esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 3 Experimental Setup
### Evaluation Metrics  
Two possible errors can be considered when dealing with SAD systems: a false positive (FP), this is the identification of speech in a segment where the reference identifies non-speech, and a false negative (FN), this is the missed identification of speech in a segment where the reference identifies speech. Using these two errors, the false positive rate (FPR) and false negative rate (FNR) can be computed according to the following equations:  
Figure 3: Convolutional recurrent neural network model proposed for the SAD task.  
\[\text{FPR}=\frac{\text{T}_{\text{FP}}}{\text{T}_{\text{non-speech}}}\,, \tag{12}\] \[\text{FNR}=\frac{\text{T}_{\text{FN}}}{\text{T}_{\text{speech}}}\,, \tag{13}\]  
where \(\text{T}_{\text{FP}}\) and \(\text{T}_{\text{FN}}\) are, respectively, the total false positive time and total false negative time for the SAD hypothesis, \(\text{T}_{\text{non-speech}}\) represents the total annotated non-speech time in the reference, and \(\text{T}_{\text{speech}}\) represents the total annotated speech time in the reference. Following the evaluation protocol originally proposed in the Fearless Steps Challenge [72], results are reported according to the detection cost function (DCF), as shown in the following equation:  
\[\text{DCF}=0.75\cdot\text{FNR}+0.25\cdot\text{FPR}\,. \tag{14}\]  
As it can be observed, false negative errors were considered more important than false positive errors in the original evaluation. In addition to FPR, FNR and DCF, which are metrics depending on the chosen threshold, results of the system are also reported using the area under the ROC curve (AUC) metric, measuring the area underneath the entire receiver operating characteristics (ROC) curve, and the equal error rate (EER), the error rate at which the FNR and FPR is equal. Both metrics provide an overall measurement of performance for all the possible threshold applied to the neural networks scores. Furthermore, the desegregated performance for all possible operating points is described using the detection error trade-off (DET) curve, showing FPR values versus FNR values.
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 2 Related Works
Using topic subscription correlations across peers and dynamically aggregating them on a skewed distributed hash table, [PERSON] et al. [23] established a TBPS system that is efficient and scalable in message distribution. A routing approach that considers the underlying routing structure while constructing the multicast trees for locality preservation was presented to reduce the time required to send messages. [PERSON] et al. [22] invented SpiderCast, a distributed technology that dynamically organizes peers with similarly associated workloads into an overlay network that can handle TBPS traffic. SpiderCast is the perfect solution because of the number of nodes, with only a few associated subscriptions per node. These techniques can be used to oversee broker-based TBPS systems in which dispersed broker groups mediate the communications between various parties (microservices on IoT devices). In microservice-based IoT systems, however, it is impractical to implement such approaches directly on IoT devices owing to device limitations. In addition, broker/peer locality awareness is lacking, making it difficult to minimize latencies for delay-sensitive applications at scale efficiently when using these approaches. Establishing scalable broker-based TBPS systems, e.g., deploying pub/sub brokers in the cloud, such as Dynamoth [24], is another interesting method for delivering data in large-scale microservice-based IoT systems. Dynamoth is a scalable pub/sub middleware that can be placed on the cloud, and serves as a data delivery service for both data producers and consumers. Dynamoth is a cloud-based service that allows external customers to use a scalable and load-balanced topic routing service across brokers. On the other hand, long delays in data flow from clients to cloud brokers may cause this solution to not work for delay-sensitive IoT applications. In another approach, distributed brokers may be placed closer to the customers and coordinated with brokers in the cloud to combine the best features of both edge computing and cloud computing. PubSubCoord [25] places brokers in the network periphery while routing servers are placed in the cloud to reduce latency. Despite this, the authors in [25] did not take advantage of topic correlations across neighboring edge brokers to minimize latency. To further enhance system efficiency, it is crucial to offer practical means for Edge/Fog servers to connect and collaborate [26]. Many IoT users will benefit from combining Edge and Cloud IoT services. Therefore, this study aims to supply a coordinated method for distributed pub/sub brokers and techniques for constructing broker overlay networks in order to facilitate massive-scale cooperation and data sharing among microservices in IoT systems.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 5 Conclusions  
In this study, we propose a channel assignment algorithm for multicast in multi-channel multi-radio wireless mesh networks. We investigate the drawback of the MCM algorithm and try to find the solution to minimize network interference and enhance network throughput. MCM only considers one-hop neighbors for channel assignment. This mechanism may yield HCP, so there is network interference among wireless nodes when they communicate with each other at the same time and this problem might have an influence on overall network throughput. Our simulation results show that the performance of MCM is much worse than that of MICA because of network interference. Accordingly, our approach focuses on reducing network interference by considering every pair of nodes in a multicast tree for channel assignment. By minimizing interference among wireless nodes  
Figure 6: Impact of a different number of multi-receivers.  
