esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis Results
### Performance Comparisons for RESTful and SOAP Based Web Services Implementation  
In this sub-section, we evaluate the performance of system operations executed based on RESTful web services in comparison to the execution based on SOAP web services.  
In Figure 16, we present the comparison results for geo context-based sensor binding process between RESTful and SOAP implementations. It shows the time taken in order to bind a sensor object onto a geographical location in the specific position of the given map. We can observe a clear difference and decrease in the execution time taken by the RESTful based services as compared to SOAP, which proves the RESTful based implementation to be better in the given scenario.  
In Figure 17, we present the map navigation query's response time comparisons based on pixel movement between the REST and SOAP implementations of the proposed framework. Each time the client navigates the map view, new blocks of maps are loaded depending on the move. It shows the comparison of the experiment results for loading the map move data from the geo sub-module and sensor data onto the moved location from the sensor sub-module. The data from the geo sub-module and the sensor sub-module are loaded via the geo-sensor platform and deployed by the geo-sensor framework. Every time, both the protocols, SOAP and REST, are tested with the same interval of move operation as a move of 100 pixels, 150 pixels, 200 pixels, 250 pixels, 300 pixels, 350 pixels, and 400 pixels. The results in response to each move operation for both the web services implementations are recorded in terms of the response time by the geo-sensor platform. The results clearly show that REST based web services outperform the SOAP based web services.  
Figure 16: Execution time comparisons for geo-context based sensor binding.  
Figure 17: Map navigation query based on pixels move requestIn this sub-section, we can conclude that REST is better in performance in comparison to SOAP for our application. The REST web services perform better through caching the information, and REST also allows the use of JSON, which provides faster data parsing. With JSON, REST offers better service to the browser uses. Also, our proposed framework is web-based and uses JSON. Table 2 provides a comparison between REST and SOAP web services.
## 5 Implementation and Evaluation
### Discussion  
In this sub-section, we provide a detailed discussion of our contribution, emphasizing the integration of ontology-driven standardization with federated learning for IoT-edge systems.  
**Integration of Ontology-Driven Standardization with Federated Learning for IoT-Edge Systems**  
* While ontology-based data standardization and federated learning have been explored separately in IoT systems, our work uniquely integrates both to achieve semantic interoperability and decentralized model training.
* Unlike existing ontology frameworks that rely on centralized knowledge repositories, our approach enables dynamic ontology alignment at the edge, ensuring that heterogeneous IoT devices can seamlessly communicate and share knowledge while preserving data privacy.  
**Addressing Non-IID Challenges in Decentralized IoT-Edge Environments**  
* We employ FedProx, which mitigates data heterogeneity issues in federated learning, ensuring stable model convergence in IoT-edge environments.
* Our framework outperforms FedAvg in terms of accuracy, convergence speed, and communication efficiency, as demonstrated in our experiments.
* Unlike conventional FL approaches that assume homogeneous datasets, our model adapts to imbalanced and diverse data distributions commonly found in real-world IoT deployments.  
**Scalable and Adaptive Ontology Evolution Mechanism**  
* Traditional ontology-based IoT frameworks often lack adaptability, requiring manual intervention to incorporate new devices and data schemas.
* Our framework introduces a dynamic ontology update mechanism, allowing real-time schema evolution without disrupting ongoing federated learning processes.
* This ensures that new IoT devices, sensors, and data formats can be seamlessly integrated, making the framework scalable for long-term IoT deployments.  
**Efficient Communication Strategies for Federated Learning in IoT**  
* We introduce gradient compression and sparse updates to optimize communication efficiency, reducing bandwidth consumption by 30% compared to baseline FL methods.
* This makes our approach particularly suitable for low-bandwidth IoT environments, where communication overhead is a major bottleneck.  
**Comprehensive Experimental Validation on Real IoT Data**  
* Unlike many existing works that focus on simulated datasets, we evaluate our framework using real-world IoT data (CityPulse dataset), demonstrating its effectiveness in practical applications such as traffic monitoring, environmental sensing, and smart parking.
* Our results highlight significant improvements in model accuracy (+5%), communication efficiency (\(-\)30%), and ontology alignment success (95%) compared to existing approaches.
