esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
## 4 TSON Control Plane  
This section overviews the TSON control plane. The TSON is fully software defined networking (SDN) enabled [17]. ONOS controller has been used as an SDN controller, wherein some applications or modules have been extended or developed. The TSON control plane architecture is depicted in Figure 17. It comprises an ONOS SDN controller where the OpenConfig driver is extended in order to handle the TSON features. Also, the NETCONF/OpenConfig agent based on the extended OpenConfig has been developed. This agent receives the request from the SDN controller via NETCONF in order to configure the FPGA. The developed NETCONF agent can process the received request and configure the TSON device implemented by an FPGA.  
OpenConfig is used to represent the TSON device. Indeed, the standard OpenConfig YANG model does not handle all the TSON features and needs to be extended. The OpenConfig YANG model implements several modules. Logical channel of the terminal device module has been extended to control the TSON parameters, such as:  
* Virtual local area network (VLAN) pairs;
* Time slots size;
* Frames size;
* Mode of transmission (TSON or Ethernet) to select the granularity.  
Figure 18 depicts the logical representation of the OpenConfig agent, wherein several clients (depending on the TSON device implementation) and two outputs are shown. The OpenConfig agent can configure the transmission mode (Ethernet or TSON) to configure the TSON parameters (if this mode of transmission is selected) or multiplex/demultiplex the logical ingress channels on one of the  
\begin{table}
\begin{tabular}{c c} \hline
**Destination** & **Latency (us)** \\ \hline Back to back, 10 G convergence & 8.86 \\ Back to back, 100 G convergence & 6.32 \\
8 km, 10 G convergence & 47.6 \\
8 km, 100 G convergence & 45.1 \\ \hline \end{tabular}
\end{table}
Table 1: Latency for different test scenarios.  
Figure 17: The TSON control plane architecture. FPGA: field-programmable gate array. SDN: software-defined networking, NETCONF: network configuration protocol, TDM: time domain multiplexing.  
optical channels. Also, an FPGA driver is developed in order to send the parameters to the FPGA agent via REST API.
## 6 Evaluation
### Generalization of Results  
The previous section established that Dynamis performs better than snapshotting, and Dynamis aggregation performs better than Dynamis skyline. We now show that these results hold for other values of \(k\) and \(w\). Figure 18 depicts the average yield \(Y\) for a 20-day trading window (\(w=20\)) as we pick an increasing number of top \(k\) picks along the \(X\)-axis. The actual gain does not matter; the intent is to show that the relative performance of the algorithms remains the same regardless of the value of \(k\). While the skyline and aggregation trend lines do converge, Dynamis remains superior compared to the snapshotting technique. Similarly, for a fixed value of \(k=10\), the relative performance of the algorithms remains the same as we vary \(w\) as shown in Figure 19.  
While the yields may look surprisingly high, one must consider the fact that the overall market did well in the period under consideration. As a result, picking the top 10 performing stocks every day and holding these for a period of several weeks is expected to provide good returns. Consider however that the amounts invested are substantial as \(w\) increases and that the risk is high. Thus, we have shown that the relative performance of our algorithms remains the same as \(k\) and \(w\) vary, with Dynamis outperforming snapshotting in all cases investigated.  
Figure 17: Average yield at the end of the evaluation period for our four algorithms with \(k=10\) and \(w=20\).  
The values reflect our opinion that the relative performance of a stock against a benchmark is slightly more important than the daily percentage increase, whereas the volume percentage change is not as important. Nonetheless, we obtain similar results by setting these weights to other reasonable values.  
Figure 19: Average yield (\(Y\)) for a \(w\)-trading day window along the \(X\)-axis, as we pick the top 10 stocks.  
Figure 18: Average yield (\(Y\)) for a 20-trading day window (\(w=20\)) as we pick an increasing number of top \(k\) picks along the \(X\)-axis.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 3 Results
### Option 2: ML Applied to Wavelet Spectrogram  
The MLP model achieved the highest cross-validated accuracy score of 0.9621 with optimized hyperparameters: activation = relu, alpha = 0.001, hidden layer sizes = (200), learning rate = adaptive, and solver = adam. The SVM model obtained an accuracy score of 0.9499, while RF recorded an accuracy of 0.957.  