Figure 7: Comparison of end-to-end packet delay.  
in a wireless network, we can improve overall network throughput and reduce end-to-end packet delay. The performance evaluation shows that our algorithm outperforms the MCM algorithm in terms of average packets received by multi-receivers and an average end-to-end delay in a given network environment.  
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8601-16-1009) supervised by the IITP (Institute for Information & Communications Technology Promotion).  
All authors contributed equally to this work. [PERSON] designed the main concept of MICA algorithm and wrote the paper. [PERSON] compared the performance of MICA and MCM, conducted the experiments, and wrote the simulation section.  
The authors declare no conflict of interest.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### Quality Metric  
The quality parameter used for LoRa and Zigbee transmissions is the BER measured by the transceiver modules. BER is a parameter that represents the number of bit errors divided by the total number of bits received in a transmission. For 5G transmissions, the SS-RSRQ parameter measured by the SA is used as the quality parameter because it is used in 5G NR networks to determine the radio channel quality.
# Fuzzy Model Predictive Control for Unmanned Helicopter
### Motivation  
Apart from the many advantages of helicopters, helicopter dynamics are highly dependent on environmental conditions and show significant differences across various flight states. In addition to well-known flight dynamics principles expressed by the equations of helicopter motion [3], there are various phenomena that affect rotorcraft behavior, which are described in the text below.  
A key example is the occurrence of the vortex ring state during the descent of the helicopter at low forward speeds. A vortex ring state occurs when the rotor produces a recirculating flow, where the induced downwash interacts with the incoming air, forming a toroidal vortex around the rotor disk. The formation of this phenomenon leads to low-frequency oscillations of the main rotor, which can result in potential control problems and loss of stability. Since this phenomenon highly affects the behavior of a rotorcraft, it is necessary to properly model the vortex ring state while analyzing helicopter dynamics. A proposed methodology for modeling the vortex ring state is presented in reference [4].  
Another example of varying helicopter dynamics in different flight conditions is the occurrence of dissymmetry of lift on the main rotor during forward flight. Advancing blades experience much higher airflow velocities than retreating blades, leading to unequal lift on opposite sides of the rotor disc. These flight conditions not only create blade flapping and rotor vibrations but can also lead to stalling in the retreating blades and rapid rolling of the helicopter.Detailed computation of asymmetric lift loads on the main rotor during forward flight using the finite volume method is described in reference [5].  
Since the rotorcraft's ability to generate thrust is based on inducing downward-directed airflow velocity, helicopters operating near the ground experience an increase in lift force due to the ground effect. The airflow induced by the main rotor stagnates near the ground, creating an area of increased static pressure. This effect is highly dependent not only on altitude but also on flight velocity. As forward flight velocity increases near the ground, a horseshoe-shaped ground vortex forms in front of the rotor. At sufficiently high speeds, this vortex is suddenly swept back under the helicopter, and the ground effect no longer contributes to the lift generation. Guidelines for modeling the ground effect are presented in reference [6], and various methods for modeling wakes in rotorcraft dynamics are presented in the paper cited in [7].