## 5 Numerical Simulations
### Computational Complexity Analysis  
In order to solve the original NP-hard problem in (6), we further unified it under the Gaussian interference game framework in (9). We realize the potential of applying the DRL techniques on various resource-limited networks as proof of the optimization techniques. Then, a DRL-based solution with experience replay is developed to reduce computational complexity. The performance of the DQL-based power allocation model is evaluated for several training scenarios, including increasing cell user demands, varying transmit power budgets. We have run the learning process an average of 9 to 10 randomly initialized experiments. The computational complexity of the learning process is inherently addressed using the two separate DQN functions (i.e., the train DQN and learning target DQN with parameters set \(\theta\)). For comparison purposes, we use the sum rate performance metrics for the optimization solution achieved through different power allocation methods. As can be observed from the numerical simulations, it is evident that the proposed DQL approach performs better than other power allocation methods.  
As expected, the complexity of the DRL algorithm mainly relies on the wireless network sizes. According to Figure 5, it is evident that with the increase in the number of destination receivers, the average sum throughput performance of the proposed DRL-based approach decreases gradually. This is because, with the increase of cell users, the wireless network size increases while the state-action space also increases accordingly. As a result, the learning algorithm can figure out more exploration to estimate the optimal action-value functions. This is why the sum-rate performance of the DQN model decreases gradually with the increase of cell users.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 3 Proposed Security Scheme
### Initial Attach with GUTI  
#### 3.3.1. MME Unchanged  
The first case is shown as Figure 2. The \"MME Unchanged\" case is the one where the MME connected with UE at initial access is not changed, and UE accesses through identical MME at the time of re-access. At the time of re-access, the initial attach process executes verification using GUTI in order to protect ISMI. The process that transmits GUTI is identical to the IMSI transmission process in Section 3.2, and the initial attach process is executed using existing information which is saved in the MME according to information such as GUTI, NAS-MAC, and NAS sequence number. Because the series of information about UE is saved in MME, processes of authentication, the NAS Security Setup, and the location update are not executed, and only the EPS session establishment process is executed.
# Quality of Service Based NOMA Group D2D Communications
## 5 Conclusions  
In this paper, we have proposed Q-NOMA group D2D communications. In order to study the performance of the proposed network, we first derive the Laplace transform of the interference expression, based on which we further derive the closed-form expression for outage probability to analyse the performance of the DRs in the proposed Q-NOMA group D2D communications. The results show that the proposed Q-NOMA group D2D achieves overall lower outage probability than its counterpart paired D2D communication. Furthermore, based on two power allocation coefficient  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Users, \(M\)** & **Q-NOMA Policy I** & **Q-NOMA Policy II** & **Paired D2D** \\ \hline
5 & 0.003 & 0.07 & 0.08 \\
7 & 0.007 & 0.06 & 0.12 \\
9 & 0.01 & 0.1 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average outage comparison between paired device-to-device (D2D) and quality of service based non-orthogonal multiple access (Q-NOMA) group D2D communications (\(M>3\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Targeted Rates, \(\{R_{m}\}_{m=1}^{3}\)** & **Power Allocation Coefficients, \(\{\beta_{m}\}_{m=1}^{3}\)** & **Policy I** & **Policy II** \\ \hline
1. & \(\{1,2,5,3\}\) & \(\{0.58,0.24,0.18\}\) & 0.04 & 1 \\
2. & \(\{1,1.5,2\}\) & \(\{0.47,0.3,0.23\}\) & 0.002 & 0.04 \\
3. & \(\{0.5,1.5,2\}\) & \(\{0.63,0.21,0.16\}\) & 1 & 1 \\
4. & \(\{0.9,1,2\}\) & \(\{0.42,0.38,0.2\}\) & 1 & 0.001 \\
5. & \(\{0.2,2,2.2\}\) & \(\{0.85,0.08,0.07\}\) & 0.006 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average outage comparison between two implementations of quality of service based non-orthogonal multiple access (Q-NOMA) group device-to-device (D2D) communications.  
policies, we have presented the comparison between two possible implementations of the proposed Q-NOMA group D2D communications. Due to the similar channel conditions and diverse QoS requirements of DRs, the results show that the proposed Q-NOMA implementation based on Policy I is more realistic and suitable than one based on Policy II for group D2D communications. As future work, we plan to investigate and analyse the performance of proposed Q-NOMA group D2D communications in underlay cellular mode, where additional interferences from base stations and cellular users will be considered. Another interesting future direction is to extend the current network model to a framework of MIMO systems. This would require a robust precoder and MIMO-NOMA design to improve the system performance and capacity while mitigating the inter and intra beam interference efficiently.  
