esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Analysis of Narrowband IoT Quality of Service
## 4 Results
### PHY Measurements: Transmission Power  
This measurement analyzed the transmission power \(P_{TX,UE}\) selected by the modem in different coverage situations. Accordingly, conclusions could be drawn on the signal level needed for output power reduction. Various signal levels were simulated in 5 dB steps using the attenuator, and 30 packets were sent for each step in uplink and downlink direction. For each packet, \(P_{TX,UE}\), as well as the RSRP signal level were recorded.  
Figure 8 shows the transmission power \(P_{TX,UE}\) of the NB-IoT UE at different signal levels. For RSRP levels below \(-102\;\mathrm{dBm}\), the maximum cell-specific output power of \(P_{CMAX,c}\) = 23 dBm was used. Above this level, the UE reduced its output power in 1 dB steps. This technique conserved power for battery operated devices, while maintaining a reasonable performance at high received signal levels. The power reduction depended on the estimated coupling loss, which was calculated using the current RSRP value [18]. Overall, this measurement indicated that an improvement in received signal level (e.g., by installing an external antenna) could significantly improve battery life.  
Figure 8: UE transmit power \(P_{TX,UE}\) as a function of downlink RSRP signal level for (**a**) uplink and (**b**) downlink transmissions.  
Figure 7: Relationship between ECL selected by the modem and downlink RSRP signal level during (**a**) uplink and (**b**) downlink transmissions.
## 6 Methodology
### State Space Analysis and Discussion
This may alleviate the problems caused by sensor distortion. The jerk and snap flow are utilized to identify EEs in the trajectory analysis, and the findings show that both methods are adaptable to interplanetary data analysis. The jerk and snap are used by means of unsupervised clustering approaches to improve the learning significantly and, therefore, the comprehension of notions about the outer space trajectory. Key elements in Figure 20: Status structures (y-axis): This refers to the normalized measurements of Juno's motion, likely including acceleration (a), jerk (j), and snap (s). These values range from \(-1\) to \(1\). Time (x-axis): The x-axis displays time in hours. Operation phases (OPs): The data are presented for three different operation phases, potentially representing various engine thrust configurations or flight modes for Juno. The first, second, third, and fourth derivatives of the position are used as status vector \(U(t)\) in time with a resolution of one minute, \(t=1,\ldots,N\):  
\[\overline{U}(t)=\big{[}\tau(t),\vec{r}^{\prime}(t),\vec{r}^{\prime\prime}(t),\vec{r}^{\prime\prime\prime}(t),\vec{r}^{\prime\prime\prime}(t)\big{]}=\big{[} \vec{r}(t),\overline{v}(t),\overline{a}(t),\overline{j}(t),\overline{s}(t) \big{]}. \tag{16}\]  
Figure 19: State space of Juno in OP3: (v, a, j). Figure 20: State space of Juno in OP3: (a, j, s). By normalization of the components in the corresponding dimension, we obtain the normalized status vector \(\widehat{U}(t)\):  
Figure 15:0  
Figure 15:1  
where \(\|\overline{a}\|\) represents the Euclidean norm of the vector \(\overline{a}\).
# Simplified Design of Magnetic Gear by Considering the Maximum Transmission Torque Line
###### Abstract  
Magnetic gears (MGs) technology is studied widely among research institutions, with several improvements being documented. This development attracts a high amount of attention due to the demand in the development of magnetic gears towards higher performance than the conventional mechanical counterpart. In general, the design is complicated and there is a lack in detailed references for designing an MG for specific transmission torque as required by its application. Trial-and-error approaches have been the norm in achieving the desired torque by referring the existing MGs for the desired value of torque. This paper presents a new simplified approach towards designing an MG for the required torque and size by referring through a Maximum Transmission Torque Line (MTTL) reference. Finite element method (FEM) is used in analyzing randomly designed magnetic gears with various parameters towards the desired values of the MTTL. The proposed approach of MTTL is a new approach to estimate the total volume of permanent magnets (PMs) required for the MG to achieve the desired transmission torque. The reference line is used to generate equation relating the specific parameters of MG to develop the simplified design of MG based on the estimated total volume of PMs. This simplified way details to 8.5% of error in targeting the desired transmission torque, a means and way for the first stage of the MG design approach to reduce the conventional approaches.  
