esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based Framework for Smart Water Supply Systems Management
## 3 Related Work
In addition, they developed a visualization tool that displays on a map the leaks detected by the CEP. The work is based on imperative processes to control the behavior of water systems. Therefore, managers must anticipate all operational scenarios, reducing the flexibility of the solution. * [PERSON] et al. [14] proposed an intelligent application for real-time monitoring of water supply systems based on CEP technology to anticipate risks and to control devices remotely. Despite having the objective of monitoring in real-time, the work does not demonstrate the scalability of the solution in a real environment. Moreover, the paper does not take into account the flexibility that water management processes need. * [PERSON] et al. [15] developed a platform called WaterWiSe that manages and analyzes data from a water network through wireless sensors. WaterWiSe supports a variety of applications, including continuous water demand and hydraulic state forecasts, online detection of events such as pipe leaks, and data mining to identify long-term trends. They deployed a platform to represent water network management. However, their solution only takes into account the infrastructure, which generates reports to indicate the occurrence of predefined issues, but does not deal with the decision-making process. * [PERSON] et al. [16] argue that replacing the imperative with the declarative paradigm is essential to make business process management more flexible. The authors demonstrated that the adoption of the declarative paradigm could (a) avoid changes in the business process to relax stringent rules, (b) provide support for changes at the instance level (ad-hoc change) and type (evolutionary change), and (c) simplify the distinction between mandatory and optional restrictions. The focus of their work is not water management systems. * [PERSON] et al. [17] observed that existing reference workflow often has to be adapted according to specific contextual factors. The authors proposed extending basic changing operations, such as task insertion and removal, with a pattern catalog. The work only reinforces the need for more flexibility in workflow-based (imperative) processes. * [PERSON] et al. [18] proposed an optimization model that takes into account the principles of value-based business process management, from an economic perspective, to determine the optimal level of process flexibility. The authors evaluated the model through an experiment conducted using production processes from an international company in the semiconductor industry. The work reinforces the arguments presented in this document: water management business processes must provide sufficient control to respect defined policies and the flexibility to allow all actions not to be restricted by those policies. Thus, subsequent to the literature review and to the best of our knowledge, this is the first work to propose the application of declarative business processes to the efficient management of water systems based on IoT infrastructures.
## 1 Introduction
### Purpose and Objectives  
This hybrid technical and survey paper aims to explore the emerging threat that deepfake social engineering poses to corporate enterprises in detail. It also provides a framework that aims to enable organizations to respond effectively to these emerging risks. This synthesis is aimed at cybersecurity professionals across different industries to address the gap between rapid developments in deepfake technology and the corresponding defensive strategies against it required to protect corporate assets. The specific objectives of this paper are as follows:  
* Identify deepfake threats;
* Evaluate detection techniques;
* Develop defensive strategies;
* Create a comprehensive model;
* Provide actionable guidelines;
* Contribute to corporate security posture. To achieve these objectives, in this paper, we intend to provide organizations with the knowledge and tools necessary to avoid and mitigate deepfake threats. This will hopefully help improve their ability to protect sensitive information, maintain trust, and ensure operational continuity in an increasingly digital and interconnected world. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Authors** & **Main Contribution** & **Threat Landscape** & **Detection Techniques** & **Organizational Impact** & **AI and ML Tools** & **Research Challenges** \\ \hline
[5] & Provides a broad survey on the creation and detection of deepfakes, focusing on generative models and AI-driven strategies. & ✓ & ✓ & – & ✓ & Organizational impact not covered \\ \hline
[8] & Introduces deepfakes as a threat to face recognition systems; highlights the importance of detection but less on enterprise-level mitigation. & – & ✓ & – & – & Threat landscape, organizational impact, and AI and ML tools not covered. \\ \hline
[9] & Discusses the current and future trends in deepfakes, with partial mention of organizational security measures. & ✓ & ✓ & \(\star\) & ✓ & Organizational impact mentioned briefly. \\ \hline
[6] & Analyzes face manipulation techniques and ML-based detection methods; focuses on image/video authentication tests. & ✓ & ✓ & – & ✓ & Organizational impact not covered. \\ \hline
[10] & \multicolumn{1}{p{142.3 pt}}{Presents a review of deepfake synthesis and detection approaches; focuses on state-of-the-art generative adversarial networks and AI-based defense.} & ✓ & ✓ & – & ✓ & Organizational impact not covered. \\ \hline
[1] & Analyzes deepfake’s risks to national security by highlighting potential disruptions to communications and military operations while proposing coordinated detection and mitigation strategies. & ✓ & ✓ & \(\star\) & AI and ML tools partially mentioned. \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of related work on deepfake detection and mitigation (discussed: ✓; never mentioned: –; partially mentioned: \(\star\)).
