esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
###### Abstract  
Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used efficiently to improve the lifetime of a network. In this paper, to support smart city vision, a terrestrial based \"Away Cluster Head with Adaptive Clustering Habit\" (ACH)\({}^{2}\) is examined in the specified three dimensional (3-D) region inside the water. Three different cases are considered, which are: single sink at the water surface, multiple sinks at water surface,, and sinks at both water surface and inside water. \"Underwater (ACH)\({}^{2}\)\" (U-(ACH)\({}^{2}\)) is evaluated in each case. We have used depth in our proposed U-(ACH)\({}^{2}\) to examine the performance of (ACH)\({}^{2}\) in the ocean environment. Moreover, a comparative analysis is performed with state of the art routing protocols, including: Depth-based Routing (DBR) and Energy Efficient Depth-based Routing (EEDBR) protocol. Among all of the scenarios followed by case 1 and case 3, the number of packets sent and received at sink node are maximum using DEEC-(ACH)\({}^{2}\) protocol. The packets drop ratio using TEEN-(ACH)\({}^{2}\) protocol is less when compared to other algorithms in all scenarios. Whereas, for dead nodes DEEC-(ACH)\({}^{2}\), LEACH-(ACH)\({}^{2}\), and SEP-(ACH)\({}^{2}\) protocols' performance is different for every considered scenario. The simulation results shows that the proposed protocols outperform the existing ones.  
underwater; wireless sensor networks; clustering; smart cities; routing protocols; IoT, acoustic signals; nodes +
Footnote †: journal: Sensors
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 8 Topic-Specific and Cross-Topic Performance
### Cross-Topic Performance  
It is often important for trained models to generalise to unseen domains. While conversations of different topics are not completely different domains, the results shown in Section 8.1 illustrate that the models indeed have varying levels of performance depending on the topic. Hence, to complement Section 8.1, where models trained on dialogues of all topics are examined for their topic-specific performance, we probe model generalisability by removing a topic \(T_{i}\) from the training set completely and then analysing its performance on a \(T_{i}\)-only test set.  
Concretely, we adopt a leave-one-topic-out approach by training on all the AnnoMI utterances from conversations that do not have topic \(T_{i}\) and testing on all the AnnoMI utterances from dialogues that only have topic \(T_{i}\). Conversations with multiple topics that include \(T_{i}\) are not present during training or testing. We note that the test set in this setup is effectively identical to that of Section 8.1, which allows for cross-topic and topic-specific performance to be compared fairly.  
For therapist behaviour prediction, the performance of the BERT models remains stable when moving from topic-specific to cross-topic, which shows that (1) the models are generalisable to new topics for this task, and (2) therapist language is relatively consistent in conversations of different topics.  
For client talk type prediction, on the other hand, consistent and more noticeable (as much as 0.05 F1 for the BERT models) performance drops can be seen for reducing alcohol consumption and smoking cessation. While this may indicate that client language varies more across topics, we note that client talk type prediction generally has lower performance than therapist behaviour prediction (Section 7), and thus it may be a more challenging task in general and need more training data irrespective of topic.