The final accuracy score of MLP, the best-performing model, on the test set was 0.9621, with a confusion matrix indicating 441 true negatives, 9 false positives, 22 false negatives, and 346 true positives. The F1-score was 0.9620, and the Area Under the Curve (AUC) was 0.9908.  
Additionally, the stacking model, which combined the predictions of MLP, SVM, and RF (in that order), achieved an accuracy score of 0.9707, demonstrating an improvement over the individual models. The F1-score of 0.9706 and AUC of 0.9974 were also superior to those of the individual models.  
Regarding computational time, the feature extraction process took an average of 17.5224 s per sample, making it a more computationally expensive method compared to Option 1. Training times for this approach were considerably longer, with SVM taking 475.90 s, RF 20,520.08 s, and MLP 703.23 s. The best model (MLP) achieved a total inference time of 0.0277 s, or 0.000034 s per sample, whereas the stacking model required 75.2242 s, or 0.091961 s per sample.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 3 CDA Adoptability and Aircraft Descent Times
### Estimation of Aircraft Descent Time
#### 3.2.1 Aircraft Velocity and Lift Model  
For a straight-and-level flight at cruise altitude, the aircraft speed (velocity) is given by  
\[\mathrm{V_{TAS}}=\mathrm{a_{o}}M_{cruise}\sqrt{\frac{T}{T_{o}}} \tag{2}\]  
where \(\mathrm{V_{TAS}}\) is aircraft's true airspeed (TAS) in nautical miles per hour (knots), \(\mathrm{a_{o}}\) is the speed of sound at sea level in knots, \(M_{cruise}\) is the aircraft's Mach number at cruise altitude, and \(T\) and \(T_{o}\) are the temperatures at cruise altitude and at sea level, respectively. The lift coefficient, \(C_{L}\), can be calculated using the classical formula for the lift force, \(L\):  
\[L=C_{L}\frac{1}{2}\rho V^{2}S \tag{3}\]  
where \(\rho\) is the density of air in kilograms per meter cubic, \(V\) is the aircraft speed in meters per second, and \(S\) is the aircraft's wing area in square meters.  
In cruise flight, the lift force, \(L\), in Newtons, may be assumed to be equal to the aircraft's weight in kilograms, \(m\). Combining this relationship with Equation (3) and rearranging terms results in  
\[C_{L}=\frac{2 mg}{\rho V^{2}S} \tag{4}\]  
where \(g\) is the acceleration due to the earth's gravity. Assuming a no-wind scenario and that the flight path's angle in degrees is \(\gamma\), then the relationship between ground speed and true airspeed is given by  
\[\mathrm{V_{ground}}=\mathrm{V_{TAS}}\cdot\cos\gamma \tag{5}\]
###### Abstract
On the other hand, the generator-based GPP method, as proposed by [PERSON], [PERSON] [18], focuses on producing continuous climate time series for ensemble streamflow forecasts. In the study of [PERSON], [PERSON] [22], raw EWFs corrected using the GPP method demonstrated a skillful nine-day lead time for precipitation forecasts when compared to a historical resampling method across a basin in southern China. Furthermore, the uses of EPFs are also restricted by their relatively low spatial resolution compared with hydrological models [23; 24]. [PERSON], [PERSON] [25] emphasized that refining precipitation forecasts holds the key to enhancing streamflow forecast accuracy. Later studies concurred, suggesting that post-processing could improve the limitations of precipitation bias in the mean and insufficient spread in EPFs, which may result in better streamflow forecasting skills. For instance, [PERSON], [PERSON] [11] applied two post-processing methods to improve precipitation forecasts from the CMA, ECMWF, and NCEP over an eastern Chinese basin. They found that the useful forecasts can be significantly extended beyond 10 days after post-processing, which showed promising prospects for flood forecasting. In another study, [PERSON], [PERSON] [26] investigated the efficacy of post-processed EPFs in summer streamflow prediction over the Huaihe River basin and discerned notable improvements over 1\(\sim\)5 lead days. Although post-processing methods have been developed to obtain more reliable EPFs, their subsequent hydrological applications, particularly in mountainous regions prone to extreme weather events, remain challenging. Moreover, the efficacy of different kinds of post-processing methods in enhancing precipitation and streamflow forecasts, combined with the identification of skillful forecast lead times for summer streamflow prediction restored by a superior post-processing method, is well worth investigating. The primary objective of this study is to post-process EPFs and then predict streamflow through a hydrological model over a mountain river basin. This study examines four renowned EPFs, i.e., the CMA, ECMWF, JMA, and NCEP. Two popular post-processing methods, i.e., distribution-based BMA and generator-based GPP, were employed. Adopting both deterministic and probabilistic evaluation metrics, the study comprehensively evaluated the forecast skill of raw and post-processed EPFs. Subsequently, raw and post-processed EPFs were applied for summer streamflow prediction via a calibrated hydrological model.
