esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Recommendation Based on User Clustering  
When there are many users, it takes a lot of time to calculate similarity between each one according to the aforementioned method. It is hard to give real-time recommendation. Thus, we further investigate the effect of clustering all users by the K-means algorithm to reduce the number of candidates for similar user recommendation.  
Although K-means clustering is incremental, to save time when updating clusters, we propose to divide it into two stages: offline clustering and online recommendation. First, users are represented by the output category after learning user preferences in Section 3.3 and then clustered using the K-means algorithm. Since the dimension of the category vector is lower than that of feature maps, it reduces the computational time. Then, we calculate cosine similarity by using the feature map of each member in the same cluster to find out the most similar members to recommend. Because the number of users in each group is much smaller than all users, it also reduces the computational time for online recommendation if user clustering can be done offline.  
If the original user posts a new post, we only need to calculate the similarity for the members of the same group. If we have a new member, we just need to calculate which cluster it belongs to for recommendation and then calculate the similarity between cluster members, instead of all users. In addition, since clustering can be done offline, the method greatly improves the efficiency of online recommendation. The architecture is shown in Figure 6.  
Figure 5: The user preference learning architecture with dimension reduction.  
The problem for conventional K-means algorithm is to determine the best number of clusters. Since Cascade K-means [50; 51] can automatically determine the optimal number of clusters, it was selected to solve this problem. We use the classification result of the user as inputs for user clustering with Cascade K-means. Lastly, after calculating cosine similarity among the members in the same cluster, we can obtain the most similar K members to recommend.  
Although we did not deploy it as a potential real-world recommendation system, we could implement different types of thresholds before initiating the update process by clustering. For example, we could count the number of new users, and set the threshold for a new user ratio. Furthermore, when the percentage of new posts by a user reaches a threshold, we could initiate an update process to update the cluster members for more accurate recommendation.
## 4 Results and Discussions
### The Impact of Dropout Probability and Batch Normalization  
Dropout is a commonly used technique in deep learning to regularize and prevent the co-adaptation of neurons [37]. It can randomly zero some neurons in the layer with probability \(p\), which is a hyperparameter. We performed a sensitivity analysis of \(p\) to investigate the performance impact caused by different values of \(p\). We changed the value of \(p\) from 0 to 0.9, and each setting repeated the experiment ten times to avoid the randomness effect of the weight initialization. We used the average of ten experiments as the performance result. Figure 8 shows the experimental results. The model gave the best  
Figure 7: Single fusion network vs. ensemble of fusion network, where a lower value is better.  
performance when \(p\) was 0.1 but the performance difference between the different settings was minor.  
In addition to the dropout mechanism, batch normalization is another technique that can speed up model training, reduce the internal covariant shift, and regularize the model. We conducted experiments to explore the importance of batch normalization in our proposed model. Based on the previous experimental results, we set the dropout probability to 0.1 and designed a new model by removing all batch normalizations from the network. The experimental results measured by the MSE and MAE are listed in Figure 9, which indicated that batch normalization was an essential step in our proposed model. In contrast, the importance of the dropout was much less than that of batch normalization. These results also conform to the conclusions of the previous work [34], that is, the dropout can be removed or reduced in strength in a batch-normalized network.  
Figure 8: Results with different dropout probabilities.  
Figure 9: The impact of batch normalization, where a lower value is better.
