esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 2 Materials and Methods
### Multiscale Entropy (MSE) Algorithm  
MSE is a widely used complexity measure for physiologic signals. MSE measures the irregularity of a time series under different time scales. MSE was proposed as a measure of the complexity of a physiologic time series. It aims to reflect the degree of healthiness of a biological system through its output physiologic signals [14; 15; 16]. The algorithm of the MSE analysis method of a time series consists of three steps [5; 6]. The first step of MSE method is to divide a time series \(\{x_{i}\}\) into \(N/\tau\) consecutive and non-overlapping windows \(w_{j}^{(\tau)}\) with each of equal length \(\tau\). The second step is to calculate the average value of the \(\tau\) elements in each window according to  
\[y_{j}^{(\tau)}=\frac{1}{\tau}\sum_{i=(j-1)\tau+1}^{j\tau}x_{i},\quad 1\leq j \leq N/\tau. \tag{1}\]  
Thus, all the average values of the \(N/\tau\) windows form a new coarse-grained time series \(\{y_{j}^{(\tau)}\}\).  
The third step is to calculate the MSE value of the original series \(\{x_{i}\}\) as the sample entropy of the coarse-grained time series \(\{y_{j}^{(\tau)}\}\) as follows. Consider the \(m\)-length vectors \(u_{m}(p)=\{y_{p}^{(\tau)},y_{p+1}^{(\tau)},\dots,y_{p+m-1}^{(\tau)}\}\), where \(1\leq p\leq N/\tau-m+1\). Another vector \(u_{m}(q\
eq p)\) is considered similar to \(u_{m}(p)\) if the following equation is satisfied  
\[max\{|\ y_{p+k}^{(\tau)}-y_{q+k}^{(\tau)}|:0\leq k\leq m-1\}\leq r \tag{2}\]  
The parameter \(m\) is the sequence length, and the parameter \(r\) is the similarity criterion. Then, set \(n_{p}^{m}(r)\) as the number of vectors \(u_{m}(q\
eq p)\), which are similar to the template vector \(u_{m}(p)\). The MSE value of the original time series \(\{x_{i}\}\) is given by  
\[MSE(\tau)=ln\frac{\sum_{p}^{N-m}n_{p}^{m}}{\sum_{p}^{N-m}n_{p}^{m+1}} \tag{3}\]  
The above equation can be considered as the irregularity of the coarse-grained time series \(\{y_{j}^{(\tau)}\}\), which is equal to the negative of the natural logarithm of the conditional probability that sequences close to each other for \(m\) consecutive data points will also be close to each other when one more point is added to each sequence [15]. The best settings of the parameters \((m,r)are(2,0.15)\) in analyzing theEEG signals from the AD and healthy subjects [4]. Thus, the same settings of \(m=2\) and \(r=0.15\) were used in the following analysis.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Screw Type** & **Method 1** & **Method 2** & **Mann–Whitney \(U\)** & **Z** & **Adjusted \(p\) Value** & \(r\)** \\ \hline \multirow{4}{*}{Spinelock} & \multirow{4}{*}{PROBE} & CLIPS & 366.500 & 6.094 & \textless{}0.001 & 0.609 \\  & & WRAP & 149.000 & 7.595 & \textless{}0.001 & 0.760 \\  & & CAPSULE & 52.000 & 8.273 & \textless{}0.001 & 0.827 \\ \cline{2-6}  & \multirow{2}{*}{CLIPS} & WRAP & 1039.500 & 1.452 & 0.882 & 0.145 \\  & & CAPSULE & 0.000 & 8.627 & \textless{}0.001 & 0.863 \\ \cline{2-6}  & & WRAP & CAPSULE & 0.000 & 8.628 & \textless{}0.001 & 0.863 \\ \hline \multirow{4}{*}{Spinecall} & \multirow{4}{*}{PROBE} & CLIPS & 352.50 & 6.191 & \textless{}0.001 & 0.619 \\  & & WRAP & 851.00 & 2.752 & 0.036 & 0.275 \\  & & CAPSULE & 43.00 & 8.325 & \textless{}0.001 & 0.833 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{CLIPS} & WRAP & 213.50 & 7.147 & \textless{}0.001 & 0.715 \\  & & CAPSULE & 9.50 & 8.554 & \textless{}0.001 & 0.855 \\ \cline{1-1} \cline{2-6}  & & WRAP & CAPSULE & 89.50 & 8.002 & \textless{}0.001 & 0.800 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the statistical comparison of electrical resistance and resistivity obtained from each coupling method. from CAPSULE (\(p=0.019\)) where no difference between screws was identified, although presenting a small effect size. A graphical representation of the electrical resistivity values and differences between methods can be observed in Figure 4.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 5 Results
