esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bounded Scheduling Method for Adaptive Gradient Methods
###### Abstract
Then Adadelta [14], RMSprop [15], Adam [16], Nadam [17] are proposed to fix this issue, which use the exponential moving averages of squared past gradients to avoid the rapid drop of learning rate. These algorithms have been successfully applied to a variety of practical problems, especially Adam has become the default algorithm for training neural networks. When training DNNs with adaptive gradient methods, the loss function decreases rapidly in the early stage of training, but the final training loss and test loss are worse than that of SGD in many applications. Moreover, since the learning rate of Adam does not decrease monotonously, the training process will diverge in some applications [18]. Some work has proposed a hybrid scheme of Adam and SGD to solve these problems. SWATS [19] proposes a strategy that Adam can be switched to SGD when a triggering condition is satisfied, which can close the generalization gap between Adam and SGD. ADABOUND [13] can achieve a gradual and smooth transition from adaptive methods to SGD by employing dynamic bounds on learning rates. For these hybrid algorithms, the switching time of Adam and SGD and the learning rate of SGD after switching still have a great impact on the performance of the algorithm, which should be tuned elaborately. In this paper, we study the adaptive gradient algorithms and propose a bounded scheduling method for Adam, called Bsadam, to improve the performance when training neural networks. The major contributions of this paper include:  
1. We investigate the factors that lead to the poor performance of [PERSON] while training complex neural networks. 2. We set effective bounds for the learning rate of [PERSON] without manual tuning, which can improve the generation capability. 3. We schedule the bounds of learning rate to improve the performance of [PERSON]. Firstly we fix the upper bound and increase the lower bound gradually to find wide, flat minima. Then we fix the lower bound and decrease the upper bound gradually to ensure the convergence of training. At last, a fixed learning rate is used to make the algorithm converge to the optimal solution. We train multiple tasks on several models to evaluate the algorithm. MNIST [20] is trained on simple neural networks, CIFAR-10 [21] and CIFAR-100 [21] are trained on ResNet [22], DenseNet [23] and SENet [24], Penn Treebank [25] is trained on LSTM [26]. All these experiments show that our method is capable of eliminating the generalization gap between Adam and SGD and maintaining a higher convergence speed in training. The rest of our paper is organized as follows. In Section 2, the background of this paper is reviewed, where the traditional learning rate methods and adaptive gradient methods are described. In Section 3, we introduce the bounded scheduling scheme for Adam. In Section 4, we present a series of experiments to verify the effectiveness of our method. In Section 5, we summarize the paper.
## 2 Operation of the BB84 Protocol
### Privacy Amplification  
To eliminate [PERSON]'s knowledge about their secret key strings, [PERSON] and [PERSON] then extract an \(\ell\)-bit key from their remaining error-corrected bit strings by applying privacy amplification. In doing so, [PERSON] performs a random universal Class-2 hash function and publicly announces the results to [PERSON]. Finally, [PERSON] executes a similar hash function to distill a secret key of the same length as [PERSON].
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 2 Related Work
\\ \multirow{4}{*}{Data volume Information Loss Anti-interference capability Fault tolerance Better Better Better Moderate Reader  
cloud computing through interactive collaboration, this approach can be applied to the fault diagnosis and prediction of production line equipment, thereby achieving process intelligence. The scientific and technological contributions of this paper include:  
1. Designing an edge node that integrates signal acquisition, data preprocessing, feature extraction, and fault diagnosis classification to reduce equipment fault diagnosis latency and data transmission volume. The data preprocessing and the improved CNN-DS model are deployed on the edge node to enable fault diagnosis of rotating machinery based on edge computing. 2. Addressing the issue that most of the collected data during the acquisition process are normal, while fault data are relatively scarce, necessitating logical data augmentation. A sliding window method is employed for data augmentation. 3. Proposing a fault diagnosis method based on dual-channel CNN decision-level fusion. This method increases the CNN input layers to process data from different sensors while maintaining accuracy. Additionally, DS evidence theory is incorporated into the fully connected layer to achieve decision-level fusion of the dual-input CNN, significantly enhancing the model's robustness, generalization, and overall performance. The structure of this paper is as follows: Section 3 elaborates on the theoretical foundations of convolutional neural networks and Dempster-Shafer evidence theory. Section 4 introduces the proposed fault diagnosis method based on edge computing, detailing the design and implementation of the edge node. Section 5 presents experimental studies conducted on the main motor of a tension leveler in a cold rolling plant, utilizing the proposed fault diagnosis approach. The experimental results substantiate the effectiveness and advantages of the method, offering valuable insights and references for the practical fault diagnosis of tension leveler main motors. Finally, Section 6 provides a conclusion.
