esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 2 Related Work  
In this section, we briefly outline current developments in positioning techniques in wireless IoT networks. Then, we with introduce the related works for LoRa signal based positioning using RSSI Proximity and the path-loss model, TDoA, and the fingerprint algorithm. The positioning techniques in IoT wireless network can be roughly divided into the following two categories: Time-based positioning such as time of arrival (ToA) and TDoA, and RSSI signal based positioning such as proximity, path-loss-model, and the fingerprint algorithm.  
ToA can be measured when a signal is transmitted by an end-device and received at multiple gateways to estimate the distances from the corresponding gateways by multiplying the speed of light to the time. The location of an end-device can be calculated by the trilateration approach with the distances. TDoA can estimate the location by measuring the time differences at multiple pairs of gateways. To accomplish this, precise clocks at all gateways are required.  
The proximity method estimates the location of an end-device by using the known location of the gateway; however, the positioning accuracy depends on the density of gateways in the network [10]. The path-loss model estimates the location of an end-device by mathematically formulating the characterization of radio wave propagation as a function of distance and other conditions. The fingerprint algorithm, also known as scene analysis, estimates the location of an end-device by matching the observed RSSI with the radio map containing RSSI data using a pattern matching method [10]. Usually, probabilistic methods, such as maximum likelihood estimator (MLE), are used for estimating the location [11; 12]. The main drawback of the fingerprint algorithm is the need to generate radio map information for a target area and updating the radio map is also an issue.  
These RSSI signal based approach and time-based approach can be combined together. [PERSON] studied a hybrid approach that fused RSSI and TDoA methods for the narrow-band ZigBee signal [13]. He recommended the fusion KNN-RF algorithm for the narrow-band LPWA signal such as LoRa and NB-IoT.
## 3 Modulation Signal Generation  
### Signal Model  
This paper was centered on identifying the impacts of fading channel conditions on communication signals. The factors that affect the received signal comprise carrier frequency offset (CFO), sampling rate offset (SRO), multipath fading, and Additive White Gaussian Noise (AWGN). At the transmitter side, the input information \(a(t)\) is modulated to obtain a modulated signal \(s(t)\), and this modulated signal is affected by fading and noise during transmission. At the receiving end, the received signal \(x(t)\) is represented as follows.  
\[x(t)=h(t)*s(t)+n(t)=I(t)+jQ(t) \tag{1}\]  
where \(h(t)\) is the impulse response function of the channel, and \(n(t)\) denotes additive Gaussian white noise.  
When the communication signal has a modulation type of PSK or FSK, it can be expressed as Equation (2).  
\[s(t)=\left[A_{0}\sum_{n}a_{n}g(t-nT_{s})\right]\cos(2\pi f_{c}t+\phi_{0}+\phi_{m}) \tag{2}\]  
where \(A_{0}\) represents the modulation amplitude, while \(a_{n}\) stands for the transmit codeword sequence. Additionally, \(g_{t}\) is the random pulse sequence, \(T_{s}\) is the sampling period, \(f_{c}\) denotes the carrier frequency of the modulation, and \(\phi_{0}\) is the initial phase and \(\phi_{m}\) is the modulation phase, respectively.  
MQAM signals differ slightly from FSK and PSK signals in that they have two orthogonal carriers, each of which independently modulates the amplitude of the signal with Equation (3).  
\[\begin{split} s(t)=&\Bigg{[}A_{0}\sum_{n}a_{n}g(t- nT_{s})\Bigg{]}\cos(2\pi f_{c}t+\phi_{0})\\ &+\Bigg{[}A_{0}\sum_{n}b_{n}g(t-nT_{s})\Bigg{]}\sin(2\pi f_{c}t+ \phi_{0}),\end{split} \tag{3}\]  
For analog modulation AM and FM, the expressions are given as  
\[s_{\text{AM}}(t)=A_{0}(1+\mu\cdot m(t))\cos(2\pi f_{c}t+\phi_{0}) \tag{4}\]  
\[s_{\text{FM}}(t)=A_{0}[2\pi f_{c}t+K_{F}\int_{-\infty}^{t}m(\tau)d\tau+\phi_{0}] \tag{5}\]  
where \(m(t)\) is the modulation signal, \(\mu\) is the AM index, \(\phi_{0}\) is the initial phase, and \(K_{F}\) is the FM sensitivity. DSB-AM and SSB-AM signals can be obtained by filtering the AM signal [30].
## 2 Control Fundamentals
### Zero Power Flow Concept  
Figure 1 shows an inverter connected to the grid with the resonant load type proposed in the mentioned AI standards [7; 11; 12]. They suggest that the concept of resonant load take place when the local consumption is nearly equal to the active power given by the inverter in terms of current, considering no reactive power, but with a resonance between the reactive components equal to the frequency of the grid. On the one hand, the active part is modeled by a lumped resistance. On the other hand, an inductance is parallelized with a capacitance to achieve the resonance frequency.  