Keywords:concentric magnetic gear; Maximum Transmission Torque Line; surface type PM +
Footnote †: journal: Applied Physics Letters
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 3 Results and Discussion
### Physicochemical Parameters of the Banana Flesh during Storage
Variations in the content of the peel carotenoids, along with the appearance of dark spots due to the activity of the polyphenol oxidases (PPOs) could have been related to the negative correlation between the a* and L* parameters [39]. Therefore, the a* parameter of the banana peel was negatively correlated with the b* parameter of the banana peel as well as the firmness and adhesiveness of the banana flesh, and it was positively correlated with the storage time, the moisture content, and the total soluble solids (Brix) of the banana flesh. Based on the above results, the image analysis of the banana peel could be used as a competent indicator for estimating the banana flesh quality. Regarding the physicochemical parameters of the banana flesh, strong negative correlations were established between the firmness and TSS (Brix) values (\(-0.77\), \(p<0.05\)), the firmness and the moisture content (\(-0.74\), \(p<0.05\)), as the decrease in firmness was ascribed to the transfer of moisture from the peel to the flesh during ripening, as well as the a* and h parameters (\(-0.76\), \(p<0.05\)). Moreover, high and positive correlations (\(p<0.05\)) were determined between the springiness and the cohesiveness; the springiness and the chewiness; the chewiness and the adhesiveness; and the chewiness and the cohesiveness (i.e., 0.84, 0.94, 0.83, and 0.82, respectively), whereas the firmness only correlated strongly with the adhesiveness (0.77). The interrelations between the texture parameters, such as the chewiness and the cohesiveness or the springiness and the chewiness, could be assigned to other parameters associated with banana ripening, such as an increase in the sugars in ripe bananas. Figure 7: Firmness, springiness, adhesiveness, cohesiveness, and chewiness of the banana-flesh samples during storage, at 18.0 \(\pm\) 0.5 \({}^{\circ}\)C.
## 2 Materials and Methods
### Sensor Fusion-Based Concept
\begin{table}
\begin{tabular}{c c c} \hline
**Sensor** & **Device** & **Data** \\ \hline Accelerometer & Smartwatch & Acceleration in three orthogonal directions \\  & & (m/s\({}^{2}\)) \\ Camera & Smartphone & Handwriting video for ArUco marker tracking, \\  & & 720p resolution and 30 FPS \\ Gyroscope & Smartwatch & The rotation rate around the x, y, and z axes \\ Magnetometer & Tablet computer & The magnetic field for all three physical axes (uT) \\ Piezoelectric sensor 1, model PZT-5 & External, connected to the tablet using a USB & Electrical output in response to mechanical strain \\  & sound card and adapter & (V) \\ Piezoelectric sensor 2, model FT-50T-3A1 & External, connected to the smartphone using & Electrical output in response to mechanical strain \\  & 3.5 mm audio connector & (V) \\ Touchscreen & Tablet computer & Touch position, size, and orientation of contact \\  & & area, pressure, interaction type \\ \hline \end{tabular}
\end{table} 
Table 2: A list of sensors employed in the proposed apparatus along with their corresponding devices and the type of data acquired. Figure 4: A few instances of the characteristics that may be acquired by employing the suggested apparatus while writing with a stylus or finger. Numbers 1-3 inside the circle represent specific gestures of lifting and lowering the pen or finger during the process of writing the word “ice”. The number of gestures highlights user-specific variations, generating distinct patterns that differentiate handwriting styles. Additionally, each point in the handwriting corresponds to sensor readings shown in the frame on the right side of the figure. stylus down, move, up) extracted from the MotionEvent object, it is possible to ascertain the number of gestures. Specifically, as indicated by the number inside the circle in Figure 4, from the pen-down and pen-up events the number of individual strokes can be gained. In particular, the number 1 denotes the initial gesture, which corresponds to writing the letter \"i\" in a single motion. Subsequently, gesture 2 signifies the action of the user lifting the stylus and then placing it back on the screen to add the dot to the same letter. Finally, gesture 3 involves writing the letters \"c\" and \"e\" in a single motion without raising the stylus. If another user writes the word \"ice\", they might not write the letters \"c\" and \"e\" in one motion. Instead, they could lift the stylus after writing the letter \"c\" and then place it down again to write the letter \"e\". This implies that the user utilized 4 gestures to write the same word. It demonstrates the variation among different users, leading to distinct gesture patterns that distinguish between different writing styles.