# Multi-RAT Orchestration Method for Heterogeneous Wireless Networks
## 5 Discussion  
Orchestration of multi-RAT heterogeneous wireless networks is not a trivial task. Implementation of such a solution in real network situations requires the use of continuously monitored indicator values, which are measured and/or estimated in either the terminal or at a network element--due to continuously changing conditions in the existing wireless environment. This is possible only by continuous measurements reported to the orchestrator established above all involved RATs. The appropriate decision-making algorithm and process for solutions we proposed in the paper also need to be deployed. The use of algorithms that are too simple may lead to, e.g., frequent path switching or selecting an inappropriate RAN from an energy efficiency (or other) perspective. An interesting approach, also in terms of research, could be to add artificial intelligence algorithms and machine learning techniques to build a self-learning and self-organizing decision-making module in the orchestrator, based on historical data from a given geographical area and period of time. In some cases, however, it may also not provide an optimal solution, i.e., in cases where the radio environment changes without specific behavior. The proposed solution is adequate for testing and providing conclusions for its practical application.  
The proposed solution, as a hardware-software prototype with proof-of-concept algorithm, indicates practical power consumption reduction at a very attractive level, i.e., 11-42%, depending on the scenario analyzed under working conditions with 4G/LTE and Wi-Fi wireless networks. We also assume that the terminals of 5G/NR would have similar efficiency conditions as measured in the testbed 4G/LTE, in the case of broadband communications related to high throughput data transfer, due to similar modulation and coding schemes and DRX mechanism. Therefore, a significant level of power reduction should also be achieved in the case of 5G-Wi-Fi orchestration, but this requires further measurements and confirmation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RAT** & **Data Volume [Mb]** & **Total Energy Consumption** & **Total Energy Efficiency** \\  & & **[mWh]** & **[Mb/mWh]** \\ \hline Wi-Fi & 2640 (\(-\)23\%) & 55 (\(-\)22\%) & 48 (+7\%) \\ LTE & 2531 (\(-\)27\%) & 98 (+27\%) & 26 (\(-\)42\%) \\ Multi-RAT orchestrated & 3444 & 77 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of aggregated KPIs for two different scenarios with indication of relative gains for Scenario A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RAT** & **Data Volume [Mb]** & **Total Energy Consumption** & **Total Energy Efficiency** \\  & & **[mWh]** & **[Mb/mWh]** \\ \hline Wi-Fi & 963 (\(-\)39\%) & 55 (\(-\)42\%) & 18 (+0\%) \\ LTE & 1687 (+7\%) & 106 (+19\%) & 16 (\(-\)11\%) \\ Multi-RAT orchestrated & 1576 & 89 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of aggregated KPIs for two different scenarios with indication of relative gains for Scenario B.
## 4 Overview of the Privacy and Security Requirements in a BYOD Environment
### IAS-Octave Security Criteria and Privacy Requirements  
The IAS-Octave classification extends the CIA triad framework by introducing additional security requirements: accountability, auditability, trustworthiness, non-repudiation, and privacy preservation [22]. Accountability and auditability contribute to establishing trustworthiness, which is closely associated with the authenticity requirement. Alternatively, it has been proposed that these three requirements can be combined to  
Figure 3: Development of BYOD security and privacy requirements [22].  
form the concept of authenticity [22]. Therefore, authenticity is essential to the standard set of privacy and security requirements for BYOD. It ensures accurate monitoring and verification of BYOD users' identities, establishing trust between BYOD devices, organizations, and their resources. Additionally, including privacy-preservation and non-repudiation requirements is crucial as part of the BYOD security framework. Privacy preservation ensures the security and monitoring of all information, end users, networks, and resources involved in the organization's BYOD implementation. On the other hand, non-repudiation guarantees that a BYOD device cannot later deny its signature's validity or an event that occurred within the organization. This requirement aids in tracing the origin of any BYOD device that poses security issues [40]. Thus, the privacy requirements for BYOD are derived from the CIA triad model and supplemented with three additional standards from the IAS-Octave framework: privacy preservation, non-repudiation, and authenticity.