It should be highlighted that when the load is under resonance, there is no reactive consumption. Note that the resonance concept is linked to a voltage driven from the current delivered by the inverter. This conventional resonant concept is limited and less significant for VC-VSI where the voltage at the PCC is explicitly controlled by the inverter. In other words, this is due to the voltage and frequency being maintained by the control law of the inverter. Thus, when using VC-VSIs, the voltage loop stunts the mains loss detection. Note that these situations are possible even though the PCC power flow is nonzero. When the mains are lost, the voltage of the PCC, \(U_{PCC}\), is not released and becomes conditioned by the inverter. When the grid is off, the grid inertia is free, and \(U_{PCC}\) becomes indirectly controlled by the inverter. As can be seen in Figure 1a, the AC capacitor voltage \(U_{C}\) and the \(U_{PCC}\) voltage are connected only through the output coupling impedance constituted by the inductance \(L_{2}\) and its equivalent series resistance \(R_{2}\), which are usually small in value.  
According to the exposed arguments, it is appropriate to reconsider the resonant load concept. The resonant load purpose is to be the worst scenario for the AI methods based on voltage and frequency displacements (AI-PM (Passive Methods)and AI-PF (Positive Feedback)), refer to Section 3 for better understanding. Thus, the worst scenario occurs assuming the power flow through the PCC  
Figure 1: Control topology in grid-connected mode with a resonant local load. (**a**) Voltage-controlled voltage source inverter (VC-VSI) topology. (**b**) Current-controlled voltage source inverter (CC-VSI) topology. PCC, point of common coupling; PLL, phase locked loop.  
(active and passive) to be about zero [28; 30]. Accordingly, the conception of zero (ZPF) or non-zero power flow (NZPF) exposed points out the resonant condition.
## 4 Proposed Scheme  
To extend the lifetime of multi-level HWSN-based IoT networks, the primary objective of the proposed scheme is to minimise energy consumption during intra-cluster communication between the sensor nodes and cluster head. Furthermore, the scheme aims to enhance network stability in the presence of heterogeneous nodes. Here, stability is quantified by the disparity between the total number of data transmission operations accomplished before the first node failure and the total number completed before the last node failure. This can be accomplished through an appropriate cluster-head rotation algorithm that considers all the heterogeneous characteristics of nodes, ensuring minimal energy consumption and maximizing intra-cluster stability. Therefore, the cluster-head rotation that achieves stable operation while maintaining minimal intra-cluster energy consumption for a network of a multi-level heterogeneous network is discussed in following section.
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
N2N [12] requires two datasets that have independent zero-mean noise, and have been verified to improve the PET image quality to a similar extent compared with supervised methods (e.g., Noise2 Clean) [13; 14]. N2V [15] employs a noisy dataset masked by a blind-plot network as the input and the same original dataset as the corresponding target, and has been applied for simulated as well as public brain PET data [16]. Notably, the majority of deep learning denoising methods are developed for FDG PET, whose images reflect basic anatomical structures and can be corrected with MRI, while CHO PET has a lower background activity in almost all normal brain structures and highlight the metabolic tumors. Therefore, networks trained by FDG PET images prior are not applicable for CHO PET and an alternative denoising algorithm is required. This study developed a deep learning-based denoising algorithm for brain tumor CHO-PET that exhibits sufficient performance while preserving the detail of lesions. The method was explored and validated without a paired dataset, aiming to expand its generalizability for abundant retrospective images.
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 4 Corrosion Fatigue Lifespan Assessment of Parallel Wire Cable
### Measured Stress of Bridge Cable  
The Runyang Suspension Bridge consists of several sections including the northern approach, the northern bank bridge, the Shiyezhou interchange viaduct, the southern bank bridge, the southern approach, and the extension section. The bridge is located downstream of the Yangtze River, which is subject to high temperatures and humidity throughout the year. Therefore, evaluating its corrosion fatigue performance is of great significance. In this study, the case study refers to the southern bank bridge, which is a single-hole double-cable steel box-girder bridge. The total span of the bridge is 470 + 1490 + 470 m (see Figure 12). The box girders are fabricated by full welding with a streamlined and flat shape, the cross-section has a height of 3 m and a width of 36.3 m. The cable system consists of two main cables and eighty-eighty pairs of hangers. The upper end of the hanger is connected to the main cable via a cable belt, while the lower end is connected to the box girder through an ear plate. In the mid-span, a pair of rigid central clamps is applied to connect the girder to the main cables. The hangers are composed of parallel steel wire ropes, consisting of 109 galvanized high-strength parallel steel wires. The steel wire has a diameter of 5 mm, and the ultimate strength required is not less than 1670 MPa.  