## 4 Evaluation Performance  
### Evaluation in Simulated Data  
The denoising results of simulated data are demonstrated to evaluate the performance. In Figure 5a, the noisy data with the size of \(500\times 500\) pixels are simulated under the speckle radius of 1 pixel, and the enhanced wrapped phase using the proposed method manifests a significant improvement on the noisy data as shown in Figure 5b. The phase unwrapping of Figure 5b using the least-squares algorithm by the discrete cosine transform (DCT) [40] is implemented in Figure 5c, which has a low error distribution as shown in Figure 5d. The Mean Squared Error (MSE) comparison between the noisy data and denoised data is provided by Table 1, and it can be seen that the noise is greatly reduced from the noisy wrapped phase.  
Figure 5: Simulated data (the speckle radius of 1 pixel): (**a**) Simulated noisy wrapped phase; (**b**) denoising result of (**a**) by the proposed method; (**c**) unwrapped phase of (**b**); (**d**) error distribution map of (**c**).  
By adjusting the size of the aperture, the simulated data in the speckle radius of 2 pixels are analyzed as shown in Figure 6a. The performance is demonstrated from the denoising result, and the denoising effect is illustrated by visualization in Figure 6b. In Figure 6c, the phase unwrapping is implemented by DCT for illustrating the denoising effect, and Figure 6d shows the low error distribution for illustrating the feasibility. In Table 1, the MSE comparison between the noisy data and denoising data indicates the good noise suppression ability of the proposed method. Besides, the calculation time for a wrapped phase with the size of \(500\times 500\) pixels is 3.45 s.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 2 NB-IoT  
NB-IoT technology is an evolution that builds upon Long-Term Evolution (LTE) [15; 16], a widely adopted standard for high-speed wireless communication, as shown in Figure 2. Collaborative efforts led by 3 GPP have resulted in the development of NB-IoT, which specifically addresses the demands of CIoT. NB-IoT operates on narrow bandwidths [17], optimizing spectral efficiency and facilitating seamless integration into existing LTE infrastructure. It uses licensed spectrum frequencies, typically in the sub GHz range, ensuring reliable and secure communication over long distances of 10\(+\) km [18]. This inheritance from LTE provides a solid technological base and allows efficient co-existence with other LTE services.  
NB-IoT technology enhances its reach through three distinct Coverage Enhancement (CE) levels--CE Level 0, CE Level 1, and CE Level 2--to improve connectivity in areas with varying signal strengths [19]. These levels, predetermined by the network, dictate the repetition frequency of downlink and uplink traffic to ensure that User Equipment (UE) in low-coverage zones remain connected. CE Level 0 caters to areas with satisfactory coverage, providing a signal boost of +0 dB. For environments requiring more assistance, CE Level 1 steps in with a boost of up to +10 dB. CE Level 2 targets the most challenging conditions by  
Figure 1: The relative power consumption (mW) among various radio operational states in NB-IoT.  
Figure 2: The operational states of NB-IoT, showcasing the stages of the random access procedure and connection setup are highlighted in yellow; the data transmission phase and the CONNECTED state, in green; and the IDLE state, in red.  
offering a substantial increase of up to +20 dB and permitting up to 128 message repetitions. This advancement increases the maximum coupling loss (MCL) capacity from 144 dB to 164 dB, improving connectivity in \"deep indoor\" locations where IoT devices may be placed, such as basements and parking structures, areas traditionally problematic for the penetration of 2G, 3G, and 4G (LTE) signals.  
NB-IoT technology aims to use energy efficiently, particularly for small data transfers [20]. In release 13, two main methods were introduced to optimize this process [21]. First, the User Plane (UP) optimization, or RRC Suspend/Resume method, simplifies the UE that switch from IDLE to CONNECTED states. It allows devices to \"pause\" and later \"resume\" their connection without redoing the entire setup process, saving time and energy. Second, the Control Plane (CP) optimization, or Data-over-NAS method, sends small user data packets through the control channel and bypasses the usual steps of setting up a data connection, further reducing energy use and simplifying data transmission. Both methods were designed to streamline small data transfers; however, CP is more efficient and less energy-intensive [22].
## 3 Methodology
### Feature Fusion Module  
In CD tasks, there are different forms of bitemporal feature fusion; [PERSON] et al. [14] used simple channel concatenation in the decoder, and [PERSON] et al. [31] studied difference maps using pixel-wise subtraction. As for the decoder, channel concatenation is commonly used for fusing features at the same layer of the encoder and decoder. However, simple channel concatenation, subtraction, and element-wise summation do not effectively explore the relationship between bitemporal images and do not achieve the interaction between channels well. In view of this, this paper adopted different feature fusion approaches in the encoder and decoder stages.  