###### Abstract  
Two novel betaine surfactants with distinct hydrophilic headgroups were synthesized, including carboxylate surfactant (DCB) and sulfobetaine surfactant (DSB). Their properties of reducing the interfacial tension (IFT) of Xinjiang crude oil/water were studied under alkaline-free conditions, as were their thermal stability, wettability, and emulsification properties. The chemical structures of the target products were characterized and analyzed by using \({}^{1}\)H-NMR and \({}^{13}\)C-NMR. The experimental results indicate that the introduction of a benzene ring to the hydrophobic group can improve the solubility and high-temperature resistance of the betaine surfactant. Thermogravimetric analysis showed that the degradation temperature of the synthesized betaine was above 190 \({}^{\circ}\)C. As the concentration of the betaine solution increased, DSB (0.0750 mmol/L) showed a lower critical micelle concentration (CMC) than DCB (0.1852 mmol/L). The wetting ability of DCB was significantly higher than that of DSB, and their contact angles on paraffin film decreased to 28.36\({}^{\circ}\) and 35.26\({}^{\circ}\). In addition, both DCB and DSB can reduce the IFT of Xinjiang crude oil/water to ultra-low levels (10\({}^{-3}\) mN/m) in the absence of alkali. The appropriate ion concentration has a synergistic effect on the surfactant to reduce the interfacial tension of oil/water and the effect of the three ions on the interfacial tension was as follows: Na\({}^{*}\) + Ca\({}^{2+}\) + Mg\  
structure. In recent years, many studies have been carried out on the effect of modifying hydrophobic groups, hydrocarbon chain length, and the types of hydrophilic headgroup. For example, according to the similar compatibility principle, benzene rings inserted into hydrophobic groups will strongly interact with aromatic hydrocarbon components in crude oil, exhibiting a lower critical micelle concentration (CMC), ultra-low interfacial tension (IFT), and excellent wettability alteration [14; 15; 16]. Sulfobetaine surfactants exhibit better salt tolerance and are more efficient in alkali-free oil flooding, and carboxybetaine surfactants showed better surface properties [17; 18; 19; 20].  
In this study, two kinds of new and high-performance betaine surfactants with a benzene ring structure and different hydrophilic groups were synthesized: 1-((3-(4-dodecylphenoxy)-2-hydroxypropyl)dimethylammonio)acetate and 2-((3-(4-dodecylphenoxy)-2-hydroxypropyl)dimethylammonio)-2-hydroxypropane-1-sulfonate. This paper aims to research and develop a surfactant for oil flooding without alkali, with excellent properties of high temperature resistance and salt resistance. The chemical structures of the target products were characterized and analyzed using \({}^{1}\)H-NMR and \({}^{13}\)C-NMR. The physico-chemical properties of DCB and DSB were evaluated using thermogravimetric analysis and surface tension measurement. The wettability, emulsification performance, and interfacial tension of the two betaine surfactant solutions were systematically studied to evaluate their application in enhanced oil recovery.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Smart Healthcare Systems  
Edge-cloud computing has redefined healthcare through remote patient monitoring, intelligent diagnostics, and real-time emergency response. The proliferation of wearable medical devices and smart sensors enables continuous health tracking, where edge nodes analyze patient vitals and detect anomalies instantly. By decentralizing health analytics, edge computing ensures that critical alerts are generated without delays, enabling timely intervention and reducing dependency on centralized infrastructures [114; 115].  
Cloud services complement edge processing by providing deep learning (DL) capabilities for disease prediction, medical image analysis, and large-scale epidemiological modeling. The hybrid approach enhances diagnostic accuracy while supporting personalized treatment plans based on historical patient data. FL further strengthens privacy by training AI models across distributed edge nodes, preventing sensitive medical data from being exposed to centralized repositories [116; 117; 118].  
Emergency response systems leverage edge-cloud architectures to optimize medical resource allocation and dynamic dispatch of ambulances and personnel. AI-driven triage mechanisms assist in prioritizing emergency cases by analyzing real-time patient conditions, reducing response times, and improving survival rates. These innovations collectivelyenable intelligent, real-time, and scalable healthcare services, addressing the growing challenges of modern medical infrastructures [119; 120; 121].
## 5 Conclusions and Future Work  
In this paper, we have proposed a new approach for solving the CNICS problem of the IBCF approach, which employs interrelationship mining to increase available information on a new item. First, it extends binary relations between different attributes using the given binary relations. Afterward, it constructs interrelated attributes that can express characteristics between a pair of different attributes by using the new binary relations. Next, interrelated attributes are used to compute similarity, and items with a higher similarity will be selected to be part of the neighborhood of a new item. Finally, the proposed approach uses rating information of the neighborhood to predict rating score, and items with the highest predicted rating scores will be recommended. We evaluated the performance of the proposed approach using not only the accuracy but also the diversity metric. Because more available information can be extracted by performing interrelationship mining (e.g., interrelated attributes) for a new item, the approach achieved significant improvements in both the accuracy and diversity performance. Therefore, the proposed approach can be used in RSs to ameliorate the CNICS problem of IBCF.  