## 1 Introduction
### Contributions of This Work  
In this study, we make the following novel contributions:  
1. Theoretical Framework: (1) We derived a closed-form expression to quantify anti-single-frequency interference performance in short-code DSSS systems, incorporating parameters such as signal-to-interference ratio (SIR), frequency offset, phase deviation, and spreading code structure. (2) We demonstrated that interference components after despreading exhibit DC or sinusoidal distributions, with fluctuation frequencies tied to interference offsets.
2. Key Distinctions from Prior Work: (1) Unlike long-code systems, short-code DSSS exhibits periodic BER oscillations with interference frequency offsets, with minimal BER at zero offset. (2) We identified threshold SIR values (e.g., \(-24\) dB for \(N=15\)) below which BER degrades sharply, providing actionable design criteria. 3. Practical Insights: (1) We showed that interference at integer multiples of bit bandwidth (e.g., \(\Delta f=R_{b}\)) causes maximum performance degradation, guiding frequency planning. (2) We highlighted how spreading code structure influences BER trends, even for codes of identical length (e.g., m-sequences vs. Gold codes). 4. Validation: (1) We validated theoretical models through simulations and experiments, confirming alignment in scenarios where interference frequency offsets match integer multiples of \(R_{\text{b}}\).
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Static Sink-Based Frameworks
#### 2.2.1 Threshold-Sensitive Energy-Efficient Sensor Network (TEEN) Protocol  
[PERSON] et al., proposed a threshold-sensitive energy-efficient data gathering and transmission protocol for reactive WSNs called TEEN [56]. In reactive networks, nodes respond immediately to sudden changes in the network environment attributes. In TEEN, data gathering is performed using a hierarchical clustering mechanism. Cluster formation is performed as in the low-energy adaptive clustering hierarchy (LEACH) protocol, which is a proactive network protocol. Unlike LEACH, TEEN is designed for reactive networks that are well suited for time-critical applications. The CHs of the uppermost levels of the hierarchy directly report to the BS. Energy consumption is reduced as the nodes only have to transmit to their immediate CHs, and the additional computation is only performed by the CHs. Moreover, CHs are rotated by every node working as a CH for a cluster period \(T\) in order to evenly distribute energy consumption. At each cluster period, the CHs broadcast a hard threshold (\(H_{T}\)) and a soft threshold (\(S_{T}\)) value to their members along with the attributes. The sensor nodes consistently sense the environment. When a parameter from the attribute set is equal to its \(H_{T}\) value, the sensed data are stored in a variable called the sensed value (SV). When the current value is higher than \(H_{T}\) and differs from the SV by more than the \(S_{T}\) value, the node transmits data. Subsequently, the SV is changed to the current value of the sensed attribute. The attributes are set to new values during broadcasting in every clustering period. The value of \(S_{T}\) can be adjusted based on the requirement, with a trade-off between the accuracy of the data and energy consumption of the network.  
Advantages: The CH rotation after each cluster period balances the energy consumption of the network to maintain the overall network lifetime. Using the \(H_{T}\) and \(S_{T}\) values leads to a decrease in the number of data transmissions by the sensor nodes.  
Limitations: As the nodes transmit data only when the \(H_{T}\) and \(S_{T}\) conditions are met, the transmission may never occur if one of the conditions is not checked. Although the rotation of the CH balances the energy consumption in the network, repeated clustering at each interval may not be computationally efficient.
# A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients
## 4 Experimental Studies
### Tunability and Calibration  
Six different positions between the completely tightened to completely unscrewed states were tested (marked with the respective spacing \(d\) in Figure 2(b)). The threshold pressure limit varied from 38 kPa up to 778 kPa.  