## 4 Simulation Results and Discussion
### Media Noise in BPMR Channel  
To simulate actual conditions, we performed the simulations in the BPMR channel with media noise. First, we tested with 6% and 8% position fluctuation in the simulations [23; 24].  
In Figure 17, the proposed algorithm was able to achieve a performance gain in the BPMR channel even with 6% and 8% position fluctuation. Then, we increased the position fluctuation further at 15 dB. Figure 18 shows that when the position fluctuation reached 18%, all tested methods had similar BER performance. This means that our proposed algorithm can only withstand a position fluctuation of less than 18%. With position fluctuation, the interference changes randomly. Therefore, the interference coefficient estimator is provided with a lot of randomly changing ITI and ISI information for detection. This makes it possible to improve performance in the BPMR channels even with media noise.  
Figure 15: BER versus SNR for 10% and 15% track misregistration (TMR).  
Figure 16: BER versus TMR at 15 dB.  
Figure 17: BER versus SNR for 6% and 8% position fluctuation.
## 5 Model for a DM Whose Preferences Are Compatible with a Weighted-Sum Function  
### The Preference Model  
In this section, we suppose that the \(i\)-th DM's preferences are compatible with a simple interval weighted-sum value function of the form:  
\[\mathbf{U}=\Sigma wf_{i}\ (j=1,\ldots\ N) \tag{18}\]  
In Equation (18), \(\mathbf{U}\) is an interval number that is calculated through the arithmetic operations defined in Section 2.3. We refer to a generic DM, and for simplicity, we avoid the use of the subscript \"\(i\)\".  
**Assumption 6 (modeling arbitrariness and ill-definition of parameters).**_The DM is willing to set a weighted-sum function model in terms of weights and objective function values. With the purpose of modeling imperfect information, those magnitudes will be considered as interval numbers._  
This assumption was implicitly made by [PERSON] et al. (2007, 2008) and [PERSON] (2016) in the frame of project portfolio optimization.  
**Remark 4**.: _Let us consider a pair (x,y) in the objective space of Problem 11. The conditions \(P(\mathbf{U}(x)\geq\mathbf{U}(y))>0.5\) or \(xD(0.5)y\) may not suffice to guarantee a credible preference favoring \(x\) over \(y\). However, there should be a credibility threshold \(\alpha>0.5\) such that \(P(\mathbf{U}(x)\geq\mathbf{U}(y))\geq\alpha\) and/or \(xD(\alpha)y\) are good arguments to justify a credible preference._  
**Definition 14 (\(\alpha\)-preference).**_Given a pair (x,y) in the objective space of Problem 11 and \(\alpha\) a real number greater than 0.5, we say that \(x\) is \(\alpha\)-preferred to \(y\) iff at least one of the following conditions is fulfilled:_  
\(a.\quad P(\mathbf{U}(x)\geq\mathbf{U}(y))\geq\alpha\)__  
\(b.\quad xD(\alpha)y\)__
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 3 Enactment, Collaboration, and e-Governance  
Human-to-human business enactments are governed by enforceable contracts either in the form of an oral, or written agreement. Contract documents [22] uniquely identify the contracting parties, the offered services, or goods, a corresponding compensation, as well as further constraints such as delivery dates, quality goals, penalties, and means of arbitration [23]. Still, a highly-automated and machine-driven ecosystem requires a digital equivalent that is accessible to and usable by all stakeholders. Moreover, traditional solely human-focused contracts are often under-specified and thus, not suitable for M2X enactments [23]. \"Most importantly, traditional contracts do not provide sufficient details about the actual transaction process, and consequently, frictions between the contracting parties are very likely, e.g., one party assumes a specific product certificate before delivering a partial compensation, and the other party assumes the opposite\" [23].  