### Clustering Functionality Testing  
Cluster analysis in this development stage serves to visualize the groups of pedestrians (clusters). Figure 6 shows the use of the K-means algorithm for separating data points into three individual clusters. This has been achieved by choosing its parameter K value of 3. The algorithm performed as expected given the input data and parameters. Additionally, density-based algorithms DBSCAN, OPTICS, and K-means were tested. The first two proved capable of successfully clustering data of the non-circular shape. To show this, a scenario similar to the one shown in Section 3.3 was established. This can be seen in Figure 10, where two parallel streets see separate pedestrian clustering. Density-based clustering algorithms are capable of determining the correct locations of cluster centers, as indicated by the blue markers. Additionally, the positions of data points have accurately reflected the actual geographical locations of detected people. However, as for the K-means algorithm, the algorithm failed to separate these data correctly. This is also depicted in Figure 10.
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 2 Relaxed SSNM Conditions: Background and Choice of Source Degenerative Inductors
Additionally, adding a mismatch does not affect NF, which depends on the source reflection coefficient (\(\Gamma_{\text{S}}\)), while mismatch at both ports is controlled only by the load reflection coefficient (\(\Gamma_{\text{L}}\)). Thus, a relaxed SSNM grants designers more freedom to assign L\({}_{\text{S}}\) per transistor rather than being locked into strict inter-stage relationships. Relaxed SSNM conditions for a multistage amplifier can be summarised as  
1. Input external port: IM\({}_{1}\
eq 0\)
2. Interstage: OM\({}_{\text{n}}\) = IM\({}_{\text{n+1}}\)
3. Output external port: OM\({}_{\text{n}}\
eq 0\)  
Here, external mismatch must lie in the \"good\" (0-0.1) or \"acceptable\" (0.1-0.25) mismatch range. Even with added mismatch, the chart-based method offers little guidance for wide-band circuitry, although it is an excellent tool for initial L\({}_{\text{S}}\) selection. In later sections, we will combine the tool with the relaxed SSNM rule to realise a wideband LNA. The next part of the paper includes developing a frequency-bounded envelope of mismatch regions on the Smith chart and defining load terminations that yield the desired mismatch combinations. This strategy will also reduce the blind dependency on software-based optimisation to obtain a wide operating band. Figure 3: IM-OM vs. L\({}_{\text{S}}\) variation (comprising the same data as in Figure 2) with an introduction of a small mismatch (X\({}^{\prime}\)-Y\({}^{\prime}\): 0.1, X\({}^{\prime\prime}\)-Y\({}^{\prime\prime}\): 0.2 mismatch).
## 4 Network Model
### The mmWave Bands  
Due to spectrum scarcity in the legacy spectrum below 3 GHz, the 5G system will extend its frequency to the mm-wave bands, particularly from 20 to 90 GHz, because there is a vast amount of unexploited bandwidth. This step is revolutionary because of the very different propagation rules, severe atmospheric attenuation and hardware constraints that characterise mm-wave communication. These challenges, however, can be overcome by using beamforming and larger scale antenna arrays.  
Figure 4: Network model, where mmWave DBS network, with multiple RRUs that connect different type of sensors, MTC devices (red circles) and dashed lines represent the fronthaul links.  