For the encoder phase, we first concatenated the bitemporal features along the channel dimension, and then the concatenated features underwent a CNN block, which mainly contained a \(1\times 1\) convolution, BN, and ReLU. The CNN block here could significantly strengthen the nonlinear representation of the network, in addition to reducing the dimensions of the features and realizing the mutual information effect between channels for fusion with the same-layer features in the decoder.  
For the purpose of fully exploiting the relationship between the bitemporal features, a new module TA was introduced in the decoder, as shown in Figure 6. The left, right, and middle are the features of each layer of the decoder after the MFE, where the left is the left branch, which corresponds to the T2 image branch of the encoder, the right is the right branch, which corresponds to the T1 image branch of the encoder, and the middle corresponds to the T1 and T2 fusion feature branch of the encoder. Particularly, we used the left and right branch features to enhance the fusion features of the middle branch. The specific operations were as follows: Firstly, the left and right moved through a spatial attention module (SAM) [25], respectively, and the two output attention maps were then multiplied by the original middle branch for adaptive feature refinement. Secondly, the two obtained outputs were channel concatenated, and the obtained outputs were finally combined with the original middle fusion features through a summation operation, followed immediately by a \(3\times 3\) convolution for the fusion features to avoid the aliasing effect introduced by element-wise summation.  
Figure 5: The structure of the MFE.
## 4 Beam Profile Measurements  
### Experimental Setup  
Figure 9 shows the experimental setup for the measurement of the leaky radiation beam profile radiated from a plate strip in water. The underwater beam profile measurement system consists of an XYZ three-axis scanner, a hydrophone (ONDA HNR-0500, frequency range: 0.25-10 MHz) with a pre-amplifier (ONDA AH-1100, frequency range: 0.005-25 MHz), a waveform generator (Agilent 33521A), a gated amplifier (RITEC GA-2500A), a broadband receiver (RITEC BR-640), a noise suppressor with a 1.0 MHz center frequency (ORBISSYS NS-0017) and a computer with master control software (UTEX WinspectTM). Material and dimensions of the 400 mm long plate strip used in the experiment are the same as those in the RSI model (1.5 mm thickness and 15 mm width); also, the aperture size is 18 mm \(\times\) 15 mm, the same as that in the RSI model. The material of an ultrasonic wedge with 19 mm height and 20 mm width is Lucite (\(C_{L}\) = 2370 m/s); the incidence angle of the wedge is 70\({}^{\circ}\) for the generation of the A(0,0) mode Lamb wave at 1.0 MHz. The wedge was mounted with a PZT (lead zirconate titanate) transducer with a 0.5 inch diameter and 1.0 MHz center frequency (GE benchmark series) at three different excitation source positions (\(d\) = 300, 350, 400 mm), and the leaky radiation beam was measured for each excitation source position. A four-cycled tone burst signal generated from the waveform generator was input to the transducer with signal amplification by the gated amplifier. The leaky wave radiated from the plate strip was measured by the hydrophone; then, the measured wave signal was transferred to the computer after amplifying and band-pass filtering (from 25 kHz to 5 MHz) by the pre-amplifier, the noise suppressor, and the broadband receiver. Scanning volume size was 90 mm (\(x\)) \(\times\) 50 mm (\(y\)) \(\times\) 90 mm (\(z\)), the same as the analysis domain of the RSI model, and scan step was 1 mm. In the post-processing process, a 3D beam profile was reconstructed by mapping the measured 3D matrix data comprising the maximum peak (\(V_{Peak}\)) to the space coordinate; as shown in Figure 9c, the maximum peak (\(V_{Peak}\)) of the gated signal beyond the threshold value was extracted and saved. Finally, the reconstructed beam profile was normalized by the maximum value in the far-field domain.  
Figure 9: _Cont._
# A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis
## 2 Materials and Methods
### Validation
#### 2.2.2 Trials  
A straight line 14 m test was performed five times. This involved walking (pushing) the unmanned wheelchair at a constant pace from the intersection of the halfway line and the sideline and stopping on the intersection of the halfway line and the baseline. As the court is FIBA level one approved, the accuracy of court markings have been independently scrutinised, ensuring the distance is 14 m. A centre court-to-baseline linear walking test was then also performed five times under the same unmanned walking (pushing) conditions. To validate distance at different time increments for both of these tests, a laser range finder (Jenoptik LDM301) with a 100 Hz sampling frequency set up on a fixed tripod was used. This provided a reference measure to independently monitor the distance of the wheelchair over the testing time, allowing for the comparison of velocity curves. Three half court lamps were then performed, initially in a clockwise direction and then in an anticlockwise direction with the chair again walked at a relatively constant pace. As the centre of the chair was kept on the line, the distance for the internal wheel and the external wheel could be deduced. As this test was aimed at assessing trajectory accuracy, the laser was not used.
# Towards a Decentralized Collaborative Framework for Scalable Edge AI
## 2 Related Work  
The field of decentralized learning and model-sharing has explored various approaches and methods to overcome the challenges and limitations of traditional learning paradigms [8; 11].  