# Highly Sensitive and Selective H\({}_{2}\)S Chemical Sensor Based on ZnO Nanomaterial
## 3 Results and Discussions
### Gas Sensing Properties
\begin{table}
\begin{tabular}{c c c c c} \hline
**Composition and** & **Operating** & **Gas Concentration** & & \\
**Morphology** & **Temperature (\({}^{\circ}\)C)** & **(ppm)** & & \\ \hline ZnO nanorods & 500 & 50 & \(\Delta\)G/G\({}_{0}\), 35 & [69] \\ ZnO dendrites & 30 & 100 & R\({}_{0}\)/R, 17.3 & [70] \\ ZnO nanoparticles & 300 & 20 & \(\Delta\)R/R\({}_{0}\), 0.8 & [71] \\ ZnO nanorods & 50 & 100 & G/G\({}_{0}\), 61.7 & [72] \\ ZnO nanowires & 150 & 20 & R/R\({}_{0}\), \(\sim\)0.9 & [68] \\ ZnO comb-like & RT & 4 & \(\Delta\)R/R\({}_{0}\), 0.8 & [73] \\ Al–ZnO & 200 & 150 & R\({}_{0}\)/R, 2.05 & [45] \\ Cu–ZnO nanograins & 250 & 10 & \(\Delta\)R/R\({}_{0}\), \(\sim\)0.9 & [74] \\ Au–ZnO nanorods & RT & 6 & G\({}_{\rm g}\)/G\({}_{0}\), 1270 & [34] \\ Carbon–ZnO nanofibers & 250 & 30 & R/R\({}_{0}\), 77.75 & [75] \\ Pd–SnO\({}_{2}\)–ZnO & RT & 20 & R\({}_{0}\)/R, 0.064 & [44] \\ ZnO & 400 & 20 & \(\Delta\)G/G\({}_{0}\), 6300 & This work \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of H\({}_{2}\)S gas sensing properties of the prepared ZnO nanomaterial with the previously reported H\({}_{2}\)S gas sensors based on ZnO. G\({}_{0}\) is the sample conductance in air and G is the sample conductance in the presence of H\({}_{2}\)S. \(R_{0}\) is the sample resistance in air, \(R\) is the sample resistance in the presence of H\({}_{2}\)S.  
Figure 4: Response of the obtained ZnO nanomaterial towards 20 ppm of H\({}_{2}\)S, NH\({}_{3}\), dimethylamine (DMA) and C\({}_{3}\)H\({}_{6}\)O at 400 \({}^{\circ}\)C. The response towards H\({}_{2}\)S was very high compared to the other gases. To visualize the response values towards all the gases the response of the structure towards H\({}_{2}\)S is shown in blue (left axis), and the response towards NH\({}_{3}\), DMA and C\({}_{3}\)H\({}_{6}\)O is shown in green (right axis).
## 4 System Architecture
### Remote Terminal Units  
RTUs are the backbone of the SCADA system, responsible for collecting data from sensors and controlling local equipment at key locations such as wells (see Figure 3), pumping stations, and storage tanks. These units are equipped with sensors that measure critical parameters, including water flow rates, pressure levels, and pump statuses, providing real-time data that is transmitted to the Central Server for further processing and analysis [3]. RTUs (see Figure 4) play a dual role in the system: they not only gather data but also execute local control commands based on predefined criteria or instructions from the central server. For example, an RTU can automatically start or stop a pump if water levels or pressure fall outside specified thresholds. This local control capability ensures that the system can respond quickly to changes in operational conditions, even in the absence of direct human intervention [7].  
Figure 4: Remote Terminal Units (RTUs).  
Figure 3: Physical Lerma Basin well.  
Figure 2: System Overall Architecture.  
To ensure reliability and minimize the risk of system failures, redundant RTUs are installed at critical locations. These backup units take over in the event of a primary RTU malfunction, ensuring continuous operation and data collection. This redundancy is particularly important in the Lerma Basin, where the water supply network spans a large and geographically diverse area, making manual intervention difficult in remote locations [19].  
The integration of RTUs with the broader SCADA system is facilitated by a hybrid communication network, which combines RF (Radio Frequency), satellite, and cellular/Internet technologies. This network ensures that data from RTUs is transmitted reliably to the central server, regardless of the location of the wells or pumping stations. The use of multiple communication methods allows the system to adapt to varying environmental conditions and infrastructure availability, ensuring robust and uninterrupted operation [8]. In addition to their operational functions, RTUs contribute to the system's cybersecurity framework. They are equipped with encryption and access control mechanisms to protect against unauthorized access and data breaches. These measures are critical for safeguarding the integrity and confidentiality of the data collected by RTUs, particularly in a system as critical as the Lerma Basin's water supply network [19].
