esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The shortest resolvable time interval (\(\Delta\)tres) should be dictated by the \"instantaneous duration\" of BWin. This is given by the time-bandwidth product (\(\Delta\
u\Delta\)t = 0.44 for a Gaussian pulse): the time duration obtained from such time-bandwidth product corresponds to the minimum value at which a pulse with specific FWHM can theoretically be fully compressed. The bandwidth of the SASE pulse is increased relative to that of the contributions of individual spikes in a way that is analogous to how the experimental bandwidth of optical pulses can be expanded by adding together multiple optical sources each with a different carrier frequency [47]. The spectral width of a single isolated SASE spike at 9 keV has been recorded to be \(\approx\) 10 eV [48] at LCLS, which, through the time-bandwidth product, corresponds to a duration on the order of 200 as. The chirped pulse at SwissFEL consists of the superposition of \"single spikes\", varying in time and central frequency. The instantaneous bandwidth is determined by the pulse length and chirp of an individual spike. Therefore, the maximum time resolution that is possible for analyzing diffraction using chirped XFEL pulses is limited by the duration and chirp of the individual spike, which is analyzed from simulations here. Experimentally, based on the SwissFEL simulations presented, intervals shorter than 200 as will not be readily distinguishable with a SASE pulse, as the autocorrelation shows the intensity distribution within the 200-as duration. A coherence time shorter than 100 as has been discussed for harder X-ray (24.8 KeV) and low bunch charge conditions at the European XFEL [49]. The time duration that each reflection is stimulated for will depend on the chirp and on the bandwidth required to fully integrate it (BWreq). The spot bandwidth is given as in [50]:  
\[\text{BWreq}=(\eta+\gamma)\text{cot}\theta, \tag{1}\]  
where \(\eta+\gamma\) is the effective mosaic spread (the sum of mosaicity and beam convergence) and \(\theta\) is the Bragg angle. There is a dependence on resolution because the Ewald spheres diverge from the reciprocal space origin (Figure 1).
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 4 The Proposed Model
### Subview Information Extraction  
After the node feature space transformation, all node embeddings are in the same dimension feature space. To learn the rich semantic information of HIN, we first extract subview HINs based on different meta-paths \(p_{1},p_{2},\cdots,p_{N}\) and accurately model local information.  
Given the meta-path generation rule \(\rho\), construct the subview HIN \(\mathcal{G}^{m}=(\mathcal{V}^{m},\mathcal{E}^{m})\), where the mapping functions of node and edge types are \(\Phi:\mathcal{V}^{m}\rightarrow\mathcal{A}^{m}=(A_{1},A_{2},\cdots,A_{p})\) and \(\Psi:\mathcal{E}^{m}\rightarrow\mathcal{R}^{m}=(R_{1},R_{2},\cdots,R_{q})\).  
Taking the ACM dataset as an example, this paper defines two meta-path rules: \(\rho_{1}\) is a P-A-P meta-path, which extracts paper-author interactions; \(\rho_{2}\) is a P-S-P meta-path, which extracts paper-subject interactions.  
For each subview, input the node information related to the subview according to the meta-path rules. Since the data variance of HIN is generally large, to ensure the effect of the model, we apply the multi-head attention mechanism in the subview. We aggregate the information of nodes on the meta-path and then splice the node embeddings of \(K\) times. As shown in Figure 3, the embedding of the node \(i\) on the subview \(\mathcal{G}^{m}\) is expressed as \(h_{i}^{\mathcal{G}^{m}}\):  
\[h_{i}^{\mathcal{G}^{m}}=\big{\|}_{k=1}^{K}\ \sigma\Big{[}\sum_{j\in N_{i}} \left(\alpha_{ij}\cdot h_{j}+h_{i}\right)\Big{]} \tag{2}\]  
Figure 2: The framework of MFHE.  
