esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Organization  
The paper is organized as follows: Section 2 reflects on the related works; Section 3 consists of the details and theoretical background of the proposed analytical model; Section 4 sums up the results of the proposed model against various performance metrics, and Section 5 presents the conclusion and future scope of this research.
## 3 Method
### Wavelet Domain Spatial Selection Module  
To enhance the network's adaptive capability to select appropriate receptive field sizes for different objects, we propose the Wavelet Domain Spatial Selection Module (WDSSM). The WDSSM has the capability to select appropriate large kernels for various objects, allowing it to dynamically adjust receptive field sizes for each object based on their individual spatial requirements. Primarily, by weighting features generated by a series of large kernels in the spatial dimension, and recalibrating spatial-wise feature responses to achieve the aforementioned functionality.  
Firstly, we concatenate the features generated by each branch:  
\[\overset{\sim}{\mathrm{U}}=\mathrm{CAT}\left[\overset{\sim}{\mathrm{U}}_{1}, \overset{\sim}{\mathrm{U}}_{2}\right] \tag{11}\]  
Due to the need for weighting the feature maps in the spatial dimension, element-wise summation of features from different branches is not applicable. Subsequently, input the \(\overset{\sim}{\mathrm{U}}\) into the WDSSM, as detailed in Figure 7.  
WDSSM predominantly uses sub-bands derived from 2D DWT to capture a wide range of contextual information, because these sub-bands contain extensive features of key points and structures. Similar to WDCSM, WDSSM also discards the diagonal high-frequency component \((D)\) and aggregates \(\{A,H,V\}\) components into two distinct spatial context descriptors, effectively extracting spatial relationships. The aggregation features can be formulated as:  
\[d_{s}=\mathrm{CAT}[A,H+V] \tag{12}\]where CAT\([\cdot]\) refers to the concatenation operation; and the attention map of Wavelet Domain Spatial Selection Module can be expressed as:  
\[\hat{\mathbf{X}}=\sigma(FC(\delta(FC(d_{s})))) \tag{13}\]  
where \(FC(\cdot)\) refers to fully connected layers, \(\delta(\cdot)\) refers to the RELU function, and \(\sigma(\cdot)\) refers to the sigmoid function. The output \(\hat{\mathbf{X}}=\left[\hat{\mathbf{X}}_{1},\,\hat{\mathbf{X}}_{2}\right]\) is a spatial selection mask composed of the spatial selection masks of two separate branches. Next, the features extracted from the two large-kernel branches are weighted by their respective spatial selection masks, and feature fusion is conducted using the convolutional layer \(\mathcal{F}(\cdot)\) to generate the final attention map Att.  
\[\text{Att}=\mathcal{F}\bigg{(}\widetilde{\text{U}}_{1}\cdot\hat{\mathbf{X}}_{ 1}+\widetilde{\text{U}}_{2}\cdot\hat{\mathbf{X}}_{2}\bigg{)} \tag{14}\]  
The final output of the Wavelet Domain Selective Kernel Module is the element-wise product between the inputs X and the attention map. Figure 5 shows the detailed structure diagram of the Wavelet Domain Selective Kernel Module.  