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 5 Conclusions  
Cyber-physical systems (CPS) are networked by default, and private information is shared among the different components related to users, critical infrastructure, or business operations. In this context, it is essential to encrypt those communication links to protect such information. However, most solutions require physical devices to store a secret key, which is a very unsecure approach.  
Therefore, in this paper, an encryption scheme based on keys generated through physical unclonable functions (PUFs). Using PUFs, any attempt to access to the key will immediately and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N\) & **Use of RAM** & **Use of Program Space** & **Processing Time to Generate the First Key Sample** \\ \hline
4 & 12\% & 65\% & 120 μs \\
8 & 14\% & 67\% & 3.9 ms \\
10 & 14\% & 67\% & 5 ms \\
12 & 14\% & 67\% & 5 ms \\
16 & 14\% & 67\% & 5 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resource consumption. Results from the fifth experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposal** & **Memory Usage (ROM and RAM)** & **Processing Time** \\ \hline PUFKY [34] & 3 KB & 5.62 ms \\ ROPUF [49] & 2 KB & 4.6 ms \\ EB PUF (our proposal) & 22 KB & 5 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Resource consumption. Comparison to previous works.  
irreversibly change the key, and given one PUF, even the manufacturer cannot clone it into an identical one. The proposed key generator is based on magnetic materials to meet the low-cost and small size requirements of CPS. In particular, materials with an activated exchange-bias effect are employed, together with simple copper coils. The encryption process can be based on a simple XOR gate because of the robustness of the proposed key generator.  
Results prove the unclonable behavior of the proposed PUF and the security level of the described encryption scheme. On the other hand, the described solution also meets the requirements of resource constrained nodes in a CPS.  
The contributions described in this work are distributed among the authors as follows: All authors wrote the paper; [PERSON]-[PERSON] and [PERSON] contributed to the theoretical formalization; [PERSON] contributed to the proposal of the different simulation paradigms; and [PERSON] developed the global architecture and application model.  
The research leading to these results has received funding from the Ministry of Economy and Competitiveness through SEMOLA (TEC2015-68284-R) project and from the Ministry of Science, Innovation and Universities through VACADENA (RTC-2017-6031-2) project.  
The authors declare no conflict of interest.
## 2 Related Work
### Markov Transition Field (MTF)  
Markov Transition Field (MTF) can encode one-dimensional vibration signals into two-dimensional images [18]. It mainly uses Markov matrices to preserve time domain information and enables the encoding of dynamically transferred information. Figure 1 shows the MTF coding schematic.  
Given the one-dimensional time series, \(X=\{x_{t}\), \(t=1\), \(2\), \(\ldots\), \(T\}\). Firstly, divide \(X\) in groups of \(j\) points; then, discretise the value domain of these points into \(N\) equal parts, where the \(i\)th part is denoted by \(n_{i}\) (\(0<i\leq N\)); each point in \(X\) is mapped into a corresponding value domain \(n_{i}\). Finally, the transfer probability between the value domains \(n_{i}\) of each point is calculated according to the Markov chain, and in this way, an \(N\times N\) weighted neighbourhood matrix \(W\) is constructed, which can be represented as:  
\[W=\begin{bmatrix}w_{11}&\cdots&w_{1N}\\ \vdots&\ddots&\cdots\\ w_{N1}&\cdots&w_{NN}\end{bmatrix} \tag{1}\]  
where the value of the element \(w\) is determined by the probability that all sequence points in the value domain \(n_{t}\) are transferred to the value domain \(n_{i}\).  