where the attention mechanism of the node is used \(K\) times, \(N_{i}\) is the set of neighbor nodes for node \(i\) in the subview \(\mathcal{G}^{m}\). \(\alpha_{ij}\) is the normalized attention value between the node \(i\) and its neighbor node \(j\), and the calculation method is shown in Figure 4, specifically:  
\[\alpha_{ij}=\text{soft}\max(e_{ij})=\frac{\exp(\text{Re}LU(q^{\text{T}}\cdot[h_ {i}||h_{j}]))}{\sum_{k\in N_{i}}\exp(\text{Re}LU(q^{\text{T}}\cdot[h_{i}||h_{k }]))} \tag{3}\]  
where \(e_{ij}\) is the attention value between the node \(i\) and its neighbor node \(j\) in the subview \(\mathcal{G}^{i}\), the activation function uses ReLU, \(q\) is the attention vector, \(||\) represents the connection of the vector, and \((\cdot)^{\text{T}}\) represents the vector transpose.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 5 Discussion
### Battery Management for Safe Operation  
The Battery Management System (BMS) monitors individual cell voltages of the main power batteries and keeps them balanced during operation. The BMS was not capable of limiting maximum battery current to a set value. When a maximum 200 A current limit was surpassed, the BMS would disconnect the battery and disallow discharge for 1 to 3 s, a concern in the event of an engine-out situation. If the turbine engine became inoperable during flight, in-flight restart was problematic. In an engine-out scenario, all power for the lift rotors would consequently come from the battery. In this situation, the batteries could exceed the current limit set on the BMS, leading to a battery power shut-off, such that no power would be available to the rotors. The resulting loss of lift could lead to vehicle loss.  
The BMS was removed for flight tests, which likely prevented catastrophic loss due to engine-out during ascent, which occurred in a few flight attempts. The aircraft experienced a current exceeding 200 A from the batteries during the dive pull-out. The maximum amperage of the turbogenerator at 33 V during pull-out was around 165 A, based on the maximum measured turbogenerator power [16]. This indicates that the batteries were temporarily discharging almost 225 A and would have been disconnected by the BMS, had it been in place.  
The primary risk of not including the BMS and switching circuitry would be battery damage caused by unbalanced cells, overcharging, or complete battery depletion. However, the risk of damaging the battery during initial flight tests was acceptable compared to the risk of endangering the entire vehicle. Thus, the battery was placed in parallel with the turbogenerator by connecting it to the central bus (the rectifier output terminals). However, this compromise to remove battery protections would not be a satisfactory solution for a design intended for long-duration runs, commercial use, or manned applications. Battery failures do pose a risk to the aircraft, but it is recommended to remove or disable similar safety features during the first flight tests to generate empirical data to determine aircraft system limits for safe use of BMS and switching circuitry.  
An additional consideration is the implementation of multiple 12S batteries in parallel to provide redundancy. By having multiple batteries in parallel the single point failure of a singular battery cell is removed. However, increasing the number of batteries in parallel has the drawback of increasing the weight of the system and complexity.
# A Review of Medication Adherence Monitoring Technologies +
## 5 Challenges and Future Trends
### Challenges
#### Tampering, Authentication, and Active Non-Compliance  
Two key challenges arise because users may try to actively deceive the system into thinking they are compliant when they are not. Tampering occurs when an unauthorized user receives the medication. The first challenge then becomes one of authentication--Is the person who is taking the medication who he claims to be? Tampering can arise for medications which can become addictive, such as opiates, where an addict or dealer has an incentive to fool the system. Authentication and authorization are analogous concepts in computer security--Is the person who they claim to be, and is this person authorized to take the medication? Although few projects have specifically tackled these security challenges, an array of wearables has investigated if a wearable is actually worn by the person it is supposed to [164]. A second set of approaches attempts to prevent unauthorized access with the use of physical barriers, such as locks on the pillboxes. A related set of approaches does not try to prevent unauthorized access, but rather take an auditing approach. For example, learning the wrist motions of different people can create an audit trail [165], which can then be used to identify tampering for later remediation.  
