62, and 68 & [118] \\ \hline SHW & 38 & \begin{tabular}{c} Fe, Ni, Co, Cu, Zn, \\ and Mo \\ \end{tabular} & 54, 52, 48, 57, 54, & [120] \\ \hline GS + MS\({}^{\text{a}}\) & 48 & \
\begin{tabular}{c} Fe, Ni, Co, Cu, Zn, \\ and \\ Mo \\ \end{tabular} & 22, 36, 24, 46, 43, & [120] \\ \hline PM + MS\({}^{\text{a}}\) & 40 &
\begin{tabular}{c} Fe, Ni, Co, Cu, Zn, \\ and Mo \\ \end{tabular} & 27, 62, 60, 29, 28, & [120] \\ \hline \hline \end{tabular}  
* **AD**, Anaerobic digestion; **TE**, Trace element; **RS**, Rice straw; **MS**, Maize straw; **SHW**, Slaughterhouse waste; **GS**, Grass silage; **MS\({}^{\text{a}}\)**, Maize silage; **PM**, Pig manure; *, also include carbonates fraction of TE.  
\end{table} 
Table 10: Water-soluble and exchangeable fractions (relative to a total concentration of TE) of TEs in the AD of various substrates. for microbial uptake during the AD of RS and this aspect should be considered for an appropriate supplementation of TEs to achieve enhanced CH\({}_{4}\) yield.
## 4 Results and Discussion
### Advantages and Disadvantages Discussion  
This study uniquely focuses on university campuses, a relatively independent and understudied area in urban flooding research, providing an in-depth understanding of campus-scale hydrological dynamics. By integrating field investigations with SWMM, it offers a comprehensive analysis of waterlogging dynamics. The introduction of the \"sponge campus\" concept, which incorporates permeable surfaces and rainwater reuse, represents an innovative approach compared to conventional urban flood mitigation strategies. Detailed spatial and temporal data on waterlogging distribution under different return periods provide a scientific basis for urban planning and sponge campus design.  
However, the study is based on the China University of Geosciences and may not be directly generalizable due to differences in climate, topography, and urban drainage systems. SWMM requires significant expertise for calibration and validation, limiting its rapid application to other campuses or regions. The long-term impacts of climate change and urbanization on campus hydrological cycles were not addressed in this study.  
Despite these limitations, this research offers valuable insights and methodologies for campus-scale hydrological simulations and flood risk assessments, which will be expanded in future studies.
## 3 Results
### Impact of CNCORS on BDS Single-System POD
#### 3.2.2 Comparisons with GBM Orbit  
Strategies 3 and 4 are compared with the GBM BeiDou orbit products, and the results are shown in Figure 4 and Table 9. The table shows that the GEO and MEO satellites exhibit a slight difference when compared to the GBM orbit and present no obvious features. For IGSO satellites, the difference between the orbit solution from strategy 3 and the GBM orbit is smaller than that between the solution from strategy 4 and the GBM orbit, because under the single-system BeiDou orbit determination mode, the accuracy of the IGSO satellite orbit determination in strategy 3 is considerably better than that in strategy 4 and undergoes the most significant improvement. Such an improvement results in a larger difference between the orbit solution from strategy 4 and the GBM
## 3 Overview of Short-Range and Long-Range Communication
### Short-Range Direct V2X Communication  
In order to facilitate direct exchange of information between V2X nodes within the allotted spectrum, mainly two technologies exist. The initial protocol, based on IEEE 802.11p, comprises separate ITS protocol stacks in Europe (ITS-G5) and the U.S. (DSRC). The second technology is C-V2X PC5, commonly referred as C-V2X Sidelink, which is based on cellular networks. The communication range for these short-range wireless vehicular communication technologies is a few hundred meters. They function within the allocated frequency bands specifically designated for ITS at 5.9 GHz and provide low-latency communication that is well-suited for safety-critical applications. ITS-G5 and DSRC are constructed upon the 802.11p standard, employing Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) for wireless medium access.  
Conversely, C-V2X PC5, introduced in 3 GPP Release 14 as a more recent technology, provides two operational modes: Mode 3 and Mode 4 [17]. In mode 3, also known as LTE sidelink mode 3, a network entity named Sidelink Communication Manager (SCM) is responsible for managing sidelink resources and establishing sidelink connections between devices. In mode 4, autonomous scheduling is performed by V2X nodes with the help of sensing-based SPS. In SPS, each V2X node orchestrates its allocated resources, spreading them out in time for mitigating collisions with concurrent transmissions on identical subcarriers [18]. However, in dense environments, scalability issues can impact latency and collisions are imminent if more than one V2X node chooses identical resource blocks, leading to transmission collisions. 3 GPP Release 16 introduced 5G New Radio (NR) sidelink, which allows direct communication between vehicles using 5G technology. Release 16 also introduced enhancements to the network-controlled mode of sidelink communication defined in Release 14. These enhancements include improved resource allocation mechanisms, better support for multi-hop communication, and the ability to use 5G NR-V2X  
Figure 2: V2X technology classification [16].  