FL is often seen as a machine learning paradigm where a central server distributes the training process to a group of decentralized clients. These clients then train a shared global model using their individual datasets, which are never shared [8; 14; 17]. The FL method has been utilized to improve the predictive accuracy of virtual keyboards, among other applications [7; 18].  
Several research papers have focused on Federated Learning (FL), examining its advances, limitations, and the need for new designs and approaches [7; 17; 19]. Other studies have investigated the impact of heterogeneity on model accuracy and performance in decentralized learning scenarios [14; 20]. Additionally, research has been conducted on communication efficiency, privacy preservation, collaboration, and knowledge transfer in decentralized learning systems [10; 21; 22; 23].  
One key approach for transfer learning is knowledge distillation (KD). Beyond model compression, knowledge distillation's applicability extends to scenarios where teacher and student models are pre-trained on different data subsets. This application of knowledge distillation aims to consolidate the distinct knowledge each model possesses, a concept that is increasingly relevant in decentralized collaborative learning [24; 25] and Federated Learning [25; 26; 27].  
The proposed Model Discovery and Distillation (MDD) module in the current research builds upon these works to improve efficiency, scalability, privacy, collaboration, and knowledge transfer among learning entities.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 8 Discussion
### Comparison of the Two-Phase Devices
Scroll systems represented another potential technology to expand a fluid in the two-phase state region, as they can manage a liquid mass fraction higher than 0.9 [8] with an acceptable overall efficiency in the range 0.30-0.60. Vane expanders were extensively tested in heat pumps and cooling applications. They appeared suitable to manage two-phase flows with an overall efficiency between 0.20-0.55, despite their limits, which were the need to reduce leaks and friction. Piston devices seemed to be another suitable candidates for expanding a two-phase fluid, although their overall efficiencies fell in a wide range from 0.10 to 0.60 depending on the considered mechanism. Nonetheless, some authors highlighted that piston reciprocating machines suffered damage to the cylinder surface and rings due to their reduced ability to withstand the droplets that limit lubrication. A possible cause impacting the behaviour in the wet expansion may be the flash expansion that depends on the pressure and volume variations of the operating chamber and determines the liquid phase agitation and the vapor generation. The volumetric performances were comparable for all the technologies, although vane devices presented the lowest value, probably due to their higher leaks and pressure losses. The comparison revealed that the lowest and highest displacement belonged, respectively, to vane devices and screw machines (Figure 21), thus, affecting the mass flow rate processed by the machine. In the applications, the BVR was of the same order of magnitude (in the range of 2-8) for the scroll, screw, and vane machines, while it increased for pistons (up to 21). As a result, the use of each expansion device under designed operating conditions appeared feasible for a selected power range (Figure 22). For this reason, screw machines led to the highest output power for heat pumps, refrigeration, and power production. At the same time, piston and vane systems were attractive for small-scale applications because of their lower values of delivered power. Figure 21: Distribution of the displacement values for the analyzed expanders. Furthermore, another aspect that should be considered in the choice of a two-phase expander is its complexity that concerns the mechanical architecture and the control of the flow through the ports (see Table 5). The survey suggested that a high mechanical complexity characterized screw and reciprocating piston machines [84]. Nonetheless, different architectures for piston systems provided devices with the lowest complexity, such as rolling, swing, and free expanders [84].
# On Parameter Estimation for Bandlimited Optical Intensity Channels
###### Abstract  
Parameter estimation is of paramount importance in every digital receiver. This is not only true for radio, but also for optical links; otherwise, subsequent processing stages, like detector units or error correction schemes, could not be operated reliably. However, for a bandlimited optical intensity channel, the problem of parameter estimation is strongly related to non-negative pulse shapes satisfying also the Nyquist criterion to keep the detection process as simple as possible. To the best of the author's knowledge, it is the first time that both topics--parameter estimation on the one hand and bandlimited intensity modulation on the other--are jointly investigated. Since symbol timing and signal amplitude are the parameters of interest in this case, the corresponding Cramer-Rao lower bounds are derived as the theoretical limit of the jitter variance generated by the related estimator algorithms. In this context, a maximum likelihood solution is developed for the recovery of both timing and amplitude. Since this approach requires a receiver matched filter destroying the Nyquist criterion of the non-negative pulse shape, we compare it to a flat receiver filter preserving the required orthogonality property. It turned out that the jitter performance of the matched filter method is close to the Cramer-Rao lower bound in the medium-to-low SNR range, but due to inter-symbol interference effects an error floor emerges at higher SNR values. The flat filter solution avoids this drawback, although the price to be paid is a larger noise level at the filter output, so that a somewhat increased jitter variance is observed.  
parameter estimation; optical wireless communications; intensity modulation; pulse shaping +
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