\[\text{Y}=\text{X}\cdot\text{Att} \tag{15}\]
# Additive Manufacturing and Textiles--State-of-the-Art
## 4 Production Methods  
Fused deposition modeling (FDM) is the dominating printing technology in the last five years (2015-2019) state of 3D printing and followed by selective laser sintering (SLS) and stereo lithography (SLA), respectively [36] These are the main types of 3D printing technologies used in relation to textile context: FDM for printing onto a textile fabric, SLS for producing textile-like structures and SLA types used to form a fabric-like stiff textiles which can be rolled and seamed to shape [47; 48]. Table 1 gives brief description of them with the materials, textile products, advantages and disadvantages.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Techniques** & **Mechanism** & **Materials** & **Advantage** & **Disadvantage** \\ \hline Stereo lithography (SLA) [49] & Photopolymer resin, and an ultra-violet (UV) laser to cure and harden individual layers to form objects [21; 50]. Rigid parts and connective joints can be printed together at one time [49; 51]. & Polyethylene, polypropylene, ABS, polycarbonate casting and molding material A flexible, elastomeric material can be combined with stiff and hard polymer [34]. 3D printed textiles are stiff and not flexible [48]. & Fast printing process [49]. More & Requires support rafts, additional time, sanding and reduces the quality of the product due to sanding; expensive material and no color variety [49; 50]. \\ \hline \hline \multirow{8}{*}{Selective laser sintering (SLS).} & A computer-controlled laser traces the layer, heating the powder to just below its boiling point to fuse the particles into a solid object [49; 50]. After the first layer is created, the building platform drops, exposing the next layer of powder [53] & only use one material per model; multi-material models printed separately and joined afterwards [21; 54]. Glass, plastic, metals, ceramics, or nylon, stainless steel, titanium alloy, nickel alloy, aluminum, copper losses, bathing assets, shoes, single and double face knits [55; 56; 57; 58; 59] & Allows designers to create delicate, yet highly functional and durable products, requiring less sampling of the object than SLA [49; 60]. \\ \hline \hline \multirow{8}{*}{FDM (Fused deposition modeling).} & FDM offers a variety of low-cost desktop printers [61]. Based on heating a filament in an extruder nozzle and depositing the molten material line by line around where it hardens. The next layer is printed on top of the previous layer [62] & Wax, metals, ceramics, acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene terephthalate (PET), aramid, onyx, glass and carbon fibers are some [22; 50; 63]. Shoes, skirt, dress, jacket, soles, yarn, knit structures and printing on and with textiles [47; 55; 59; 64; 65; 66] & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A brief description of additive manufacturing techniques for 3D printing techniques for printing in the textile industry.
## 3 Results and Discussion
This is exactly the case of OP27 and OPA227, which have virtually identical voltage and current noise coefficients, but OP27 has a noticeably lower input impedance which is comparable with the QTF impedance. The TL071 amplifier shows worse results than OP42, because of a slightly higher voltage noise and a much lower gain-bandwidth product. Taking into consideration the gain of the voltage configuration, the upper corner frequency of the TL071 based voltage preamplifier is around 29 kHz, which is already below the resonance frequency of the QTF. Therefore the output amplitude for the TL071 amplifier is lower compared to other amplifiers taken into consideration in the non-inverting \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Op–Amp Model**} & \multicolumn{2}{c}{**Output Amplitude [dB]**} & \multicolumn{2}{c}{**SNR [dB]**} \\ \cline{2-5}  & **Voltage** & **Transimpedance** & **Voltage** & **Transimpedance** \\ \hline OP27 & \(-\)19.66 & \(-\)32.23 & 71.82 & 68.49 \\ OPA227 & \(-\)17.81 & \(-\)35.06 & 74.03 & 66.06 \\ TL071 & \(-\)21.01 & \(-\)35.35 & 76.73 & 66.51 \\ OP42 & \(-\)12.82 & \(-\)34.42 & 82.36 & 67.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured amplitude of the signal from the sensor and signal-to-noise ratio for the QTF preamplifiers implemented with different operational amplifier models working in transimpedance and voltage configurations. Figure 5: Comparison of the signal level and noise properties of the QTF preamplifier with different operational amplifiers working in voltage and transimpedance modes. configuration. Nevertheless, due to a 40 times lower noise current compared to OP27 and OPA227 the overall SNR performance of TL071 in the non-inverting configuration is better. One can see that the highest voltage noise of TL071 compared to other considered operational amplifiers makes it the worst solution for a transimpedance configuration. In order to check if the measurement results conform with our previous theoretical analysis presented in [6] we calculated preamplifier noise from the noise models discussed there. Certainly, it was not possible to calculate the absolute value of the preamplifier signal amplitude neither its SNR, as both these values are strongly influenced by many other factors. But assuming that the given noise models are correct, the ratio of the SNRs calculated and measured for two different op-amps working in the same configuration should be similar. To make the comparison easier we scaled up the calculated SNR values in such a way that the theoretical and measured SNRs of OP42 based preamplifier are identical. Theoretical SNRs of the remaining op-amps were scaled up accordingly. Comparison of such calculated and measured SNRs is given in Table 3.  