sidelink as a backup for LTE-V2X sidelink. However, Release 16 Sidelink equipment is not yet available on the market.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
###### Abstract  
In recent years, IIoT scenarios show increasing demand for high-precision positioning. Cellular-based positioning plays a very important role in this application area. The cellular-based positioning system can be categorized as an asymmetric processing system. In this system, multiple base stations transmit positioning reference signaling to one device and the device derives its coordinates based on the measurement of the positioning reference signal by using certain positioning algorithms. The classical positioning algorithms encounter great challenges in satisfying the stringent precision requirement. Meanwhile, artificial intelligence (AI)-based positioning solutions draw great attention due to their strong ability in improving positioning accuracy in IIoT scenarios. For AI-based algorithms, generalization capability is one important metric. It demonstrates the ability to adapt to different environments. However, there is little literature touching on the investigation of generalization capability. In this article, we will tackle this issue by considering typical features in IIoT scenarios including the clutter distribution, network synchronization error, and receiving timing error. The impact of these typical features on the generalization capability is firstly evaluated and then the feasibility of existing popular generalization improvement solutions, i.e., optimized training data set and fine-tuning are tested under different cases. At last, directions to further guarantee the generalization capability are presented. The results of this article provide useful experience for developing AI models for positioning in realistic IIoT scenarios.  
positioning AI; clutter distribution; network synchronization; receiving timing; generalization +
Footnote †: journal:  
18 March 2023  
Revised: 8 April 2023  
Accepted: 25 April 2023  
Published: 27 April 2023
## 3 Solution Fundamentals
### Rule Management System and Drools  
A Rule Management System (RMS) is a form of an expert system which usually uses an ontology based representation to codify the information into a knowledge base that can be used for reasoning [19]. An RMS depends on two kind of memory as it is shown in Figure 4. The working memory holds the facts which present the domain knowledge, while the production memory holds a set of rules which are usually presented in a form of conditional statements. The reasoning engine solves a given problem by matching the present facts with the existing rules. The reasoning engine organizes and controls the follow of the solution route based on one of the following reasoning methods [20]:  
* Forward reasoning chaining: in forward reasoning chaining, the RMS starts with a set of initial facts then it determines new facts every time a rule matches a fact. The RMS will go through a chain of rules firing sessions to reach its final target, which ultimately leads to find the solution route from the initial facts to the final goal in a forward approach of reasoning.
* Backward reasoning chaining: in backward reasoning chaining, the RMS starts from the final goal, then it finds which rules must be fired to lead to this goal, therefore the RMS can determine the facts which are needed to reach the final goal. During the backward chaining, a consequence of sub-goals will appear, which ultimately leads to find the solution route from the final goal to the initial sub-goal in a backward approach of reasoning.  
Drools is a Business Rule Management System (BRMS) that has been created by jboss organization, therefore it adds more features than a regular RMS [21]. As it provides a tool for a rule-based solution creation, managing, deploying, and analyzing. One of the biggest advantages of Drools is its ability to apply a hybrid chaining reasoning. A hybrid chaining reasoning is a mix between the forward and the backward chaining which can be more efficient in some cases than both. Also, Drools extends the Rete algorithm for pattern matching which called ReteOO. ReteOO denotes the enhancement of Rete algorithm by combing it with other Object Oriented (OO) concepts such as abstraction, inheritance, and encapsulation.
## 3 Preliminaries and Motivations
### RPL Fundamentals
#### 3.1.1 Topology Formation  
After accepting the DODAG settings in LBR's DIO, based on the receiving rank in DIO, a node can select a preferred parent and then decides its own rank. The quadruples of instance number, DODAG ID, version number, and rank can uniquely identify one node. Then, that node will update its own information and broadcast a new DIO message containing the updated fields. This step is relayed and repeated periodically through a DIO timer by each node, until all nodes join the DODAG. Figure 2 illustrates this process. If a node wants to join quickly, it can broadcast a DODAG information solicitation message actively. The node which receives this type of message will broadcast its DIO message immediately, which will accelerate the DODAG formation process.
# Conductive Textiles for Signal Sensing and Technical Applications
## 4 Testing for Conductive Textiles  
The electrical properties of conductive textile materials require proper characterization based on their applications. They are often characterized through determining the resistance, conductivity, surface resistance or sheet resistance, and EMS (electromagnetic shielding) of the textile substrates. These tests are usually performed following standards or by slightly modifying the standards [134].
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 3 Deep Learning Model
### Transformer
#### 3.2.2 Multi-Head Attention  
There is an advanced version of self-attention called the multi-head attention mechanism. In the previous chapter, the input was multiplied only once by the transformation matrices \(W^{Q},~{}W^{K}\), and \(W^{o}\), and then its corresponding \(q\), \(k\), and \(v\) values.  