Once the threshold limit of pressure was reached, a sudden fall in pressure could be seen, which led to the reduced constraint motion presented in Figure 0(c). This negative stiffness profile is significant in enabling the pressure redistribution (discussed in Section 4.2). The minimum spacing \(d\) corresponds to the state of the device when the movable alloy is farthest from the magnet; this means the magnetic binding force was lowest. Therefore, it required less pressure to move the head down compared to when the movable alloy was closest to the magnet (a higher \(d\)). In order to set the proposed module for a desired threshold pressure limit, it becomes important to map the observable parameter, \(d\), which is the spacing between the head and the fixation of the module (Figure 2(c)). The controllable access that the user would have is the screwing, and so, a desired spacing \(\hat{d}\) can be obtained  
Figure 3: Experimental setup, tunable profile, working scheme, tunability with spacing.  
based on the extent of the screwing. As the threshold pressure and actual spacing \(d\) (directly dependent on spacing \(g\) from Equation (1)) are non-linearly related, an exponential relation was considered and is given by Equation (2). The desired threshold pressure \(\hat{P}_{th}\) can be achieved by finding the corresponding spacing \(\hat{d}\) (using a mapped calibration), which can be visually tuned (to make \(\hat{d}=d\)) by the user based on the screwing.  
\[\hat{d}=a_{1}\times\log(a_{2}\times\hat{P}_{th}+a_{3}) \tag{2}\]  
For the proposed device, a spacing \(d\) of 1.7-2.7 mm is achievable, corresponding to the completely screwed and unscrewed configuration, respectively. Different experiments were performed with different spacings, and the corresponding threshold pressure limits were recorded. Figure 3d presents the experimentally obtained results and the fitting based on Equation (2) using the identified parameters \(a_{i}\) as listed in Table 2. A deviation in the lowest limit between COMSOL and the experiment possibly came from the non-linearity in the flux leakage and/or the lack of the precise inclusion of the geometrical/material-based properties.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics
#### 2.4.3 Packet Loss  
The percentage of packets lost is the total number of packets sent over the network in proportion to time [6]. If packet loss occurs in the TCP protocol, it will send the lost packet again, resulting in increased overhead in terms of energy wasted to forward a dropped packet [13]. The UDP protocol, however, does not resend a lost packet, which causes packet loss. Several things cause the loss of packets on the network, including network overload, corruption of the packet, errors in physical media problems, and the failure of the receiver's transmission (i.e., buffer overload) [30]. Packet loss can be calculated using Equation (3).  
\[\text{Packet Loss}=\frac{\text{total of packet sent}-\text{total of the packet received}}{\text{total of packet sent}}. \tag{3}\]
## 1 Introduction
Therefore, depending on the density of the acquisition angles and the methods chosen, RTI acquisition can lead to obtaining a voluminous and complex set of data [18,32,33]. Estimation of the descriptors constitutes a first step in data reduction, which allows a better understanding of the local surface characteristics and facilitates the analysis and post-processing carried out based on this type of acquisition. However, these descriptors vary in terms of unit, amplitude, and even dispersion; analyzed independently, they generally do not make it possible to match the various stakes of analysis in the context of controlling the appearance of manufactured surfaces. In this article, we propose a methodological contribution to answer this problem. The proposed method consists in locally (_intra-surface_) or globally (_inter-surface_) estimating the visual saliency from the data and descriptors extracted from RTI acquisitions. Figure 1: Diagram of an RTI acquisition. Indeed, saliency maps aim to respond to an important issue for manufacturers, mentioned above, which is to allow better detection while assessing the criticality of aspect anomalies. In Section 2, we show how a multivariate and multi-scale analysis of the descriptors previously extracted from the RTI acquisitions can be used to determine the local saliency of the points/pixels of the inspected surface in an efficient way. This approach is then extended in Section 3 to the analysis of _global anomalies_, i.e., the mapping of the difference in appearance between one or more surface states and a reference surface state. These maps make it possible to functionally quantify the distance between two surfaces, i.e., in terms of overall appearance thereby meeting the challenge of managing the manufacturing and surface finishing processes, which require that differences in terms of appearance between surfaces be discriminated and evaluated.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Azimuthal Angle Analysis  
(i) Pupil Player Software Azimuthal Angle:  
For each subject's recording, 2 human researchers conducted a semi-automatic analysis of the recording to estimate the azimuthal angle of the gaze from the line, as seen in Figure 3a. This analysis was conducted with the Pupil Player software, using the validation function. The two researchers chose frames where the subject's gaze was clearly detected and part of the participant's line was visible. At each appropriate frame, they manually marked a point on the line that seemed the closest to the subject's gaze (see the yellow dot on the line, in Figure 3a above). Based on the chosen point on the line and the location of the gaze, an estimate of the angular distance was made. In order to measure the inter-observer  
Figure 2: Pupil Core eye tracker device with three cameras. The two cameras, one for each eye, are circled in yellow and the world camera is circled in red.  