Although there are some differences between the theoretical evaluation and the measurements, which may result from imperfect noise models of the preamplifiers, technological tolerance of the properties used in the theoretical estimations, etc., it is quite clear that all the previous conclusions stand valid.
## 2 Materials and Methods
### Heart Rate Variability (HRV)
#### Vascular Occlusions Test (VOT)  
To assess the dynamics and kinetics of SmO\({}_{2}\) in the VL muscle, a VOT protocol was used involving a 220-250 mmHg occlusion for 3 min [33]. Before the VOT, a 1 min baseline measurement of SmO\({}_{2}\) was recorded. Following this, a manual pressure cuff (NIBP Cuff for PM900, London UK) was inflated on the distal thigh to induce ischemia for 3 min, causing visible muscle desaturation (Figure 2). After the 3 min occlusion, the cuff was released, and 3 min of reperfusion was allowed to analyze muscle resaturation and reactive hyperemia through changes in SmO\({}_{2}\)[3]. Subsequently, the participant rested for another 2 min (totaling 5 min) before repeating the VOT measurement on the other leg. VOT measurements were performed randomly on either the dominant or non-dominant leg to allow recovery of blood flow from ischemia and to avoid interference from blood flow redistribution [4]. For data analysis, the average of the two measurements from each leg was used.  
Figure 2: Visualization of vascular occlusion test. **Note.** Baseline = Average values of the 30 s before starting VOT-NIRS; Desaturation = SmO\({}_{2}\) decrease during the ischemic period (3 min); Resaturation = SmO\({}_{2}\) increase during the recovery period; Reactive hyperemia = SmO\({}_{2}\) increase compared to baseline values. Reoxygenation Kinetics = Oxygen utilization rate based on mean response time (MRT), which is equal to the sum of the delay time (DT) and the time constant (Tau).
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 4 Received Signal Strength Distribution Approximation
### Biased Received Signal Strength Distribution Approximation  
Even though the Rician RSSI distribution approximation described in the previous section may possibly fit the measured RSSI probability density, however, it would be assumed that is not an actual RSSI distribution. The measured RSSI distribution over a fading channel is more likely to experience the unobservable signal, which affects the overall RSSI distribution approximation, especially for RSSI fading near the level of receiver sensitivity. The statistics of measured RSSI data is the Rician RSSI distribution f(r). Hence, the measured RSSI data will be used to eliminate the unobservable signal by a cut-off or bias function w(r). The observable RSSI distribution f\({}_{\rm Biased}(r)\) is the Rician RSSI distribution approximation f(r) multiplied by the bias function w(r) and divided by themselves. In addition, the offset (r\({}_{0}\)) is used to adjust the starting point of RSSI distribution to an origin, and allows integration to unity for a denominator normalization of the resulting density function. The observable RSSI distribution f\({}_{\rm Biased}(r)\) is presented as,  
\[\rm f_{Biased}(r)=\frac{w(r)f(r\ -\ r_{0})}{\int_{-\infty}^{\infty}w(r^{ \prime})f(r^{\prime})dr^{\prime}} \tag{8}\]  
The bias function w(r) used to improve the empirical models is derived from the probability of a successful packet delivery, which denotes the inverse of frame error rate (FER) on Wi-SUN channel with 2 FSK PHY layer, at a given signal-to-noise ratio (SNR), to arrive at,  
\[\rm w(r)=1\ -\ \rm FER=(1\ -\ P_{2 FSK})^{M} \tag{9}\]  
where P\({}_{2 FSK}\) denotes the bit error rate (BER) and M denotes a value of bit per frame. The noise floor and noise figure of the receiver are measured in the experiment. P\({}_{2 FSK}\) of the Wi-SUN channel with various data rates is shown in Figure 2. P\({}_{2 FSK}\) with multi-data rates depends on the system data rate and the channel bandwidth, which are determined in Equation (10). Given the system data rate of R, and the channel bandwidth of B, one can derive that  
\[\rm P_{2 FSK}=\frac{1}{2}\exp\left(-\frac{E_{b}}{2N_{o}}\right)\times\frac{R}{B} \tag{10}\]
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 3 Proposed Algorithm
### Trade-Off Criterion in Fullband  
The fullband MSE of the speech and the fullband residual noise can be, respectively, given by  
\[\mathcal{J}_{s}=\sum_{k=\mathcal{K}}\sum_{l=\mathcal{L}}J_{s}\left[M_{l}\left( k\right)\right], \tag{10}\]  
and  
\[\mathcal{J}_{d}=\sum_{k=\mathcal{K}}\sum_{l=\mathcal{L}}J_{d}\left[M_{l}\left( k\right)\right]. \tag{11}\]  
The loss function without any constraints can be given by  
\[\mathcal{J}_{x}=\mathcal{J}_{s}+\mu\mathcal{J}_{d}, \tag{12}\]  
where (12) is the same as the newly proposed components loss function as given in [21].  