This work has been supported by a PhD Fee and Stipend Scholarship for [PERSON] from the School of Engineering, Computer and Mathematical Sciences at Auckland University of Technology.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] derived the Lemmas and Theorems, conducted the simulations and drafted the paper. [PERSON] and [PERSON] critically reviewed the results, proofread and revised the paper.  
The authors declare no conflict of interest.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 4 Proposed Algorithm and Its Complexity
### Conjugate Gradient Analysis  
The conjugate gradient of \(\mathbf{C}\) will be derived below, and for the convenience of derivation, let \(f(\mathbf{C})=\sum\limits_{q=0}^{MQ-1}|s(q)|^{2p}\), \(g(\mathbf{C})=\sum\limits_{k\in\Omega}|R(k)|^{2p}\), and \(L(\mathbf{C})=P_{X}+\frac{1}{M}\sum\limits_{m=0}^{M-1}\sum\limits_{n\in \mathcal{R}}|C_{n,m}|^{2}\). Then, the objective function \(\widetilde{O}(\mathbf{C})\) is rewritten as  
\[\min_{\mathbf{C}\in\mathbb{C}^{MN}}\widetilde{O}(\mathbf{C})\ =\ \frac{1}{p}\big{[}\alpha\log_{10}f( \mathbf{C})+\log_{10}g(\mathbf{C})\big{]}-(\alpha+2)\log_{10}L(\mathbf{C}) \tag{22}\]  
For ease of presentation, the conjugate gradient \(\
abla_{\mathbf{C}^{*}}\widetilde{O}(\mathbf{C})\) is first computed monosymbolically and then converted to a vector, i.e., we first derive the gradient of \(\widetilde{O}(\mathbf{C})\) with respect to \(C_{u,r}^{*}\) (\(u\in\mathcal{R},r=0,1,\ldots,M-1\)), which is the conjugation of \(C_{u,r}\). Using matrix calculus and the chain rule, the gradient \(\partial\widetilde{O}(\mathbf{C})/\partial C_{u,r}^{*}\), is given by  
\[\frac{\partial\widetilde{O}(\mathbf{C})}{\partial C_{u,r}^{*}}=\frac{1}{\ln 10 }\bigg{[}\frac{1}{p}\bigg{(}\frac{\alpha}{f(\mathbf{C})}\frac{\partial f( \mathbf{C})}{\partial C_{u,r}^{*}}+\frac{1}{g(\mathbf{C})}\frac{\partial g( \mathbf{C})}{\partial C_{u,r}^{*}}\bigg{)}-\frac{\alpha+2}{L(\mathbf{C})} \frac{\partial L(\mathbf{C})}{\partial C_{u,r}^{*}}\bigg{]} \tag{23}\]  
In the following, \(\partial f(\mathbf{C})/\partial C_{u,r}^{*}\), \(\partial g(\mathbf{C})/\partial C_{u,r}^{*}\) and \(\partial L(\mathbf{C})/\partial C_{u,r}^{*}\) will be further specifically derived. Based on the expression of \(f(\mathbf{C})\), we have  
\[\frac{\partial f(\mathbf{C})}{\partial C_{u,r}^{*}}=\sum\limits_{q=0}^{MQ-1} \frac{\partial|s(q)|^{2p}}{\partial C_{u,r}^{*}}=p\sum\limits_{q=0}^{MQ-1}|s(q )|^{2(p-1)}\frac{\partial|s(q)|^{2}}{\partial C_{u,r}^{*}} \tag{24}\]  
According to (2), we know that  
\[\frac{\partial|s(q)|^{2}}{\partial C_{u,r}^{*}}=\frac{\partial s(q)s^{*}(q)}{ \partial C_{u,r}^{*}}=s(q)e^{-2\pi u\frac{q}{Q}},\qquad q\in[(r-1)Q,rQ-1] \tag{25}\]  
Further, the gradient is expressed in vector form. Let \(\widetilde{\mathbf{C}}=\left[\widetilde{\mathbf{C}}_{0}^{T},\widetilde{\mathbf{C} }_{1}^{T},\ldots,\widetilde{\mathbf{C}}_{M-1}^{T}\right]^{T}\) and \(\widehat{\mathbf{C}}=\left[\widehat{\mathbf{C}}_{0}^{T},\widehat{\mathbf{C} }_{1}^{T},\ldots,\widehat{\mathbf{C}}_{M-1}^{T}\right]^{T}\). Then, according to (33), the gradient vector denoted by \(\mathbf{G}\) can be written as  
\[\mathbf{G}=\
abla_{\mathbf{C}^{*}}\widetilde{\mathcal{O}}(\mathbf{C})=\frac{1 }{\ln 10}\left(\alpha\frac{\widetilde{\mathbf{C}}}{f(\mathbf{C})}+\frac{ \widehat{\mathbf{C}}}{g(\mathbf{C})}-(\alpha+2)\frac{\mathbf{C}}{M\cdot L( \mathbf{C})}\right) \tag{34}\]
## 5 Proof of Concept
### Flow Rules to Optimize Response Times
#### 5.1.1 Monitor Module  
A separate thread from the Ryu controller sends specially crafted Internet Control Message Protocol (ICMP) echo packets at fixed intervals (\(dt\) seconds) through each of the feeder links using \(OFPPacketOut\) messages. It then receives the ICMP replies through \(OFPPacketIn\) messages, computes the delay, and stores them in a global data structure. The ICMP packet has a payload of size 44 bytes that contain the following information:  
* switch ID,
* target IP address of a given server associated with the feeder link,
* creation time stamp.  