## 3 Underwater Routing Protocols
### QoSDFR  
The quality of service-aware directional flooding-based routing (QoSDFR) improves the DFR protocol in that the sink transmits feedback towards the nodes about the link quality [26]. This is contrary to the DFR where every node computes the link state condition. On the basis of the feedback from the sink, every sender node adjusts the number of relay nodes either along the full or partial routing paths. The feedback from the sink reduces energy consumption as the nodes do not have to check the link condition repeatedly. However, it brings a high end-to-end delay as nodes have to wait for the feedback from the sink.
## 1 Introduction
Given the large scale, heterogeneity, and complexity of the converged communication-compute-storage systems in future networks, management solutions must be highly automated and intelligent. The integration of dynamic intelligent network telemetry and closed-loop control mechanisms leveraging machine learning (ML) and data analytics techniques offers promising new management technologies. A wide spectrum of technologies, often developed based on various ML methods, has been proposed to enable intelligent and autonomous management for various aspects of future networks [11]. However, the heterogeneous nature of ML techniques and the unique characteristics of future networking technologies impose a variety of requirements for integration between these areas. The current disparate management mechanisms for ML functionalities and network functions may degrade the effectiveness of intelligent network operations. Therefore, it becomes vital that a wide range of intelligent management technologies is applied based on the foundation of a consistent architectural framework with a common holistic vision. This calls for standardization of new management architectures that support seamless integration of various ML/data analytics techniques for addressing the management challenges in a holistic and interoperable way. Currently, various relevant network SDOs, including 3 GPP, ETSI, and ITU-T, are actively working on this important subject and making encouraging progress. The goal of this paper is to provide a big picture that reflects the latest developments of architectural frameworks for intelligent and autonomous management in cloud-native future networks. The paper first gives an overview of the technical trend toward cloud-native network design and network-cloud/edge convergence in future networks. Then, the paper surveys the latest representative progress in the standardization of the management architecture for future networks, including works by 3 GPP, ETSI, and ITU-T, and analyzeshow cloud-native network design may facilitate the architecture development for addressing the management challenges in future networks. Open issues related to intelligent and autonomous management from the architectural perspective are also discussed in the paper to identify some directions for future research and development. The remainder of the paper is organized as follows. Section 2 introduces the incentive for cloud-native design and discusses its impacts on future networking. Then, the state-of-the-art developments of the management architecture, including 3 GPP Network and Management Data Analytics Functions/Services (NWDAF/MDAS), the ETSI Experiential Networked Intelligence (ENI) architecture, the ITU-T Architecture for ML in Future Networks, and the ETSI Zero Touch Management (ZSM) Framework, are reviewed and analyzed in Sections 3-6, respectively. Open issues and possible directions for future research are discussed in Section 7. Section 8 draws the conclusions.
## 2 Methodology
### Likert-Dichotomous Conversion  
It was necessary to put the results of both surveys on the same scale in order to compare the results. The Likert scale had five different values, while the binary scale had only two. It is easier to collapse this scale than to create five values from the dichotomous one, although the latter is not impossible. For the transformation, it was necessary to select a Likert score from which a positive response was considered. For example, by choosing the score \"2\" as the threshold, \"2, 3, 4 and 5\" would add up as positive responses. What  
Figure 2: Number of survey responses by subject and format.  
threshold should be chosen to minimize the differences between the responses collected from the two surveys? The Likert scale involves a degree of subjective judgment influenced by cultural background. Emotions play a role in whether individuals respond positively or negatively to a question [27,28]. Different cultures perceive the midpoint differently; it represents a positive emotion for some, while it is more neutral for others. It was not known a priori the profile of the students surveyed. Choosing the wrong cutoff could introduce systematic bias into our analysis. This was possible for each threshold analyzed.  
Since our aim was to look for similarities, the transformation was performed for all the responses collected, that is, from 1 to 5. After the transformation, a ratio of the affirmative responses among the total responses was obtained for each subject and question. This transformed quantity was compared with that obtained directly from the dichotomous survey. The histograms of the differences between the two magnitudes revealed that the threshold \"3\" produced the result with the least bias (Figure 3). Therefore, threshold \"3\" was chosen to transform the Likert surveys into binary or dichotomous surveys.