The loss function with residual noise control is  
\[\mathcal{J}_{x}=\mathcal{J}_{s}+\mu\mathcal{J}_{d}^{\text{con}}, \tag{13}\]  
where  
\[\mathcal{J}_{d}^{\text{con}}=\sum_{k=\mathcal{K}}\sum_{l=\mathcal{L}}\Big{|} J_{d}\left[M_{l}\left(k\right)\right]-\Re\left(\beta_{l}\left(k\right),D_{l} \left(k\right)\right)|^{2}\Big{|}.\]  
It is obvious that (13) is a generalization of (12), where (13) reduces to (12) when \(\Re\left(\beta_{l}\left(k\right),D_{l}\left(k\right)\right)|^{2}\equiv 0\). One can observe that \(\beta_{l}\left(k\right)\) is both frequency and frame-dependent, so it can control the residual noise in each time-frequency bin.
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 2 Operations Chain Model  
Generally, the operation of equipment should follow a specific operating procedure. To accomplish a particular task, it needs to be decomposed into multiple operation function modules. Each function module could be further decomposed into multiple function modules and multiple basic operation steps. Sometimes, a certain basic operation may repeat many times in a task, which leads to a situation where a basic operation may correspond to multiple function modules. Other times, there are many paths to finish a specific function module, for example, while there are two hot standby redundant devices, either can be chosen. In this circumstance, the module is considered complete as long as one of the devices is operated, although there may be several basic operations that do not finish. Thus, the relationship among these operations is \"OR\". While several operations of a function module should be completed simultaneously, the module can be considered complete. Thus, the relationship among these operations is \"AND\". Sometimes, within a function module, only one of the operations can be performed, and any other operation cannot be conducted. In this case, the operation relationship is \"MEX\", which means Mutually Exclusive. Sometimes, there is a precise sequential relationship among operations. A chain model can describe the sequential relationship.  
Therefore, the operations of equipment are composed of a series of function modules with sequential relationships, forming a linked chain. Each node on the linked chain is a structure similar to a \"tree\". The branches and leaves on the \"tree\" are composed of sub-level function modules and basic operations, with relationships such as \"OR\", \"AND\", and \"MEX\" among operations.
## 3 Results
### Optimization of Buffer Solution Amount  
The volume of buffer solution used in the extraction process is a significant factor affecting the efficiency of Hg(II) ion preconcentration. To determine the optimal buffer volume, various amounts were tested: 0.2 mL, 0.5 mL, 1 mL, 2 mL, 3.5 mL, and 5 mL. Each buffer volume amount was evaluated for its effectiveness in maintaining the desired pH and ensuring the complete extraction of Hg(II) ions. Through these experiments, it was found that using 2 mL of buffer solution provided the most effective results (Figure 3). This volume ensured that the pH was consistently maintained at the optimal level of 4.0, facilitating complete extraction of the Hg(II)-HMIMPF6 complex. Lower volumes, such as 0.2 mL and 0.5 mL, were insufficient in stabilizing the pH, leading to incomplete extraction. Conversely, higher volumes, like 5 mL, did not significantly improve extraction efficiency and were less practical for the process. Therefore, 2 mL was selected as the optimal buffer volume for subsequent experiments, ensuring reliable and consistent extraction of Hg(II) ions.  