The second challenge is observing active non-compliance, which is when a legitimate user actively deceives the system. Such behavior can occur when a user disagrees with a medical professional's treatment, but appears to comply rather than challenge the professional's judgment. Active deception on the part of the user is more difficult to solve as the person using the system is legitimate, but chooses not to consume the medication. A variety of approaches can be employed, such as video monitoring, but simple actions, such as placing medication in the mouth, faking a swallow, and then spitting it out later, will deceive most current technologies. Creating monitoring systems that correctly identify active non-compliance remains an important research challenge.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 5 Fault-Tolerant Scheme for WSN in Precision Agriculture
### Fault-Tolerant Clusters  
#### 5.1.1 Sensor Nodes  
As we know, the main task of a sensor node is the collection of data from the agricultural field; then, the information is to be transmitted to the primary CH. We already have mentioned before that the sensor nodes enter the passive state just after collecting and sending data in order to minimize the energy consumption. Now, the role of detection of failed sensor nodes is assigned to the leader CH. When the time slot associated with a specific sensor node ID arrives, a data request will be sent from the cluster leader to the sensor node. This sensor then must reply to that request before the timeout. If the timeout occurs without any response, the sensor node will be considered as _failed_ and its ID will be stored in a queue of failed sensor nodes. The list of failed sensor nodes will be sent to the BS by the CH along with the collected data.
###### Abstract  
A Si3N4 slot waveguide has the ability to confine light tightly in the slot, shows weak absorption of 980 nm pump light, and has lower transmission loss compared to a Si slot. Hence, the optical waveguide amplifier based on Er3+ and Yb3+codoped was proposed to be integrated with a Si3N4 slot to increase the gain. The core-shell NaYF4/NaLuF4: 20%Yb3+, 2%Er3+ nanocrystals-polymerite methyl methacrylate covalent linking nanocomposites were synthesized and filled into the slot as gain medium. The concentrations of Er3+ and Yb3+ were increased compared with traditional physical doping methods. High-efficiency emission at 1.53 um was achieved under 980 nm laser excitation. The slot waveguide was accurately designed using the semivector finite difference method in combination with the maximum confinement factors and the minimum effective mode area. The optimum width of the slot was 200 nm, and the optimum height and width of the silicon strip waveguide were 400 nm and 400 nm, respectively. The six-level spectroscopic model was presented, and the gain characteristics of the slot waveguide amplifier were numerically simulated. A net gain of 8.2 dB was achieved, which provided new ideas and directions for waveguide amplifiers.  
Keywords:Si3N4 slot waveguide; optical waveguide amplifier; nanocrystals +
Footnote †: journal: _Nanomaterials_
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 6 Design and Development of the Prototype
### Sender Module  
This is a generalized module for any type of information being provided to the WMS. It can scan the data from the barcode scanners/RFID scanners from the inventory, incoming dock, and outgoing dock. It can also include any data, such as the information of the employees entered into the system, the temperature and humidity of the Manufacturing Department, the financial records of the raw materials being bought, as well as the sales records of the products being outsourced. For every type of information being entered into the system, appropriate sensors or input devices can be installed into system and integrated with the network. The goods arriving at the inflow can be tagged with an RFID tag or with a barcode sticker, depending on their structure, degree of sustainability, and some other feasibility factors. RFID tags are deemed suitable for products that are non-perishable, as they can stay intact for a long time and can be used for tracking purposes. Various methods have been discussed in the literature regarding tagging goods and installing scanners for tracking [44; 45].  
For this case study, we designed hand-held and portable scanners using RFID reader modules and barcode readers, integrated with ESP controllers. ESP 32 is a high-speed microcontroller used for its very low latency, large number of GPIOs, and the availability of Bluetooth Low Energy (BLE), WiFi, and Ethernet connectivity, all at an affordable price. A number of sender modules were created for testing and validation and implemented all over the factory on the designated nodes. The scanners in close proximity to each other  
Figure 8: Functional view (for the case study).  
were connected to the same broker to avoid using extra brokers. The RFID readers were connected with the controller using the Serial Peripheral Interface (SPI), and barcode readers were interfaced over BLE. The scanners are portable and work as standalone devices. They communicate with the central system over WiFi using the publisher-broker-subscriber model of MQTT, with the added option of Ethernet.  