## 1 Introduction  
Vanadium(V) and chromium (Cr) are key resources that have been widely used during the manufacturing of petrochemicals, catalysts, ferrous and non-ferrous alloys, and stainless steel [1; 2; 3; 4]. Due to the similar chemical and physical properties, vanadium and chromium commonly co-exist in minerals. Industrial vanadium is usually extracted from vanadium titanomagnetic ore and stone coal, during which a large amount of wastewater containing V(V) and Cr(VI) is produced. To satisfy the discharge standard, Na\({}_{2}\)SO\({}_{3}\) or iron scurf is added to reduce and precipitate Cr(III), V(IV), and V(III), with the solid named as vanadium-chromium reducing residue (VCRR). China produces over 500 thousand tons of VCRR every year, causing significant environmental pollution, therefore, proper and efficient VCRR management technologies are urgently required.  
To date, various hydrometallurgy technologies, such as direct NaOH leaching [5] and electric field alkaline leaching [6; 7] have been developed to recover vanadium from the VCRR. The \"NaOH-H\({}_{2}\)O\({}_{2}\)\" combined direct leaching and roasting alkaline leaching are commonly used for VCRR processing [8; 9; 10]. However, the recovery of chromium was rarely focused. For simultaneous vanadium and chromium recovery, a new process of using sodium carbon-aided oxidation roasting, followed by sulfuric acid leaching was proposed by [PERSON] et al. [11]. The extraction efficiency of vanadium could reach 90%, while only around 60% chromium was recovered. To further improve the chromium extraction efficiency, ion exchange [12; 13; 14] and solvent extraction [15; 16; 17] for simultaneous vanadium and chromium recovery were conducted. However, those processes significantly increased the operational complexity, making it economically infeasible.  
As the solubility of vanadium(III) is much lower than that of vanadium(V), vanadium recovery in the form of vanadium(V) precipitates is preferable. [PERSON] [18; 19; 20] introduced MnO\({}_{2}\) to oxidize vanadium(III) to vanadium(V), which was then hydrolyzed and separated from chromium(III). The overall vanadium recovery efficiency was 86.5%, which was lower than most industrial practices, therefore, more effort is required to improve the recovery efficiency. In this paper, MnO\({}_{2}\) was used to oxidize the low-valent vanadium during the acid leaching process, followed by selective adsorption for vanadium(V) and Cr(III) recovery. The effect of reaction time, reaction temperature, MnO\({}_{2}\) dosage, and liquid-to-solid ratio on vanadium and chromium leaching efficiency were investigated. The leaching kinetics, as well as the optimal conditions for vanadium and chromium recovery, were also examined.
# FIDO2 Facing Kleptographic Threats By-Design
### Security Status of Fido2  
Interestingly, Microsoft Security states on their web page that FIDO2 \"aims to strengthen the way people sign in to online services to increase overall trust. FIDO2 strengthens security and protects individuals and organizations from cybertimes by using phishing-resistant cryptographic credentials to validate user identities\". This statement does not provide categorical promises about unconditional security. On the other hand, FIDO2 is based on concepts such as digital signatures, challenge-and-response authentication, and separate signature keys generated at random to protect privacy. However, secure components composed together do not always result in a secure product.  
The security analysis of FIDO2 (for consecutive versions of the protocol--see Section 2 for details) is given in [21; 22]. Both papers cover the core components of FIDO2: the WebAuthn protocol and the Client-to-Authenticator Protocol. However, apart from considering different versions of these protocols, the papers assume different attestation modes [23]. The privacy of FIDO2 is formally defined and proven in [24]. Moreover, the paper [24] augments the protocol with the functionality of global key revocation.  
However, the FIDO2 protocol has not been evaluated in the context of kleptography. Note that kleptographic attacks discussed in the literature are typically focused on the possibility of installing an asymmetric leakage in cryptographic primitives. They do  
Figure 4: A general picture of kleptography: (**a**) the information flow in the case of an honest setup of a signing device, (**b**) the case of an infected signing device. The covert channel is protected with asymmetric key \(pk_{X}\) stored on the infected device.  
not consider more complicated scenarios, such as a contaminated device connecting to a malicious, cooperating server, establishing a shared key, reseeding the deterministic random number generator, and then erasing any traces of its malicious activity. However, such attacks seem closer to reality and should also be examined--consider, for example, spyware [25] installed on a mobile phone.