The suspension cables on each side of the main girders are numbered from north (Yangzhou) to south (Zhenjiang) as No. 1-45, No. 46, and No. 47-91. The No. 46 cable is the central buckle. The spacing between the cables along the bridge is 16.1 m. The suspension cables are made up of 109 galvanized high-strength steel wires with a diameter of 5 mm. The steel wire is required to have a strength of no less than 1670 MPa. To obtain the stress-time history of the suspension cables, displacement sensors were used to measure  
Figure 12: Runyang Suspension Bridge Sketch (Unit mm).  
the axial deformations of cables. Figure 13 shows the field diagram of the measuring of No. 46 cable. The data acquisition frequency was set to 200 Hz.  
The axial stress \(\Delta\sigma\) is calculated using the following equation.  
\[\Delta\sigma=\frac{\Delta l}{l}E \tag{28}\]  
where \(\Delta l\) is the axial deformation of the cable, \(l\) is the length of the cable and \(E\) is the Young's modulus. Figure 14 presents the processed stress time spectrum, where the extraction time is selected as 500 s.  
Figure 14: Typical stress time spectrum of cable.  
Figure 13: Field diagram of cable stress measurement.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 5 Efficient Depth-Based MAC Protocol
### Handling Newcomers  
In this sub-section, we introduce the concept of the newcomer sensor nodes and how ED-MAC protocol can deal with them. A newcomer sensor is a node that joins the network during the operational phase, which needs to be scheduled for transmitting and receiving data packets. This procedure can be made by letting the newcomers listening to their one-hop neighbouring nodes. The newcomer node can then select the first available slot and inform its neighbouring nodes by sending a control message. This message contains the available slot chosen by the newcomer node during the listening mode.  
Due to an inability of nodes to hear from more than one-hop neighbouring nodes, a newcomer may select the same slot as the two-hop neighbours; however, the random selected sub-slots can be different to avoid collision. Therefore, the concept of the sub-slots is not only designed for a very specific collision scenario but also for handling newcomer nodes.  
Figure 6: Network schedule between node \(A\) and node \(L\).  
The topology in Figure 7 can be used to describe how the ED-MAC protocol handles newcomer sensor nodes in detail. In this figure, for instance, and by assuming that node \(M\) joins the network, where node \(E\) becomes its one-hop neighbouring node. Upon joining the network, it switches to listening mode until it hears from its one-hop neighbouring nodes. Thereafter, \(M\) broadcasts a _Hello_ message back to its neighbourhoods to inform them about its transmission slot that selected during the listening duration. If this slot interferes with other neighbouring nodes of node \(E\), which are considered as two-hop neighbours of node \(M\), such that nodes \(C\), \(F\) and \(G\), the random selection of sub-slots can avoid the probability of collisions. For example, by assuming that node \(M\), as a newcomer node, selects the same slot of node \(C\), which is two hops away from each others and cannot be known by node \(M\) during the listening time. In this case, both \(M\) and \(C\) nodes use the same slot to transmit their packets to node \(E\) as a reception node. Consequently, transmitting a packet by nodes \(C\) and \(M\) may result in a collision at node \(E\). Hence, \(C\) and \(M\) nodes select a random sub-slot out of those available to send their packets. In this way, nodes \(M\) and \(C\) can transmit their packets after each others by using two different sub-slots with no chance of collision.  
Whenever a node transmits its packet, it will then be in a listening mode for the next sub-slot to receive a _Hello_ message from newcomer sensor nodes, if there are any, as illustrated in Figure 8.  
Figure 7: An example of how a newcomer, node \(M\), joins the underwater network.
## 3 Methodology
### Analysis  
The CTS and CTS-SR algorithms proposed in Sections 3.3 and 3.4 introduce the concept of unexpected popularity based on the Bayesian Truth Serum theory. The former integrates the confidence levels of the classifiers and decides whether to follow the majority or minority results by comparing the prior and posterior probabilities of the event itself.  
Similarly, CTS-SR also emphasizes confidence as a critical metric, fully considering the answers of expert classifiers with specialized knowledge in scenarios with high decision difficulty, making it more aligned with real-world prediction scenarios. Both methods break through the traditional strategies that focus on majority results, thoroughly considering the expertise and confidence levels of decision makers, and constructing a more comprehensive decision-making process and scenario, thereby enabling a more integrated and detailed judgment of unknown events. However, the proposed algorithms still have certain limitations. As the calculation of relevant parameters during training and prediction mostly relies on the confidence levels of the classifiers, defining and computing confidence poses a challenge and may result in increased time overhead. Additionally, when the classification results of all classifiers are highly consistent, CTS may struggle to recover unexpectedly correct answers, which requires further extensive research.