## 3 Results
### Experimental Results and Analyses
#### 3.2.1 Comparative Experiments on Datasets  
In this section, we analyze the performance metrics of the AlexNetBN model after training with different datasets to validate the effectiveness and importance of data augmentation techniques in model training.  
Figure 5a shows that the enhancement of the dataset greatly improved the model performance, and it can be seen that the model trained using the enhanced dataset had a reduced change in accuracy after the fifth round of training, and tended to converge with a recognition accuracy of more than 90%; whereas the model trained using the original dataset began to converge only after the ninth round, and had a recognition accuracy of only about 80%. As can be seen from Figure 5b, in the model training after adding the BN layer, the number of completely correctly recognized license plate numbers (PFC) of the model trained with the enhanced dataset was significantly larger than that of the model trained with the original dataset, and at the same time, the PFC value was very sensitive to the occurrence of recognition errors, which suggests that it was less likely to recognize license plate numbers incorrectly in the model trained with the enhanced dataset. From the analysis of Figure 5a,b, it can be concluded that, under the same model architecture, the model trained with the augmented dataset outperformed the model trained with the original dataset in terms of recognition accuracy, convergence speed, and PFC. This indicates that the diversity and relevance of the dataset are crucial to the model's performance. Data augmentation enhances the effectiveness of training data by introducing diversified sample transformations, allowing the model to better learn feature diversity and robustness. This not only improves the generalization ability of the model but also achieves better performance within a shorter training period [38]. Therefore, this study clearly demonstrates the effectiveness of data augmentation techniques.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Convergence in the Solution
Further expansions to separate the \(\mathcal{O}\) term yield  
\[\Delta t_{n+1}=\underbrace{\left(\frac{\tau}{\|\mathbf{\phi}_{-}(t_{n},\mathbf{u}(t_{ n}))\|}\right)^{1/(\hat{p}+1)}}_{=\Delta t_{n+1}^{\text{ref}}}+\mathcal{O}(\tau^{1/( \hat{p}+1)}\Delta t_{n}).\]  
For the \(\mathcal{O}\) term we have \(\tau=\mathcal{O}(\Delta T_{\text{ref}})\) by definition and \(\Delta t_{n}=\mathcal{O}(\Delta T_{\text{ref}})\) from the induction hypothesis Equation (13). This completes the induction step, and the adaptive timesteps fulfill Equation (11) for \(\Delta T=\Delta T^{\text{ref}}\), which yields \(\mathbf{e}_{n}=\mathcal{O}(\tau^{p/(\hat{p}+1)})\). Thus, we established convergence for the adaptive method. Further, we built a structure with which one can prove similar results for different controllers, for example, PID controllers [19]. To prove convergence, it is sufficient to show \(\Delta t_{n}^{\text{new\ cont}}=\Delta t_{n}^{\text{deadbeat}}+\mathcal{O}(( \Delta t_{n}^{\text{deadbeat}})^{2})\). **Remark 1**. : _Assumption (12) prevents vanishing error estimates in Equation (10), which would result in extremely large timesteps. In practice, one often enforces a maximal timestep \(\Delta T^{\prime}=\text{const}\). manually. To preserve \(\mathbf{e}_{n}=\mathcal{O}(\tau^{p/(\hat{p}+1)})\) one needs to scale \(\Delta T^{\prime}\) appropriately with \(\tau\)._  
**4. Goal Oriented Adaptivity Using Local Error Estimates**  
Consider Problem (1) and (2). We approximate \(\mathcal{J}\approx\mathcal{J}_{h}\) using quadrature and \(\mathbf{u}\) by \(\mathbf{u}_{h}\) such that  
\[\mathcal{J}_{h}(\mathbf{u}_{h}):=\sum_{n=0}^{N-1}\Delta t_{n}\sum_{k=0}^{s}\sigma_ {kj}(t_{n}^{(k)},\mathbf{u}_{n}^{(k)})\approx\int_{t_{0}}^{t_{e}}j(t,\mathbf{u}(t)) \mathrm{d}t=\mathcal{J}(\mathbf{u}). \tag{15}\]  