As mentioned earlier, each functional unit in the factory is called a \"node\". The raw material (uncut cloth rolls) for production is identified through barcode stickers, and the finished goods are packed in a carton tagged with RFID tags. At each node, the goods are scanned and cross-matched with the S/O, which reduces the chances of errors and increases the system efficiency.
## 3 Conventional Geolocation Techniques
### Received Signal Strength (RSS)  
RSS is analogous to the TOA technique in which the emitter transmitter must lie in the covering region, the measured distance will determine a circle centred at the receiver. If the power transmitted by the emitter is known, then the distance between the transmitter and the receiver will be calculated to determine the path loss of radio signals through popular mathematical modelling with distances by measuring RSS. Mathematically, the RSS measurement model is the average received power formulated as:  
\[p_{r,i}=K_{i}p_{t}d_{i}^{-\alpha} \tag{5}\]  
where \(p_{t}\) is the emitter transmitted power in the absence of noise, \(d_{i}\) the distances between the emitter and sensor \(i^{th}\), \(\alpha\) is the path loss constant, and \(K_{i}\) is other factors that affect the received power. Based on the propagation environment, \(\alpha\) is a shift from three to five. Three RSS measurements can calculate the coordinates of the emitter. However, RSS methods also result in significant range estimation errors due to shadow fading effects. Utilizing the pre-measured received signal strength contour centred at the receiver, the accuracy of the RSS methods can be improved by the received signal phase technique [47; 48]. Widespread RSS measurements were employed in Wireless Sensor Networks (WSN) based on detection and localization techniques [49; 50].
# The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy
## 1 Introduction
### Related Work
Participants estimated distances by reaching for/pointing at the virtual target at three egocentric distance levels: 90, 120, and 150 cm from the participant. Various applications in augmented reality and virtual environments require reaching by hand or holding other objects to touch a virtual target. For example, many mixed reality-assisted medical procedures involve positioning a medical device at a depth indicated by a (virtual) marker. Distance judgment by reaching/pointing has been studied in previous studies [13; 45; 46; 47]. Based on some of the results of previous studies [12; 40; 46], it was expected that distance estimation would be less accurate with the direct pointing technique than with the indirect cursor technique. In particular, a recent study compared the exocentric distance judgment and spatial perception between direct and indirect interactions and found that the accuracy in the frontal space significantly differed [46]. In the current study, the experiment was carried out to find whether such differences also exist for egocentric distance judgment. We expected a more accurate distance estimation for longer distances between the participant and the target [11]. We also expected that underestimations would be less severe with the direct pointing technique [13] than with the indirect cursor technique.
## 2 Data Processing and Network Description  
In this section, we illustrate our model in detail. As shown in Figure 1, the proposed algorithm is divided into two parts. In the first part, the time-series signals are processed using SSA, which effectively extracts the spectral features of the signals. By removing noise components, the impact of interference on the signal features is reduced, thereby improving the accuracy of the subsequent classification process. Then, the data from the I and Q channels are separately transformed into two-dimensional images. This is carried out to consider both the temporal and frequency domain information of the signals, enabling a more comprehensive representation of important signal features. Finally, at the end of the first part, the data from the I and Q channels are fused to obtain a more accurate and comprehensive feature representation.  
In the second part, a meta-learning-based relation network is designed for the classification of unknown signals. In practical applications, there are usually only a limited number of samples available for training. Therefore, conventional deep learning methods that rely on large-sample training struggle to adapt to small-sample conditions. To overcome this problem,we introduce meta-learning. Meta-learning leverages shared knowledge and patterns learned from different tasks, enabling fast adaptation to new tasks even with limited samples. Firstly, the feature embedding module is used to extract features from the images obtained in the first part. Then, the relation metric module learns the similarity metric among the known modulation signal samples. Finally, the unknown signals are thoroughly classified.