In order to facilitate the expression of time series features, an \(M\times M\) MTF matrix is constructed according to the serial data, which can be represented as:  
\[M=\begin{bmatrix}m_{11}&\cdots&m_{1M}\\ \vdots&\ddots&\vdots\\ m_{M1}&\cdots&m_{MM}\end{bmatrix}=\begin{bmatrix}w_{t|x_{1}\in n_{t},n_{i}}& \cdots&w_{t|x_{1}\in n_{t},x_{M}\in n_{i}}\\ \vdots&\ddots&\vdots\\ w_{t|x_{M}\in n_{t},x_{1}\in n_{i}}&\cdots&w_{t|x_{M}\in n_{t},n_{i}}\end{bmatrix} \tag{2}\]  
where the element \(m\) represents the transfer probability from the value domain \(n_{t}\) of \(x_{i}\) to the value domain \(n_{i}\) of \(x_{j}\).  
Utilising MTF as a pre-processing method can have several advantages:  
1. The original signal and the 2D image are coded mapping relations, avoiding the loss of information of the original signal.
2. The dependency between each grouping and the time step is considered, which preserves the temporal correlation of the signal over time.
3. Different colours reflect the probability of transfer between different data, and the two-dimensional space amplifies the temporal information, facilitating greater performance of the residual network.  
Figure 1: MTF encoding.
## 5 Results
### Phase III: Requirements Specification
\begin{table}
\begin{tabular}{c c} \hline \hline
**FR01** & **Access for the Requestor to the CISI** \\ \hline Agent & Requestor (User authorized) \\ \hline Goal & Request for login and password of the requester; \\ \hline \multirow{3}{*}{Description} & The system must link each requestor registered in the system to the locations of its operation; \\  & Show these locations to selection by the requestor; \\ \hline Outcome & To allow access to the system. \\ \hline
**FR02** & **New order for maintenance** \\ \hline Agent & Requestor \\ \hline Goal & Access to the screen of service request; \\ \hline \multirow{6}{*}{Description} & By clicking on the “new request” icon, the system must verify the existence of a WO pending final evaluation of the service by the requestor; \\  & If there are any WOs, pending final evaluation, the system must communicate \\  & this pending WO to the user; \\  & The system must display a pop-up notification window for the requester to \\  & access the pending WOs and perform the evaluation; \\  & When no WO is pending, the system may release the screen for a new request. \\ \hline Outcome & Release screen of service request. \\ \hline
**FR03** & **Detail of input criteria** \\ \hline Agent & Requestor \\ \hline Goal & Specify criteria for maintenance of HVAC systems; \\ \hline \multirow{6}{*}{Description} & The system must display a field with “kind of equipment” with the following selection options for the requester: Air conditioner, Refrigerator, Drinker, Freezer; \\  & When selecting the “Air Conditioning”, the system must offer the following selection options for the requester: Split or Window; \\  & The system should display a “Service Type” field with the following selection \\  & options for the requester: Installation or Repair; \\  & If the requester selects “Installation”, the system must display the options: \\  & Do you have wiring? and Do you have a drain system? Each option should \\  & exhibit with them checkboxes YES and NO for the requestor’s choice; \\  & If the requestor selects “Repair”, the system must display the “equipment \\  & height” field with the following selection options: up to 2 m, 2 to 3 m, up to 3 m. \\ \hline \multirow{6}{*}{Description} & The system must display a “Failure” field with the following options for \\  & selection by the requester: lack of refrigeration, freezing, leakage, noise, \\  & equipment does not start, cleaning; \\ \cline{1-1}  & The system must display a “best service time” field for the requester to write \\ \cline{1-1}  & the best option; \\ \cline{1-1}  & Link the selection of air conditioning, refrigerator, freezer, or water cooler to \\ \cline{1-1}  & the “HVAC” service group; \\ \hline Outcome & Service criteria detailed. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specification of functional requirements for the redesign of the information technology system used in the issuance of WOs for the maintenance of HVAC systems.