It is generally accepted that the mm-wave communication is better used with small-cell radius below 200 m, in order to reduce the high path loss and atmospheric attenuation (by making APs closer to the users). Fortunately, this action of using dense mm-wave hotspots fits with the trend of current network densification by small-cells [26; 27; 28].  
As per the Federal Communications Commission (FCC), International Telecommunication Union (ITU) and the UK Office of Communications (Ofcom), released the many bandwidths at mmWave band for point-to-point communications and broadband wireless systems with light license [29]. This band includes: 500 MHz at 26 GHz band in the UK, LMDS band from 28 to 30 GHz and 38 GHz, the license-free band at 60 GHz and the E-band at 71-76 GHz, 81-86 GHz and 92-95 GHz [26]. The mmWave node has three sectors with directional antennas patterns:  
\[A(\theta)=-min\left[12\left(\frac{\theta}{\theta_{\rm 3 dB}}\right)^{2},\ A_{m} \right]\ \text{where}\ -180\leq\theta\leq 180 \tag{1}\]  
where \(\theta_{\rm 3 dB}\) is the 3 dB horizontal beam width that is equals to 65 degree, \(\theta\) ranged from \(-180\) to 180 degree and \(A_{m}\) is the maximum attenuation that's equal to 20 dB [25]. All MTC devices are equipped with omnidirectional antennas with 0 dB gain. The network model parameters used in the simulation are shown in Table 2.
## 4 Results and Findings
### Validation and Verification  
The information inferred from the results of the survey are reflected within the literature review section. There is sufficient evidence to the effect that there is little capacity among locals in Saudi Arabia to deal with issues of technology. The fact that a majority of the people driving the agenda regarding the future of technology in the country are foreigners portend significant risks for the country. The proportion of local persons influencing technological advancements in the country are limited to advisory and oversight services that may lack the technical capacity to deal with issues of cybersecurity. An over-reliance on foreigners to drive different technologies in the country also place the Saudi Arabia at a risk of losing essential intellectual property rights to competing nations and thereby reduce their ability to defend themselves. There is a necessity for the gradual improvement of the cybersecurity framework of Saudi Arabia with the view of giving the government full control of its e-government assets.
## 2 Literature Review
### Gaps in Knowledge and Research Objectives  
Even though there has been some work in integrating process mining and DES for construction operations, no previous effort presented a complete methodological framework to implement the integrated system for real-world applications. Moreover, previous studies validated the limited framework using simulated data, which can pose an additional challenge upon implementation as real-world activities contain more complexities and uncertainties. Also, there is a gap in how to further use the simulation modes using the most up-to-date data from the field. A dynamic estimation framework is missing which uses the most recent field data and near-time simulation modeling. Considering these gaps, the goal of this paper is to develop a framework for dynamic productivity estimation for heavy civil operations by using process mining, DES modeling, and Bayesian updating techniques. The specific research objectives undertaken in this paper are:  
1. Utilize event logs from the real-life construction operation to discover the process and create a DES simulation model.
2. Create a continuous updating mechanism using the Bayesian updating method for dynamic productivity estimation.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion
### Effect of Stabilizers on Standard Proctors  
For optimum combinations of molasses and cement with clay soil, standard proctor tests were performed in order to determine the MDD (maximum dry density) and OMC (optimum moisture content) of the soil during this research. The MDD and OMC of clayey soil were found to be 86.2 \(\text{lb}/\text{ft}^{3}\) and 24.5 percent, respectively (Figure 8). Standard Proctor tests were conducted on both the native subgrade soils and the treated/stabilized soils following ASTM D-1557 [28]. Figure 9 presents a comparison of the compaction curves for the native subgrade soils and those treated with the specified stabilizers.  
Soils treated with molasses exhibit a characteristic bell-shaped curve in their moisture-density relationship, as shown in Figure 9. These curves shift to the left compared to untreated soils, indicating that molasses treatment increases the maximum dry density (MDD) and reduces the optimum moisture content (OMC). This observation aligns with the findings of [PERSON] [29]. The increase in density can be attributed to the positive charge of molasses, which is readily attracted to the negatively charged surfaces of clay minerals. The adhesive properties of molasses further enhance its attraction to soil particles, binding them together. [PERSON] [13] also reported that the addition of cane molasses to clay soil reduced the clay content.  