The interrelationship mining used in the proposed approach can extend the domain of comparison of different attribute values and make values of different attributes become comparable. But for RSs which have different types of binary relations and attributes, determining the type of binary relation that should be extracted and how many interrelated attributes should be constructed is a challenging issue; hence, we plan to look into this in our future work.  
Y.K. proposed the research direction and gave the conceptualization. Z.-P.Z. and T.M. implemented the experiment and performed the verification. Z.-P.Z. and Y.-G.R. wrote the paper. All authors contributed equally to the analysis, experiment, and result discussions.  
Figure 3: MP (**a**) and MN (**b**) results with MovieLens 1M dataset.  
Conflicts of Interest: The authors declare that there is no conflict of interests regarding the publication of this article.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 2 System Model and SWIPT-D2D Schemes
### Fading Model  
We assume that the channels between \(S\) and the terrestrial nodes undergo independent shadowed Rician fading with the following PDF:  
\[f_{|h_{si}|^{2}}(x)=\alpha_{i}e^{-\beta_{i}x_{1}}F_{1}(m_{si};1;\delta_{i}x),x \geq 0, \tag{20}\]where \(\alpha_{i}=(2b_{si}m_{si}/(2b_{si}m_{si}+\Omega_{si}))^{m_{si}}/2b_{si}\), \(\beta_{i}=(1/2b_{si})\), \(\delta_{i}=\Omega_{si}/({4b_{si}}^{2}m_{si}+b_{si}\Omega_{si})\); \(\Omega_{si}\) denotes the average power of the line of sight (LOS) component, \(2b_{si}\) denotes the average power of the multipath component, \(m_{si}\) denotes the Nakagami parameter, and \({}_{1}F_{1}(m_{si};1;\delta_{i}x)\) denotes the confluent hypergeometric function. For convenience, the fading coefficient \(m_{si}\) is assumed to be an integer. Then, the the PDF of \(\Lambda_{si}\) can be rewritten as  
\[f_{\Lambda_{si}}(x)=\alpha_{i}\sum_{\kappa=0}^{m_{si}-1}\frac{(-1)^{\kappa}(1- m_{si})_{\kappa}\delta_{i}^{x}}{\left(\eta_{s}\right)^{\kappa+1}(\kappa t)^{2}}x^{ \kappa}e^{-(\frac{\beta_{i}-\delta_{i}}{\eta_{s}})_{x}}, \tag{21}\]  
where \(\left(\cdot\right)_{\kappa}\) is the Pochhammer symbol. The corresponding CDF is obtained as follows:  
\[F_{\Lambda_{si}}(x)=1-\alpha_{i}\sum_{\kappa=0}^{m_{si}-1}\frac{(-1)^{\kappa} (1-m_{si})_{\kappa}\delta_{i}^{x}}{\left(\eta_{s}\right)^{\kappa+1}(\kappa t)^ {2}}\sum_{p=0}^{\kappa}\frac{\kappa t}{p!}\left(\frac{\beta_{i}-\delta_{i}}{ \eta_{s}}\right)^{-(\kappa+1-p)}x^{p}e^{-(\frac{\beta_{i}-\delta_{i}}{\eta_{s }})_{x}}. \tag{22}\]  
We assume that the channels among terrestrial nodes undergo independent Nakagami-\(m\) fading. Then, the PDF of \(\Lambda_{j}\) can be presented by  
\[f_{\Lambda_{j}}(x)=\left(\frac{m_{j}}{\Omega_{j}\eta_{r}}\right)^{m_{j}}\frac{ x^{m_{j}-1}}{\Gamma(m_{j})}e^{-\frac{m_{j}x}{\Omega_{j}\eta_{r}}}, \tag{23}\]  
where \(j\in\{ru1,u1u2\}\), \(m_{j}\) denotes the fading coefficient, \(\Omega_{j}\) denotes the average power, and \(\Gamma(\cdot)\) is the gamma function. The corresponding CDF is obtained as follows:  
\[F_{\Lambda_{j}}(x)=\frac{1}{\Gamma(m_{j})}\gamma\left(m_{j^{\prime}}\frac{m_{ j}x}{\Omega_{j}\eta_{r}}\right), \tag{24}\]  
where \(\gamma(\cdot,\cdot)\) is the incomplete gamma function.