## 2 Wireless Communication for Maritime Applications
### Long-Range, No-Line-of-Sight Communication  
An interesting phenomenon that can be utilized to achieve long-distance and no-line-of-sight communication using communication protocols in the GHz frequency range, such as Wi-Fi or Bluetooth, is the evaporation duct. Under the appropriate conditions, a region of rapidly decreasing humidity appears above the ocean, which causes RF signals to be trapped in this layer and act similarly to a waveguide [22], leading to less attenuation of the signal over distance and allowing non-line-of-sight communication, as illustrated in Figure 2.  
The bibliography has some examples that demonstrate the feasibility of utilizing the evaporation duct. For example, in [22], the authors report a range of 78 km when using a frequency of 10.6 GHz, which could allow a large bandwidth to be used to transfer data  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Data Rate** & **Range** \\ \hline VHF [7] & 1.2 kbps & 120 km \\ Long-range Wi-Fi [7] & 3 Mbps & 20–50 km \\ LTE [7] & 7.6 Mbps & 10 km \\ GSM/GPRS [27] & 168 kbps & 2–35 km \\ Iridium [23] & 176–704 kbps & worldwide \\ Starlink [28] & 100+ Mbps & worldwide (expected) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reported data rate and range performance of long-range communication protocols.  
Figure 2: Representation of the evaporation duct and its use in long-distance communications.  
(10 Mbps were reported). In [33], a range of 64 km was achieved with a 10.4 GHz signal. In [34], it is numerically demonstrated that frequencies in the range of 10-12 GHz have the smallest propagation losses over a 100 km distance in the evaporation duct, using a fixed antenna height, with losses increasing for higher frequencies. According to the same study, a 5.8 GHz carrier is also better than a 2.4 GHz one; therefore, using 5.8 GHz Wi-Fi combined with the evaporation duct seems a viable option to extend the communication range, although such a system is highly dependent on atmospheric conditions.
## 2 Preliminary
### Sparse Convolution  
3D sparse convolution [20] is widely used for processing point cloud data in autonomous driving. In contrast to traditional convolution, sparse convolution selectively computes only the non-zero values, eliminating redundant computations and enhancing processing efficiency. The difference between traditional and sparse convolution is shown in Figure 2. This distinction highlights the efficiency of sparse convolution in maintaining a sparse representation. To better introduce sparse convolution, we denote the input feature vector as \(x^{in}\), the output feature vector as \(x^{out}\), and the weight matrix as \(W_{\delta}\). And \(x_{j}\in\mathbb{R}^{C}\) is a C-dimensional feature vector for the coordinates \(p_{j}\in\mathbb{Z}^{D}\) in a D-dimensional space. \(W\in\mathbb{R}^{K^{D}\times C^{in}\times C^{out}}\) represents the weight, where \(K\) is the shape of the kernel offset, \(D\) is the spatial space dimension. The weight can be divided into \(K^{D}\) matrices of size \(C^{in}\times C^{out}\), which are denoted as \(W_{\delta}\) for \(\delta\in\Delta^{D}(K)\). Then, the convolution can be represented as follows.  
\[x^{out}_{k}=\sum_{\delta\in\Delta^{D}(K)}x^{in}_{j}\cdot W_{\delta}\quad \forall\ p_{j}\in P^{in},\ q_{k}\in P^{out}, \tag{1}\]  
where \(p_{j}=s\cdot q_{k}+\delta\), and it represents all non-empty neighbor voxels around center \(p\). When \(s\) is 1, the input coordinates are the same as the output coordinates. The whole sparse convolution is shown in Figure 3, as follows: the sparse convolution will build a Hash table based on the input coordinates, and output coordinates will query the table. Upon a query hit, the corresponding inputs and weights are connected via a Gather operation, followed by matrix multiplication. The Scatter operation then aggregates the partial sums of each output value and distributes them back to their respective output positions.  
Figure 2: Sparse convolution computes only when the center overlaps with a non-zero value when the calculation is performed. However, every non-zero value needs to multiply with every weight and will dilate very quickly in conventional convolutions. (**a**) Sparse convolution, (**b**) conventional convolution.