Electronic smart contracts [24; 25] address the listed issues by enabling and governing business transactions using a computerized transaction protocol such as a blockchain. Blockchain technology [26] ensures a trustworthy, tamper-resistant, P2P transaction processing, and enables a distributed, often decentralized, transparent way for communication. More generally, a blockchain is a distributed ledger that enables users to send data, process it, and verify it without the need for a central entity [26]. In addition, smart-contract blockchain technology comprises computer programs for the consistent execution by a network of mutually distrusting nodes where no arbitration of a trusted authority exists. As a result, allowing for fact tracking, non-repudiation, auditability, and tamper-resistant storage of information in a distributed multi-stakeholder setting.  
On the one hand, the running case of Section 2.1 only presents a small fraction of potential applications and use cases of the M2X Economy. On the other hand, the running case already contains several examples of different M2X interactions, transactions, and collaborations, i.e., TaaS, road space negotiations, toll gate payments, BEV charging, traffic light information dissemination, and smart parking. The enactments of the listed examples follow a similar process structure, thus allowing for an abstraction towards a general lifecycle of the M2X Economy. Consequently, we stipulate that all M2X-related interactions, transactions, collaborations, and further enactments can be governed and represented using a blockchain-based smart contract.  
In the following, Section 3.1 details a conceptual lifecycle for M2X business enactments and collaborations using electronic smart contracts. Afterward, Section 3.2 outlines corresponding distributed e-governance mechanisms.
## 2 Theoretical Background
### Bandwidth of the Specific Mode  
As is known, there is no strict definition for the bandwidth of AM-FM signal. In this subsection, it is assumed that the signal function has a bandwidth limitation and follows [PERSON]'s rule. Thus, the bandwidth of nonlinear intrinsic mode function can be defined as [33].  
**Definition 2**.: _The bandwidth of a mode component can be expressed as:_  
\[BW_{AM-FM}=2(BW_{AM}+BW_{FM}) \tag{4}\]  
_where \(BW_{AM}\) is the maximum frequency of IA resulting from the Amplitude Modulated (AM), and \(BW_{FM}\) stands for the bandwidth caused by Frequency Modulated (FM). Theoretically, the IA has no bandwidth limitation, but we assume that we can find an \(BW_{AM}\), which includes most energy of IA \(A_{k}(t)\). \(BW_{FM}\) increases both with the maximum deviation \(\Delta f\) of the IF and with the rate of excursion \(f_{FM}\), which can be defined as:_  
\[BW_{FM}=\Delta f+f_{FM} \tag{5}\]
## 4 Case Analysis of Concepcion Earthquake
### Ionospheric Structure Anomaly before Earthquake
In addition, we found that the ionospheric TECs in the area of the epicenter fluctuate dramatically in the approximate period with Nmf2 disturbances. Obvious TEC anomalies were observed before the shocks, on February 15, 20, 21, and 25, and all the ionospheric TEC anomalies were positive (Figure 14b). As shown in Figure 14c,d, on 21 February 2010 (six days before the shock), an apparent TEC increase in the southeast region of Chile and the Weddell Sea from UT 10:00 to 12:00 was captured. The anomalous amplitude was about 5 TECU. Four days later, on 25 February 2010, a strong, positive, TEC anomaly (>6 TECU) was observed over the region of the seismogenic zone from UT 08:00 to 10:00. The ionospheric variable characteristics before the Concepcion earthquake conform to the LAIC model with an identical spatial distribution of TEC and Nmf2 disturbance peaks. There are differences in the pre-earthquake ionosphere anomalies (PEIA) recorded in GIM and COSMIC. Compared with COSMIC occultation observations, the spatio-temporal continuity of GIMs (in the presentation of the planar ionosphere structure) is obviously better. The size and intensity of the TEC anomalies, however, are uncertain, especially in the ocean [55]. In addition to the difficulty in determining the TEC boundaries, the lack of GNSS sites in marine areas gives rise to another major challenge. By contrast, the COSMIC profiles