## 3 Results and Discussion
For the 30-km transmission case, as was indicated by the eye-diagrams and by the narrow 6-GHz bandwidth system's TF before, the performance gets significantly degraded by the chromatic dispersion in the link, such that the equalization schemes at the receiver cannot compensate for the signal distortion. Note that pre-compensation of the total system's TF (Figure 5f) was implemented for the 25-km and 30-km cases, as was previously described in the 20-km transmission case. The sensitivity requirements of the scheme of Figure 1a at 7% HD-FEC threshold are summarized in Table 1. The 20% HD-FEC threshold is also drawn in Figure 6 so as to illustrate the sensitivity requirements for the corresponding reduced raw data rate. Taking the MLSE-1024 case as reference, the power budget of the different transmission scenarios can be estimated as 8.8, 8, 8, 7.2 dBm, for back-to-back, 10-km, 15-km, and 20-km, and 5 and 4.2 dBm for MLSE-4096 for 25-km and 30-km. Power budget improvements could be achieved with advances in transmitter optical sub-assembly (TOSA) output powers in such systems. Finally, in order to infer whether the use of the FIR filter at the input of the MLSE is essential, we take the scheme of Figure 1b as a worst case scenario, and we analyze the results omitting the FIR filter at the receiver's DSP. We use only the MLSE with different degrees of complexity, and we calculate the BER for back-to-back, 10-km, 15-km, and 20-km. The results are shown in Figure 7. As it is shown, employing only an MLSE at the DSP results in a performance similar to employing the hybrid implementation of FIR/MLSE for the back-to-back-case (Figure 6a). For 10-km, MLSE alone performs worse than the FIR/MLSE scheme (Figure 6b) by \(\sim\)1.5 dB at 7% HD-FEC threshold. For 15-km and 20-km transmission, the MLSE scheme alone cannot make the transmission feasible, regardless of the increase of the MLSE states; so, the FIR/MLSE implementation proves essential in that case to enable the 56 Gb/s PAM-4 transmission. So, the FIR filter proves essential to improve the performance if implemented before the MLSE, because it partially equalizes the signal and compensates for linear and quasi-linear distortions, so the MLSE can cope with the residual ISI and distortions of the signal, making the transmission feasible over 30-km. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **FIR** & **MLSE-16** & **MLSE-64** & **MLSE-256** & **MLSE-1024** & **MLSE-4096** \\ \hline Back-to-back & \(-9\) & \(-8.8\) & \(-8.8\) & \(-8.8\) & \(-8.8\) & \\
10-km & \(-7.5\) & \(-8\) & \(-8\) & \(-8\) & \(-8\) & \\
15-km & \(-7\) & \(-8\) & \(-8\) & \(-8\) & \(-8\) & \\
20-km & \(-5.5\) & \(-7\) & \(-7\) & \(-7\) & \(-7.2\) & \\
25-km & – & \(-3\) & \(-4\) & \(-4.1\) & \(-4.5\) & \(-5\) \\
30-km & - & - & - & - & \(-3\) & \(-4.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity requirements of the scheme of Figure 1a at 7% hard decision forward error correction code (HD-FEC) threshold.
## 2 Intelligent Vehicle Dynamics Model
### Trajectory Tracking Model  
The vehicle trajectory tracking model uses the lateral deviation and heading deviation between the actual vehicle and the ideal trajectory to characterize the trajectory tracking effect. The established vehicle trajectory tracking model is shown in Figure 3, where \(X\) and \(Y\) are the coordinate axes of the geodetic coordinate system.  
Figure 3: Vehicle trajectory tracking model.  