The delay is computed as the time difference between the time at which an ICMP response is received from a server and the time stamp in the payload of the response. The method used for delay computation is described in Algorithm 1. The number of echo requests sent to and received from each server is updated in the respective global data structures. Likewise, another thread handles the estimation of the packet losses based on the number of echo requests sent and received at a certain rate (every \(mt\) seconds) and computes the packet-loss ratio using Algorithm 2.
## 4 Experimental Section and Results Analysis
### Concept Drift Detection  
The EI-kMeans method is chosen to perform concept drift detection for OASVR. The detection window size refers to the number of detection data points used for concept drift detection. In industrial scenarios, we aim to minimize the lag in concept drift detection. And, the Pearson's chi-square test requires a minimum data count of 5 for detection. So, the window size is set to 10 and the number of clusters is set to 2.  
When the number of new streaming data points reaches the detection window size, the Pearson's chi-squared test is applied to the new data to detect concept drift. The test calculates a \(p\)-value and, if the p-value is greater than 0.05, it indicates that concept drift has occurred. For the detected drift data, they will be discarded and not used in the model training. The results with the inclusion of concept drift detection are shown in Table 2 below:  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **K1** & **K2** & **K3** & **K4** \\ \hline OASVR & 0.957 & 0.924 & 0.898 & 0.922 \\ OASVR-Elk & 0.973 & 0.944 & 0.932 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Before and after OASVR incorporates concept drift detection results.  
Figure 6: Dynamic window size and corresponding training time for each streaming data during the OASVR training process on four different datasets: K1, K2, K3, K4.  
The predictive results after incorporating concept drift detection are shown in Figure 7. From Table 2, we can know that the average \(R^{2}\) of OASVR-Elk is 1.85% higher than OASVR on four datasets. Figure 7 shows the predictive result of OASVR-Elk. While OASVR demonstrates some capability in handling noise, it still struggles with abrupt concept drift. OASVR-Elk does not online train the data detected by EI-kMeans as experiencing concept drift. This prevents OASVR from updating weights for erroneous data, allowing the model to fit a more reasonable data distribution and thus improving the overall performance.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 5 Experiments  
To assess the performance of the proposed approach, two replay datasets were considered: the ASVspoof 2017 version 2.0 benchmark dataset [35] and the ASVspoof 2019 benchmark dataset [36]. The audio files included in these datasets are characterized by a 16 kHz sampling rate and 16-bit resolution. As reported in Table 3, ASVspoof 2017 v2.0 was split into three subsets. The first subset was a training set that contained 3016 files, of which 1507 were genuine, and 1507 were replay spoofing files. The second subset was a development set containing 1710 files, of which 760 were genuine, and 950 were replay spoofing files. The third subset was an evaluation set containing 13,306 files, of which 1298 were genuine, and 12,008 were replay spoofing files.  
Figure 4: Illustrative example of the proposed spoofing countermeasure based on local classification subproblems.  
Figure 3: Architecture of the proposed spoofing countermeasure based on local classification subproblems.  
As shown in Table 4, the ASVspoof 2019 replay spoofing dataset comprised a training set with 48,600 spoofed utterances and 5400 genuine utterances, a development set with 24,300 spoofed utterances and 5400 genuine utterances, and an evaluation set containing various randomly chosen acoustic and playback configurations [36].  