In the multi-head attention mechanism, taking two inputs \(a_{1}\) and \(a_{2}\) as an example, \(q\), \(k\), and \(v\) will be multiplied again by a transformation matrix. Assuming there are two attention heads, two types of \(q\), \(k\), and \(v\) will be obtained, respectively. As shown in Figure 6a, the first attention head \(q^{1,1}\) will perform an attention calculation with \(k^{1,1}\), then it will perform Softmax, and then it will multiply by \(v^{1,1}\). Next, \(q^{1,1}\) will be calculated with \(k^{2,1}\) for attention, then Softmax, and finally multiplied by \(v^{2,1}\). Finally, adding the previous two results gives \(b^{1,1}\), that is:  
\[b^{1,1}=\sum_{i=1}^{n} Softmax\Big{(}q^{1,1}\cdot k^{n,1}\Big{)}v^{n,1} \tag{12}\]  
where \(n\) is 2, which is the number of heads.  
Figure 6: _Cont._Then, as shown in Figure 6b, the second attention head \(\mathbf{q^{1,2}}\) will perform an attention calculation with \(\mathbf{k^{1,2}}\), then it will perform Softmax, and finally it will multiply by \(\mathbf{v^{1,2}}\). Then, \(\mathbf{q^{1,2}}\) performs an attention calculation with \(\mathbf{k^{2,2}}\), then it performs Softmax, and finally it multiplies \(\mathbf{v^{2,2}}\). Finally, adding the previous two results gives \(\mathbf{b^{1,2}}\), that is:  
\[\mathbf{b^{1,2}}=\sum_{i=1}^{n}Softmax\Big{(}\mathbf{q^{1,2}}\cdot\mathbf{k^{n,1}}\Big{)} \mathbf{v^{n,1}} \tag{13}\]  
Finally, these two outputs are concatenated and multiplied by an output transformation matrix \(\mathbf{W^{O}}\) to obtain the final output \(\mathbf{b^{1}}\), as shown in Figure 6c.
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 5 Comparison and Discussion of Experimental and Numerical Simulation Results
The experimental results show that:  
(l) For Ly-12 aluminum targets with different thicknesses in the same experiment, the peak stress of thermal shock wave attenuates exponentially with the increase of target thickness, indicating that the short pulse thermal shock wave attenuates rapidly at the initial stage of propagation. (2) For target materials with basically the same thickness in the same experiment, or with basically the same thickness and energy injection amount in different shots, the thermal shock stress peaks are basically the same, which indicates that the test results obtained in this paper are effective and reliable. (3) For the Ly-12 aluminum target with an average energy injection of 174-196 J/cm\({}^{2}\) and thickness of 2-5 mm, the measured average stress peak value of thermal shock wave is 1.65-0.97 GPa; for carbon phenolic targets with an average energy injection amount of 185 J/cm\({}^{2}\) and thickness of 2-5 mm, the measured average stress peak value of thermal shock wave is 1.591-1.005 GPa. By comparison, with the depth of less than 3 mm, the stress of the aluminum target is larger than that of the carbon phenolic target, i.e., the stress of the former is attenuating more rapidly than that of the latter. (4) It can be seen from the measured waveform that the electron beam thermal shock waves can be regarded as a triangle wave.
## 2 Overview of the Healthcare BCI System  
The proposed system is composed of a switching mode and a function selection mode. When the system operates in an online scenario, a small LED lamp located beside the computer keeps flashing (flickering) at a specific frequency in this switching mode, as illustrated in Figure 1. Also, the user's EEG is recorded continuously, and the healthcare function selection panel in the computer screen remains dark (see Figure 2a). The flickering LED lamp serves as a source that provides the user with a continuously flickering stimulus. This stimulus can induce SSVEPs in the user's EEG as long as the user keeps gazing at the LED lamp for a few seconds. Therefore, if the user wants to open the function selection mode, he/she simply needs to gaze at the flickering LED lamp until the function selection mode is turned on. The function selection mode is activated once an SSVEP is detected in the ongoing EEG.  
Figure 1: Overview of the proposed healthcare brain-computer interface (BCI) system.  
The function selection mode allows the user to choose the function that he/she wants to activate. In this mode, there is a 3-s focus redirection period followed by an online EEG epoch acquisition period. During the focus redirection period, the whole healthcare function selection panel is intensified, as shown in Figure 2b, and the user needs to redirect his/her focus to the element that he/she wants to choose, _i.e._, the target element within the 3 s. During the online EEG epoch acquisition period, the rows and the columns of the panel are intensified in a random order, one column or one row at a time. Each row or column intensification triggers an EEG epoch in a time-lock fashion. Figure 2c shows that the second row of the panel is being intensified. After performing a number of rounds of row-column intensifications, the average EEG epochs over rounds are fed into the P300 detector for P300 detection, one at a time. Finally, the BCI system determines the target element according to the P300 detection results, and activates the function that the target element represents. All the periods/procedures mentioned above, including EEG processing, SSVEP detection, offline P300 detector training and online target element detection, are introduced in details in Section 3.