Here \(t_{n}^{(k)}\), \(\sigma_{k}\) are the quadrature nodes and weights, and \(\mathbf{u}_{n}^{(k)}\approx\mathbf{u}(t_{n}^{(k)})\). We first show convergence in the QoI based on convergence in the solution with the following theorem. **Theorem 2**. : _Consider Problem (1) and (2) with j sufficiently smooth, a grid \(t_{0}<\ldots<t_{e}\) with timesteps \(\Delta t_{n}=\mathcal{O}(\tau^{1/q})\), and an approximation \(\mathcal{J}_{h}\approx\mathcal{J}\), see Equation (15) by a quadrature scheme of order \(r\). For an approximation \(\mathbf{u}_{h}\approx\mathbf{u}\) with_  
\[\mathbf{u}_{h}(t)-\mathbf{u}(t)=\mathcal{O}(\tau^{p/q}),\quad\forall t\in[t_{0},t_{e}], \tag{16}\]  
_the error in the QoI fulfills \(e^{\mathcal{J}}:=|\mathcal{J}(\mathbf{u})-\mathcal{J}_{h}(\mathbf{u}_{h})|=\mathcal{O }(\tau^{\min(r,p)/q})\)._  
Proof. : By splitting the error, we obtain  
\[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]0  
and address the two errors separately.
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 5 Result Analysis  
In this section, we chose six sites from the NorNet Core testbed and design three scenarios to test the performance of the proposed algorithm. The effective throughput of the network was measured within 30 s. Six CC algorithms (Cubic, OLIA, Hybla, Reno, Scalable, and Vegas) and two path management algorithms (FullMesh and PCDC) were considered for comparison.  
The scenarios in this section were chosen to present three different kinds of scenarios:  
* An inter-continental setup (between Norway and China) in Section 5.1;
* A regional setup (between two cities in Norway) in Section 5.2;
* A trans-continental setup (between two cities in different countries of Europe) in Section 5.3.  
For the sites, we used NorNet Core sites with different access technologies (research network, business-grade fiber, consumer-grade fiber and ADSL) to have heterogeneous network connectivity.
The possibility of detecting differences in molecular configuration and polarity of hydrocarbons and other polar species, such as water or isopropanol, is strictly related to the fact that nonpolar molecules are hardly visible in the THz spectral range in contrast to large-dipole-moment molecules [9; 10; 11]. Therefore, based on the pulse shape and its spectrum, the composition of a sample can be determined, and the parameters, such as the refractive index or absorption coefficient as a function of THz radiation frequency, can be calculated. THz-TDS has also been used as a method of detecting water or other polar compounds in petrochemicals. The published research focuses only on crude oils and diesel fuels [12; 13; 14]. Generally, the obtained sensitivities oscillate around 1% [11; 12]; however, the authors of [13] reported on extremely low detection limits of 0.01%wt. for their measurements. This result is rather difficult to verify because no absorption data for dehydrated samples were given in the paper as a reference. An interesting correlation between THz pulse delay and the amount of water contained in crude petroleum was reported in [14], but in this case, the reported water content in samples was rather high, i.e., ranging from 1.8% to 90.6%. In this article, we study the limits of sensitivity of THz-TDS detection of contamination of gasoline. We study mixtures of gasoline with water and isopropanol and show that the lowest detection limits for isopropanol and water content that can be achieved by using one of the best commercial THz-TDS systems are 125 ppm and 250 ppm, respectively. These values approach those of much more complicated and time-consuming spectroscopic methods (like spectroscopy of the evaporated gasoline gas phase). The measured pulse delays are compared with theoretical calculations using the modified Gladstone-Dale mixing rule for the solutions. The comparison demonstrates good agreement for gasoline-alcohol mixtures and large discrepancies for gasoline-water mixtures, suggesting that gasoline-water mixtures cannot be considered as idealized binary mixtures. Our work clearly shows that the pulse time delay measurement by THz-TDS is a sensitive and fast method of gasoline contamination detection and, as such, can be easily integrated with industrial real-time quality control applications.