reliability, we measured the intra-class correlation (ICC) of the two measurements, and received r_ICC = 0.68 for the active subjects, r_ICC = 0.58 for the passive, and r_ICC = 0.95 for the combined data set (active and passive together). The inter-observer reliability values fell within the range of the inter-observer reliabilities previously found in our lab [66] and elsewhere [67]. The high level of inter-observer correlation for the combined data set reflects the fact that the noise coming from the difference between the observers was small compared to the signal we reported. In fact, the square root of the mean squared differences (MSE) between the observers (9.9%) was at least 7 times smaller than the mean differences between their reported observations (>70%, see Section 3.3).  
(ii) Manual Measurement of Azimuthal Angle:  
To test the reliability of our measurements of the angular distance, we selected certain video images of situations that could be physically reconstructed in the studio class. Below, in Figure 3b, we can see 1 instance out of 12 in which we physically reconstructed the finding of the angle. Using information from the video frames, we reconstructed the location of the center, the gaze, and the line. We placed a bottle in the room and two other objects that symbolized the gaze and the line and, with the help of a protractor, we physically measured the angle. To measure the reliability of our semi-automatic measurements (of one observer) using these manual angular measurements, we measured the ICC of the two measurements, and obtained r_ICC = 0.87.
## 2 Navigation Sensors and Techniques for Drones
### Ground-Based Localization  
There are several localization methods available that can complement GNSS. Unlike GNSS which uses satellites in orbit to provide signals to the user, the transmitters are ground-based. Although there are both local and global systems, one example of a global system is LocataTech, which is developed by Locata. There are several transmitters installed at areas with strong radio-positioning signals, and they can provide localization services to users in indoor and outdoor environments. Unlike GNSS, which has a typical received signal strength of -125 dBm, LocataTech is able to provide received signal strength of -84 dBm at 10 km and -64 dBm at 1 km from the transmitter [21]. Furthermore, the distribution of the transmitters can be adjusted according to the required operation environments. However, the downside of these localization approaches is that the infrastructure needs to be installed and available prior to operation.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 3 PQC-Based PKE Protocols for Using the PQC Algorithm
### Pqc-AES Hybrid PKE Protocol
Furthermore, the hybrid approach can improve overall performance since AES encryption and decryption operations are generally faster and more efficient than PQC standaloneencryption. Moreover, since AES-256 is a widely adopted and well-analyzed symmetric encryption algorithm, it has been demonstrated to be secure. In the implementation of the proposed hybrid protocol and conventional AES-256 used in the evaluation, we adopt AES-GCM as the symmetric encryption scheme due to its strong security properties as an authenticated encryption algorithm [28] and its efficient performance. In comparison, AES-CBC requires separate MAC handling, making it more prone to implementation issues. Therefore, AES-GCM is prioritized over other types of symmetric encryption schemes. ```
1:Input: Data to be transmitted (\(data_{c}\) from Client, \(data_{s}\) from Server)
2:Output: Securely exchanged data
3:Client (C) and Server (S):
4: Perform steps 1-24 of Algorithm 1 to establish shared secrets \(ss_{c}\) and \(ss_{s}\) (and confirm them). 5:Key Derivation (Both C and S):