From the audio files, three audio features were extracted: mel-frequency Cepstral coefficients (MFCCs) [43], the constant Q Cepstral coefficients (CQCCs) [4], and the linear frequency Cepstral coefficient (LFCC) [5]. The equal error rate (EER) [76] is considered as the performance measure. EER represents the operating point at which the false acceptance rate (FAR) and false rejection rate (FRR) are equal [76].
## 3 Results and Discussion
### Volumetric Soil Water Content (VWC) during Soil Column Experiment
Only after it broke was 200 mL of liquid collected; this amount is equal to the volume of the injected liquid. The results of foam characterization are summarized in Supplementary Text S1. Our generated foam was very light, expandable, dry, and hexagonal in shape (see Table S2 and Figure S5). It could not flow directly through the hole without pressure. After the foam broke and changed into a liquid, the liquid reagent was easily transported through the holes in the soil columns. This breaking led to a slow release of remediation reagents into the soil, preventing a fast flow along preferential flow paths. As shown in the volume of leachate collected from the bottom of soil column (see Figure S3), the release of remediation reagent using a foam spray (either with or without holes) produced lower leachate volumes than if a water pulse was used, even with holes. The lower leachate volume indicated that the remediation reagents released were more present in the contaminated soil.
## 4 Experiment
### Experimental Results
Figure 16: The end-to-end delay of sampling data packets that require cross-span transmission. Figure 15: The end-to-end delay of sampling data packets transmitted without cross-span. balancing, thus encountering local optima. In comparison to the ELB algorithm, the PSFRS algorithm considers both load balancing and the necessary upper bound on delays, better meeting the transmission requirements of time-sensitive services. Figure 16 shows that at lower hop counts and shorter linear distances, there is not a significant difference in end-to-end delay between the algorithm proposed in this paper and the SPF algorithm. However, as the number of hops and distances increase, the optical interconnect parallel subnet architecture and the reinforcement learning scheduling strategy utilized in this paper optimize both intra-subnet and inter-subnet transmissions, resulting in significantly better performance compared to the SPF algorithm. The reason behind this improvement is that the SPF algorithm may encounter congestion and jitter during long-distance inter-subnet transmissions, whereas the performance of long-distance branch line transmissions based on laser alignment is far superior. Similarly, compared to the ELB algorithm, the proposed PSFRS algorithm in this paper demonstrates superior performance in scenarios involving time-sensitive business transmissions across Low Earth Orbit satellite constellations. This is because during transmissions across distances, it is necessary to consider both intra-constellation transmission delays and inter-constellation transmission delays. The ELB algorithm focuses solely on balancing node loads to facilitate business transmissions. Consequently, in the context of LEO giant constellations, as the distance between source and destination nodes increases, the transmission delay of the ELB algorithm exhibits a sharp increase, failing to meet the transmission requirements of time-sensitive services. Therefore, the parallel subnet architecture and intra-subnet scheduling transmission strategy employed in this paper exhibit excellent performance in large-scale satellite networks and provide valuable insights for further research and implementation.
## 6 Experiment
### Experimental Results
#### 6.3.3 The Equilibrium Problem of Privacy Protection and System Overhead  
Next, we discuss the equilibrium problem of privacy protection and system overhead for DGS-HSA. From the attacker's point of view, when the attacker receives a query request \(Req\), \(P_{A}=\frac{1}{s}\) and \(P_{S}=P_{A}/\frac{k}{s}=\frac{1}{k}\). Given \(k\), then \(P_{S}\) is also determined. Hence, we mainly discuss, when \(k\in[2,42]\), with the change of \(k\), how the value of \(s\) can balance the privacy protection and system overhead. When we calculate system overhead, for simplicity, each second-level grid contains about 3000 historical locations.  
After normalization, \(P_{A}\) and \(C(G_{sub})\) can be represented in the same coordinate system. As shown in Figure 9, when \(s=7\), the two curves have an intersection, i.e., the proposed scheme achieves the balance between privacy protection and system overhead at the intersection. At this moment, the storage overhead and communication overhead generated by each submission of the query are about 1.6 Mb. Moreover, as \(k\) changes, the value of \(s\) changes as shown in Figure 10, which is the equilibrium point of privacy protection and system overhead. We can see from Figure 10 that when \(k\geq 7\), the equilibrium is reached at \(s=7\); when \(k<7\), the local optimum is reached at \(s=k\). Furthermore, the above results show that the presented scheme is feasible.  
Figure 8: The probability \(P_{S}\) that the user’s real query content is recognized varies with \(k\).  
Figure 9: The balance of privacy protection level and system overhead.