Conditions: C\({}_{0}\): 2.5 ug/L Hg(II); pH: 4.0; ionic liquid amount: 10 uL HMIMPF6; extraction time: 20 min; extraction temperature: 26 \({}^{\circ}\)C; extraction speed: 750 rpm; added dodecanol amount: 80 uL; final volume: 150 uL.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 2 Related Works
### Tourist Support by ICT
#### 2.2.2 Mobile Phone Applications  
The number of existing smartphone applications for sightseeing is huge [27]. They work as an electric version of guidebooks and usually provide information on restaurants, souvenirs, weather forecasts, MAP, etc. In Japan, there were 666 mobile applications for sightseeing as of March 2015 [28]; 96% of them are free. However, they are not used; 69% of them were downloaded less than 1000, and 9.1% were downloaded less than 10,000. The average number of downloads of the pied application was less than 500.  
Some examples of sightseeing support applications are as follows.  
A personalized sightseeing information application is explained in [29]. An application for cultural heritage recommendation is introduces in [30]. Moreover, a platform of sightseeing application for cultural heritage is discusses in [31].
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 4 Challenges in Implementing Swarm Intelligence-Based Multi-Robotics
* Energy Efficiency: Energy efficiency is crucial for the extended operation of swarm robotic systems, especially in remote or harsh environments where recharging may not be feasible. Minimizing energy consumption includes optimizing path planning, task allocation, and communication protocols. The system must balance the energy expenditure with the mission requirements to ensure longevity and reliability. * Adaptability: Swarm robotic systems must be adaptable to dynamic environments. This involves adjusting to changes in the terrain, obstacles, environmental conditions,and unexpected events. The algorithms must enable real-time responses to new information, allowing the swarm to modify its behavior and flight strategies. In a search-and-rescue mission following a natural disaster, the environment can change rapidly due to shifting debris, weather conditions, or other factors. An adaptable swarm of robots was designed to modify its search patterns in real time based on sensor data. When an area became inaccessible due to new obstacles, the robots quickly adjusted their routes and continued searching other regions, demonstrating the importance of adaptability in dynamic environments. * Security: Security is important in swarm robotics, particularly for sensitive or hostile environment applications. Protecting the swarm from malicious attacks, such as hacking or the introduction of rogue robots, is essential. Security measures include robust encryption of communication channels, authentication protocols, and the ability to detect and isolate compromised robots. In military applications, ensuring the security of robotic swarms is vital to prevent adversaries from compromising the mission. A case study in this area involved implementing encrypted communication and multi-factor authentication for all robots in the swarm. Additionally, the system included anomaly detection algorithms that could identify and isolate any robot exhibiting suspicious behavior, such as deviating from its assigned tasks, thus maintaining the integrity of the swarm.
## 2 Materials and Methods
### Data Source
#### 2.2.2 External Elevation Data  
The SRTM (Shuttle Radar Topography Mission) project employed two SAR sensors, one C band sensor (wavelength of 5.6 cm, SRTM-C radar) and another X band sensor (wavelength of 3.1 cm, SRTM-X radar). We used the SRTM-C DEM with 1 arc second spatial resolution (about 30 m) acquired in February 2000 to generate a simulated topographic phase, which was used together with TanDEM-X bi-static InSAR data in a differential interferometric scheme to estimate glacier elevation changes [30]. Since SRTM-X radar only acquired DEMs along discrete swaths 50 km wide, an SRTM-X DEM was used to estimate radar penetration between the C band and the X band in glaciated regions. In addition, since ICESat satellite altimeter data have an accuracy on an order of 0.1 m (1 \(\sigma\)) in flat and smooth regions, ICESat elevation data were used as a height reference to correct the other DEMs in flat off-glacier areas [38].