## 3 Materials and Methods
### Experiment Setup
Once all the periods in the validation set have been simulated, the value of Equation (11) is computed ex-post, and the best value of parameter \(\theta_{i}^{data\ benefit}\) found so far is recorded. This procedure is repeated K times. A random sampling strategy was adopted to select the starting value for the BO, and five different scenarios involving different starting points were considered for each component. The value of \(\theta_{i}^{data\ benefit}\) resulting in the lowest value of Equation (11) is adopted in the test set by the proposed approach as the effective value for parameter \(\theta_{i}^{data\ benefit}\). The value of research space \(\theta\) and the number of trials K are reported in Table 6. The number K of trials to follow has been selected, recurring to a heuristic elbow procedure. Indeed, the trials suggested that only small increments in performance were achievable for a greater value of K. Furthermore, similar K values were observed [37] when no more than 20 trials were reported. Finally, the test set is adopted to simulate and compare the decisions generated by the proposed approach and the benchmarks. Initially, the predictive module is trained on the historical data coming from both the training and the validation set, and the value \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Search space (\(\theta\)) & 0-99,999 \\ N trials & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Bayesian optimization parameters. \begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Hyperparameter Value** \\ \hline Number of layers & 1 \\ Max number of epochs & 100 \\ Early stopping threshold (\(\sigma\)) & 15 epochs with no improvements in the RMSE \\ Loss function & of the validation dataset \\ Input chunk length & MSE \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LSTM hyperparameters. adopted by the proposed approach of \(\theta_{i}^{data\ benefit}\) is the one identified in the validation set. As time evolves discretely with the arrival of new, unevenly sampled deliveries, based on the decisions resulting from the prescriptive module, new supplier performance data are collected over the test set, and the continuous training module updates the predictive ones with the newly acquired data. In conclusion, the values of the evaluation metrics described in Section Evaluation Metrics are thus computed considering all the discrete periods contained in the test set for both the proposed approach and the benchmarks.
## 2 System Model
### NOMA in Small-cells
#### 2.1.2 NOMA Receiver with Nonideal SIC  
In this case, the NOMA receiver performance is investigated in a nonideal condition. The case is considered because it is more inclined toward a somewhat practical behavior. Here, the SCU performs SIC in the presence of the channel effects produced by the transmission channel, which in this case, is the Rayleigh fading channel, hence creating a more practical approach than the perfect SIC case. Now, let us suppose that, in the \(k\) paired UEs, for the received signal at UE-m, the signal information of the SEUs with imperfect condition in the \(b\)th SBS is given from (5) as:  
\[\hat{s}_{b,k}=h_{b,k}\times\left(\sum_{k=m+1}^{N}s_{b,k}\sqrt{P_{b,k}}\right)+ \underbrace{n}_{noise}+\underbrace{I}_{ICI}, \tag{11}\]  
where, \(n\) represents the AWGN, \(I\) presents the ICI, and \(h_{b,k}\) is the Rayleigh fading channel. Then, the signal extraction of UE-m, after performing SIC, is given by using (5) and (11):  
\[s_{b,m}=\left\lfloor\frac{y_{b,m}-\hat{s}_{b,k}}{\sqrt{P_{b,k}}}\right\rfloor. \tag{12}\]For the case of two paired UEs, the SEU is UE-2 and the SCU is UE-1. Thus, in the received signal at UE-1, the signal information of UE-2, after including the effects of the Rayleigh fading channel and ICI, is given by (11):  
\[\xi_{b,2}=h_{b,2}\times s_{b,2}\sqrt{P_{b,2}}+\underbrace{n}_{noise}+\underbrace {I}_{ICI}. \tag{13}\]  
The extracted signal of UE-1 after SIC is given using (12):  
\[s_{b,1}=\left\lfloor\frac{y_{b,1}-\hat{s}_{b,2}}{\sqrt{P_{b,1}}}\right\rfloor. \tag{14}\]  
After the description of the NOMA receiver in small-cells with the ideal and nonideal SIC cases, the WHT part is next presented.