can effectively reflect the ionospheric structure of Nmf2 and the electron density at different heights, including the oscillation mode and the uplift or descent in the F2 layer. These RO events are conducive to capturing weak traces of PEIA atmospheric dynamics but are not conducive to collecting commonion signals by traditional ground-based GNSS monitoring. Due to the sparsity of COSMIC products, the detailed mechanism by which AGWs trigger PEIAs in dissemination is yet to be determined. With the arrival of advanced COSMIC-2 products, its capability of producing ~5000 high-accuracy soundings per day will promote the in-depth excavation of Lithosphere-Atmosphere-Ionosphere Coupling. Relevant investigations on the specific propagation mode of AGWs, the ionospheric traveling disturbance, and the damage degree of the ionosphere's structure are expected in the future.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 4 Experimental Results and Analysis  
### Experimental Data Collection  
#### 4.1.1 Image Data Collection  
During the static load experiment, to document the deformation of the bridge under varying loads, the bridge structure is photographed using the camera equipment specified in Section 2. The initial condition is when the vehicle is unloaded. For each additional increment of 10 kg, images are captured after the structure has stabilized (Figure 11).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition Number** & **Load Condition** & **Load Position** \\ \hline J1 & 0 kg & \\ J2 & 10 kg & Near the Edge of the \\ J3 & 20 kg & Mid-Span \\ J4 & 30 kg & Mid-Span \\ J5 & 40 kg & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Static load conditions.  
Figure 10: Experimental vehicle and additional weights.  
During the dynamic load experiment, the model car will be propelled by a motor located at the right end of the test beam, moving from the left to the right at a speed of 0.15 m/s. Given that the test beam is 6 m long, the dynamic load signal acquisition time will be approximately 40 s.
## 5 Recent Developments in RF Energy Harvesting
### Simultaneous Wireless Information and Power Transfer  
Simultaneous wireless information and power transfer (SWIPT) is an emerging technology that enables the concurrent transmission of energy and data over the same RF spectrum. This approach is highly beneficial for next-generation wireless networks and IoT applications. A recent study developed a SWIPT-enabled rectenna that operates at 2.45 GHz and achieves an energy-harvesting efficiency of 65% while simultaneously decoding data signals with minimal interference [84]. The system employs power-splitting and time-switching architectures to optimize both power transfer and data communication. Another innovative SWIPT approach utilizes MIMO (Multiple-Input Multiple-Output) techniques to enhance energy and data transfer efficiency. A SWIPT-MIMO system was proposed for 5G networks, demonstrating a 35% improvement in power transfer efficiency compared to conventional SWIPT designs [80]. The system dynamically allocates power and information-carrying signals based on the user's energy demand. Recent SWIPT systems also leverage hybrid relaying protocols and cooperative communication strategies to improve energy fairness and spectrum efficiency across multi-user environments. The integration of SWIPT with AI-driven optimization further enhances performance. A predictive AI model was integrated into a SWIPT-enabled IoT network to optimize resource allocation, leading to a 30% improvement in energy-harvesting efficiency. This model incorporates real-time contextual awareness of user mobility and signal fading, enabling the development intelligent modulation and coding schemes for dual-function transmission. Additionally, advanced SWIPT channel models have been developed to simultaneously enhance the Age of Information (AoI) and energy-harvesting timeliness. This was achievedthrough an online adaptive packet transmission strategy based on the Robbins-Monro algorithm, which dynamically adjusts transmission parameters to maintain optimal system performance in fluctuating environments. These innovations reinforce the status of SWIPT as a pivotal technology for the future of wireless communication and energy-harvesting applications [85].