Soils treated with cement display the usual bell-shaped curve in the moisture-density relationship, although these curves shifted to the left compared to untreated samples, as shown in Figure 9. The reduction in density might be due to the flocculation and agglomeration of clay particles, which occupy more space, resulting in a lower dry density. This observation is consistent with the effect of cement on the specific gravity of soils mixed with various cement concentrations, as reported by [PERSON] et al. [30].  
Soils treated with a combination of molasses and cement exhibit the typical bell-shaped curve in the moisture-density relationship, although these curves shifted leftward compared to untreated soils. The increase in density could be linked to the reorganization  
Figure 7: Plasticity Index results with constant molasses content and varying cement percentages.  
of flocculated and agglomerated clay particles into a more compact arrangement due to the higher amount of plastic paste in soil-cement-molasses mixtures compared to soil-cement mixtures (Roshan) [31]. Additionally, the enlargement of clay particles transformed the fine soil into coarse soil, which may explain the reduction in optimum moisture content (OMC).
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 2 Materials and methods
### Data Sources  
The data used in this research (Table 1) were pre-processed to meet the accuracy requirements of the research and for consistency between different data sources. MODIS reprojection tool (MRT) was used to pre-process the NDVI data. Analysis included reprojection, stitching, and format conversion, while the original HDF format was transformed into the ArcGIS editable TIFF file, and its coordinate system and projection were defined. Furthermore, MATLAB was used to remove outliers through the maximum value synthesis method to obtain the optimal NDVI raster. The meteorological data was interpolated to monthly scale spatial data using Albers projection and Kriging interpolation, before being synthesized into annual precipitation and annual mean temperature spatial data at the monthly scale using Raster Calculator. Finally, the data were masked using the administrative boundaries of the Inner Mongolia region.  
We also used livestock data from 2010 [59], gross domestic product (GDP) data, and human population (POP) data to represent different anthropogenic factors. Specific information is shown in Table 1 and Figure 2.
# Flexible-Imaging-Fiber-Guided Intrratracheal Intubation in Rodents
###### Abstract
Drug injection through inhalation is widely used in research on inhaled substances, as no animal sedation or anesthesia is required [12]. However, this requires expensive equipment and has the disadvantage of low mass-transfer efficiency [13; 14]. It is difficult to accurately control and determine the amount delivered to the lungs because the inhaled material is deposited in the nasopharynx during respiration, which is different from the human oral structure. Moreover, rodent models inside the chamber are under significant stress during administration due to limited food and water supply. Intratracheal intubation usually involves direct introduction into the airway. Tracheostomy is a surgical procedure in which the trachea is cut, and the substance is administered directly to the lungs through the airway. This is commonly used for pulmonary delivery in rodent models because of its minimal drug loss and high delivery efficiency upon intratracheal administration [15]. Tracheostomy is a surgical procedure that creates an opening in the trachea to facilitate breathing or to allow direct access to the respiratory system for drug or toxin administration. The use of tracheostomy for drug or toxin administration has advantages and disadvantages that must be carefully evaluated. The benefits of tracheostomy are as follows. Tracheostomy allows direct access to the respiratory system, which is particularly useful for drug or toxin administration targeting the lungs. This method ensures that the substance of interest reaches the lungs efficiently and rapidly, bypassing other organs and tissues that may otherwise metabolize or eliminate it before reaching the target site. It allows for precise dosing of the administered substance,as it can be directly delivered to the respiratory system without any loss or degradation. It can reduce the risk of systemic toxicity associated with some drugs or toxins by avoiding exposure to other organs and tissues, thus minimizing unwanted side effects [16]. However, tracheostomy is a surgical procedure that carries some risk, particularly in rodents, and its disadvantages include high side effects for microsurgery and repetitive surgeries. There is a risk of bleeding, infection, or other complications associated with the procedure, which could impact the animals' well-being and affect the study results. It can cause respiratory distress in rodents, particularly in the early postoperative period. This distress can affect the animals' behavior, physiology, and overall health, potentially confounding the study results. This paper describes an imaging-based intubation system through which the desired animal model can easily be implemented by visually checking the vocal cord and administering several drugs or toxins through the route. This study, therefore, provides information on the procedure and system for injecting drugs directly into the lungs of rodent models.