6:\((K_{1},K_{2})\leftarrow\) HKDF\((ss_{c}||ss_{s},\) salt, \"AES keys\")
7:Client-to-Server Data Transfer. 8:\(iv_{c}\leftarrow\) GenerateInitializationVector\(()\)
9:\(seq_{c}\leftarrow\) GenerateSequenceNumber\(()\)
10:\(ct_{data_{c}}\leftarrow\) AES-Encrypt\((K_{1},iv_{c},data_{c},\) mode\(,seq_{c})\)
11:\(mac_{data_{c}}\leftarrow\) HMAC-SHA256\((K_{2},ct_{data_{c}}||seq_{c})\)
12:Send \((iv_{c},ct_{data_{c}},mac_{data_{c}})\) to Server (S)
13:Server (S):
14:if\(mac_{data_{c}}=\) HMAC-SHA256\((K_{2},ct_{data_{c}}||seq_{c})\)then
15:\(data^{\prime}_{c}\leftarrow\) AES-Decrypt\((K_{1},iv_{c},ct_{data_{c}},\) mode\(,seq_{c})\)
16:if\(seq_{c}\) is valid (not a replay) AND Decryption successful then
17:Process \(data^{\prime}_{c}\)
18:else
19:Reject
20:endif
21:else
22:Abort
23:endif
24:Server-to-Client Data Transfer:
25:\(iv_{s}\leftarrow\) GenerateInitializationVector\(()\)
26:\(seq_{s}\leftarrow\) GenerateSequenceNumber\(()\)
27:\(ct_{data_{s}}\leftarrow\) AES-Encrypt\((K_{1},iv_{s},data_{s},\) mode\(,seq_{s})\)
28:\(mac_{data_{s}}\leftarrow\) HMAC-SHA256\((K_{2},ct_{data_{s}}||seq_{s})\)
29:Send (\(iv_{s},ct_{data_{s}},mac_{data_{s}})\) to Client (C)
30:Client (C):
31:if\(mac_{data_{s}}=\) HMAC-SHA256\((K_{2},ct_{data_{s}}||seq_{s})\)then
32:\(data^{\prime}_{s}\leftarrow\) AES-Decrypt\((K_{1},iv_{s},ct_{data_{s}},\) mode\(,seq_{s})\)
33:if\(seq_{s}\) is valid AND Decryption Successful then
34:Process \(data^{\prime}_{s}\)
35:else
36:Reject
37:endif
38:else
39:Abort
40:endif
```  
**Algorithm 2** PQC-AES hybrid PKE protocol
## 3 Numerical Models
### Turbulence Model  
In the present study, the realizable turbulence model was used to resolve the complicated turbulence of the two-phase flow inside the jumper [29]. The governing equations of turbulent kinetic energy \(k\) and the turbulent energy dissipation rate \(\varepsilon\) are given below:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{j}}\left(\rho k u _{j}\right)=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{ \sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho \varepsilon-Y_{M}+S_{k} \tag{5}\]  
\[\begin{array}{c}\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial }{\partial x_{j}}\left(\rho\varepsilon u_{j}\right)=\frac{\partial}{\partial x _{j}}\left[\frac{\partial\varepsilon}{\partial x_{j}}\left(\mu+\frac{\mu_{t} }{\sigma_{k}}\right)\right]+\\ \rho C_{1}S\varepsilon-\rho C_{2}\frac{\varepsilon^{2}}{\varepsilon+\sqrt{ \varepsilon}}+C_{1\omega}\frac{\varepsilon}{C}_{3\varepsilon}G_{b}+S_{ \varepsilon}\end{array} \tag{6}\]  
where \(G_{k}\) indicates the kinetic energy of turbulence generation. \(Y_{M}\) represents the dissipation rate's overall contribution from changing dilatation in compressible turbulence [30]. \(G_{b}\) denotes the buoyancy-induced turbulence kinetic energy. The constants of the turbulent model are given below:  
\[C_{1\omega}=1.44,\sigma_{k}=1.0,C_{2}=1.9,\sigma_{\varepsilon}=1.2\]
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 7 Approximated State Reference Signal from a Periodic Output Reference Signal  
In this section, a method is proposed for determining an approximation of the state reference signal for the case in which the output tracking signal is periodic. In some cases, the computation of the state reference is a significant challenge due the nonlinearities and the switched nature of the power converters. This difficulty was perceived [2] and identified as one of the main difficulties in the control problems of some power converters. This problem is also closely connected to steady state oscillations in nonlinear circuits or mechanical systems, a topic that has attracted the interest of researchers for many years; see, for example, [50; 51].