# Estimating Hardness of Cosmetic Cream Using Electro-Mechanical Impedance Sensing Technique
## 1 Introduction
In the EMI sensing technique, the impedance of the piezoelectric sensor shows a corresponding change depending on the mechanical properties of the material surrounding the piezoelectric sensor, and the mechanical properties of the material can be inferred through the impedance value of the piezoelectric sensor. When cementitious materials react with water, they initially remain in a fluid state and then undergo a phase transition to a solid state over a certain period of time. In studies conducted by [PERSON] et al. [20; 21; 22; 23], the piezoelectric sensors were used to continuously monitor EMI signals over the hydration time to accurately evaluate the stiffness at the moment of the phase transition from the fluid to the solid state. In this context, assessing the mechanical properties of creams in cosmetics that remain in a fluid state may be simpler than assessing the mechanical properties of cementitious materials undergoing phase transitions. Therefore, it is believed that inferring the hardness of a cosmetic cream by embedding a piezoelectric sensor in the cosmetic cream and measuring the impedance of the piezoelectric sensor will be possible. In this study, the hardness of cosmetic cream was estimated using the EMI sensing technique. Through the embedding of a piezoelectric sensor in the cosmetic cream, we measured the EMI signal of the piezoelectric sensor. Subsequent analysis was conducted to establish a correlation between the EMI sensing technique and the existing hardness test method, specifically the penetration resistance test using a texture analyzer.
## 1 Introduction
### Subjective Thermal Comfort: Affecting Factors and Assessments  
Subjective assessment tools, such as the Predicted Mean Vote (PMV) and Thermal Sensation Vote (TSV), are important in the study of thermal comfort by capturing individual perceptions and experiences. The subjective assessment of thermal comfort in sedentary participants has been explored through scoping reviews that categorize studies based on parameters such as thermal sensation and comfort, effects of fatigue, and other related aspects [14]. Some other subjective tools, such as the \"positive and negative affect schedule\", are used to assess emotional responses to thermal environments, highlighting the psychological dimensions of thermal comfort [29].  
Thermal comfort is fundamentally defined as a state of mind that expresses satisfaction with the thermal environment, as outlined in ISO 7730 [30], which is inherently subjective and varies significantly among individuals owing to differences in metabolism, acclimatization, habits, and expectations. The subjectivity of thermal comfort makes it challenging to create a universally satisfactory thermal environment [4] and testing methods. Subjective evaluations may include scales for body moisture, comfort, and sweat levels, with participants providing feedback at regular intervals during the experiments [8; 29]. In some studies, subjective data were collected through comfort/discomfort questionnaires, which were converted into quantitative indices using scales ranging from high discomfort to high comfort [28]. [PERSON]'s model, which includes the PMV-PPD index, provides a quantitative approach to evaluating indoor thermal environments [31]. In contrast, the TSV, which uses a 7-point scale ranging from cold to hot, is employed to directly capture individuals' thermal sensations and comfort levels in various settings, including field studies and experimental conditions [29]. This scale allows for a more immediate and personal assessment of thermal comfort, reflecting the subjective nature of thermal perception.  
Thermal comfort assessment when sitting on office chairs is a complex and intricate process that largely depends on the worker's subjective assessment and numerous other physical and psychological parameters.  
The objectives of this study were to investigate whether and what the impact of different designs of office chair seats and backrests is on participants' assessment of differences in thermal sensations at their workplace. This study took into account the office climate and wearing clothes made of natural materials, whereby the thermal characteristics of chairs and clothing, temperature fluctuations in the room, the mood of the subjects, and their daily habits are excluded.  
To achieve this, a survey of subjective assessments of thermal comfort was conducted using a questionnaire with a seven-point scale.
## 4 Evaluation Criteria and Experimental Results
### Ensemble Classifier (Stacking Generalization)  
The performance evaluation of the proposed ensemble classifier model for the purpose of aspect sentiment analysis relies on two parts: (1) making a comparison between the proposed model and the baseline classifiers in addition to another ensemble method on three different domains; (2) evaluating the performance and accuracy of the proposed model on three different domains.  
Tables 6 and 7 illustrate the comparison between the proposed model and the baseline classifiers as well as three other different ensemble methods including bagging, adaboost and majority voting for the selected domains.  
As outlined in Tables 6 and 7, the proposed model has scored better results compared to the baseline classifiers and other ensemble classifier methods, with an accuracy level of 81.2%, precision of 81.1%, recall of 80.4%, and F1-scores of 81%. The lowest accuracy performance of other ensemble methods is for'majority voting' with 77.5%. The lowest accuracy performance of the baseline classifier