## 5 Use Case: IWSN Resilience Using Maintenance 5.0
### Applying Novel Maintenance 5.0 to Previos Study
#### 5.2.1 Manufacturing Level  
At a manufacturing level, as a means of training and improving worker skills for IWSNs security, remote experts, softbots, or AI systems can seamlessly connect with field technicians to help get machines back online faster improving availability, which is one important factor to OEE metric.  
Manufacturing engineers could use a smart phone, smart glasses, or a head-mounted wearable, such as the HMT-1Z1, with which the other remote expert can see the environment situation and give advice or precise instructions on the best way to recover the process and continue with the production. Another possibility is the communication of an AI system with the worker to give feedback through a virtual assistant or a Telegram Bot. In an IWSN, repair and maintenance service metrics are improved by the increase in network availability (RSII) and reliability (PDR) and the decrease in Energy consumption.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 4 The Network Layer
### The Link Layer
#### 4.1.1 Precision Agriculture Communication Protocols  
There is a wide variety of networking technologies suitable for the deployment of smart farming applications. The most popular are the following:  
* The IEEE 802.15.4 standard is a widely used networking technology in precision agriculture and defines the physical layer and the Media Access Control (MAC) technique in Low-Rate Wireless Personal Area Networks (LR-WPANs).
* ZigBee is another suitable technology for short range radio communication in the fields using low-power devices capable of transmitting data over long distances using intermediate stations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Type** & **Sensor Model** \\ \hline Soil moisture sensor & 10-HS,SY-HS-220, FC-28 \\ \hline Temperature sensor & LM35, SHT15, DS18B20 \\ \hline Humidity sensor & DHT22, DHT11 \\ \hline Electric conductivity sensor & DFR0300 \\ \hline Wind speed and direction sensor & SEN0170 \\ \hline Barometric pressure sensor & BMP180 \\ \hline Carbon dioxide sensor & CDM4161A, MHZ16 \\ \hline Ph sensor & MCP1525 \\ \hline Light sensor & TSL2561, BH1750 \\ \hline Solar radiation sensor & 6450 TSR \\ \hline Thermal sensors & ThermoMAP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision agriculture sensor models.  
* LoRa is a type of wireless configuration that has been created to achieve long-range connections for Low-power Wide Area Networks (LPWANs). LoRAWAN is a protocol for managing communication between LPWAN gateways and nodes.
* Bluetooth Low Energy is a global personal area network protocol built for transmitting small data pieces infrequently at low rates with significantly low power consumption per bit.
* RFID (Radio Frequency Identification) is a different technology that uses radio signals to monitor and identify in real time objects without requiring line-of-sight communication. An RFID system includes a reader, a tag, and a host and is presented as ideal for field monitoring in multiple studies. Moreover, the communication between sensor nodes and a base station can be supported by:
* the Wi-Fi protocol, based on the IEEE 802.11 standard. This standard specifies the set of media access control (MAC) and physical layer (PHY) protocols for implementing wireless local area network (WLAN) Wi-Fi computer communication in various frequencies.
* the GSM (Global System for Mobile Communications), a standard developed by the European Telecommunications Standards Institute (ETSI) to describe the protocols for second-generation (2G) digital cellular networks used by mobile devices such as mobile phones and tablets.
* the GPRS (General Packet Radio Service) technology standard that provides rapid sending and receiving of data over the GSM mobile networks based on packet switching, a well-known network transmission process.
* the 2G, 3G and 4G (LTE) are respectively the 2 nd, 3 rd and 4 th generation of GSM technology aiming at higher speeds.  
Table 3 summarizes the communication technologies adopted in smart farming systems according to literature.