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 2 Related Work
### DUDe Association
Furthermore, another study conducted by [PERSON], [PERSON], and [PERSON] [18] outlines an association technique as a sum-utility maximization issue that jointly augments the downlink sum rate and limits uplink sum power. Another group of research studies [19; 20; 21; 22] uses game theory to get an optimal solution and conceive a model for a rival attitude of UE, BSs, and the access controller of a HetNet; for example, the stable matching technique that was used by different works to evaluate the uplink-downlink user association benefit separately. For instance, there is a work carried out by [PERSON] et al. [19] which presents a solution to downlink user association for small cell networks based on utility functions, taking into consideration the rate and fairness for cell edge users. To provide a better BS services to UE experiencing a relatively low SINR, [PERSON] et al. [19] propose a promotion function representing the amount of promotion given to each class of users. Furthermore, the Nash bargaining technique was used by [PERSON] et al. [20] to get beneficial user association maximizing the sum of log-scale uplink and downlink energy inefficiency among all UEs. The proposed model aims to improve the uplink and downlink system capacity and reduce the uplink transmit power by introducing the Hungarian method to adjust the model to a Nash bargaining solution. Additionally, in another study by [PERSON] et al. [21], the authors coordinate between matching and coalition games to manage the uplink user association issues based on optimizing the utility function in terms of packet success rate and delay. Moreover,in their paper, [PERSON], [PERSON], and [PERSON] [22] handle the cooperative spectrum sharing issue between multiple primary and secondary users as a two-sided market, and study the market equilibrium under both complete and incomplete cases. The outcome attests that the performance gap between Pareto-optimal equilibrium and robust equilibrium for primary users is considerably small for the critical case where every primary user guaranteed the utility under all possible misrepresentation actions of secondary users. The work in this paper extends prior art--most notably the recent works conducted by [PERSON] et al. [23] and [PERSON], [PERSON], and [PERSON] [24]. The main contributions are summarized in Table 1.
## 3 Fabrication Methods and Testing Setup
### Testing Setup  
Lab-based testing method is shown in Figure 5a. First, the SAW sensor with the HfO\({}_{2}\) sensing material is connected to the oscillator via wire bonding, and the oscillator output is linked to the mixer. Next, the reference SAW device is attached to a different oscillator using wire bonding and connected to the same mixer. The mixer outputs the frequency difference between the two SAW devices. This sine wave signal passes through a low-pass filter (LPF) to remove high-frequency components and is converted into a square wave by a comparator. Finally, the FPGA converts this square wave signal into a digital signal, enabling real-time display of the frequency difference on a PC. The operating mechanism has already been explained in Figure 1b. To minimize signal variations caused by external interference, a 3D-printed shield was developed to protect both the oscillator's electronic system and the sensor, as shown in Figure 5b, allowing for stable frequency changes of the sensor to be observed.  
Figure 4: (a) Fabrication procedure of the SAW-based UV-C corona sensor, and (b) detailed process of HfO\({}_{2}\) deposition using ALD.
# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
## 2 Materials and Methods
### Explosive Isometric Contractions (Strength and Acceleration Signals)  
Explosive knee extensor strength was assessed using an isokinetic dynamometer (Humac Norm II, CSMI, USA). The participants performed three explosive MVCs of \(\sim\)3 s at 60\({}^{\circ}\) of flexion (0\({}^{\circ}\) = full extension), interspersed by rest periods of 30 s. The participants were consistently instructed to produce force \"as fast and hard as possible\" during the explosive MVC and to keep the maximum effort as long as requested by the examiner. To minimize the dampening effect occurring at the interface between the shin and the lever, and simultaneously to avoid pain, a standard soccer hard shin guard was used [1, 22]. Our group has used this same procedure in previous investigations [1, 10, 16, 22]. An accelerometer (EMG 830c\({}^{\circledR}\), EMG System do Brasil, Sao Jose dos Campos, SP, Brazil) was placed on the shin guard to measure ACC signals in the x, y, and z axes (measured in g, Figure 1). Both torque and ACC signals were sampled at 1 kHz and synchronized (EMG 830c\({}^{\circledR}\), EMG System do Brasil, Sao Jose dos Campos, SP, Brazil). Both the right and left limbs were tested randomly.  
Figure 1: Illustrative image demonstrating the accelerometer placement during the explosive strength assessment. The accelerometer was fixed on the shin guard, with detailed orientation along the \(x\), \(y\), and \(z\) axes.
## 2 Preliminaries to the C-HARQ Protocol  
The basic idea behind C-HARQ is to merge cooperative ARQ with a hybrid ARQ technique, so as to utilize the benefits of both the schemes. While CARQ can provide a form of spatial diversity, hybrid ARQ introduces error correction capabilities, thus presenting the possibility of achieving both high throughput and efficient performance. Before going into the details of C-HARQ, a brief discussion of hybrid ARQ and CARQ is presented in the following subsections.