The heading deviation and its differential equation can be expressed as  
\[\left\{\begin{array}{l}\psi=\psi_{h}-\psi_{d}\\ \dot{\psi}=\dot{\psi}_{h}-\dot{\psi}_{d}\end{array}\right. \tag{13}\]  
In Formula (13), \(\psi\) is the heading deviation, \(\psi_{h}\) is the actual vehicle heading angle, and \(\psi_{d}\) is the desired heading angle of the vehicle.  
\(\psi_{h}\) and \(\psi_{d}\) can be expressed as  
\[\left\{\begin{array}{l}\dot{\psi}_{h}=\gamma\\ \dot{\psi}_{d}=\frac{\gamma_{x}}{\rho_{1}}\end{array}\right. \tag{14}\]  
In Formula (14), \(\rho\) is the radius of the curvature of the desired trajectory.  
Using the Serret-Frenet equation, the trajectory-tracking lateral deviation equation can be expressed as  
\[\dot{e}=v_{x}\sin\psi+v_{y}\cos\psi \tag{15}\]  
In Formula (15), \(e\) is the lateral deviation.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Case Two--Multi-Hop (Mesh)
#### 3.5.3 Updating of \(Mk_{\mathit{GW}}\)  
When \(SM_{n,g}\) leaves its group and from the network, destroying old \(Mk_{\mathit{GW}}\) and allocating a new master gateway key to all nodes \(SM_{n,g}\) in that group is very crucial. This is because the leaving node \(SM_{n,g}\) may be replaced by a vulnerable node to relay false messages and communicate with other nodes; therefore, \(Mk_{\mathit{GW}}\) revoking/re-keying is required. The \(NOC\) is responsible to inform the other \(SM_{n,g}\in GW_{g}\) nodes in that group and send a new \(MK_{\mathit{GW}}\) which is encrypted using \(K_{\mathit{sm,noc}}\).  
The following are the process steps of the updating \(Mk_{\mathit{GW}}\):  
1. A smart meter node \(SM_{n,g}\) must send a network leaving request \(L_{REQ}\) to the assigned dedicated node \(GW_{g}\) of that group. \(K_{\mathit{GW,SM}}\) ( \(L_{REQ}\), TS, ID \(SM_{n,g}\)).  
Figure 7: Authentication of a SM—Case 2.  
2. The \(GW_{S}\) will inform the other \(SM_{n,g}\) nodes in its group (multicast) and \(NOC\) (unicast) about the leave using the messages \(K_{GW,SM}\) ( \(L_{SM_{n,g}}\), TS, ID \(GW\)) and \(K_{GW,NOC}\) ( \(L_{SM_{n,g}}\), TS, ID \(GW\)).
3. Removing the node \(L_{SM_{n,g}}\) with \(L_{REQ}\).
4. \(NOC\) regenerates a new master gateway key \(MK^{\prime}_{GW}\) and sends it to the specific gateway. This, in turn, multicasts to all the remaining \(SM_{n,g}\) in the group, encrypted using \(K_{sm,noc}\). The updating of \(MK_{GW}\) process described above introduces a cost of re-keying. This cost has two factors:  
1. Additional processing overhead. Assume that the generation of a \(MK_{GW}\) key requires \(\times\) CPU cycles. The processing overhead, \(\text{Oproc}\ \text{noc}=1\times(\text{because}\ \text{NOC}\ \text{has}\ \text{to}\ \text{generate}\ \text{one}\ \text{key}\ \text{MK}_{GW}\) for \(GW\).
2. Communication overhead that includes two multicast and two unicast messages. The unicast messages are between the gateway and the \(\text{NOC}\) and the multicast messages are within the group.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Challenges
#### 3.3.1 Page Cloaking  
Several studies, including [PERSON] and [PERSON] et al. [66; 67] have improved OpenWPM's capabilities for crawling sites. However, if a site has a tracking script, it may detect the crawler and hinder its normal detection abilities, particularly for phishing sites. [PERSON] et al. [68] revealed that page cloaking used a combination of user interaction, bot behavior, and fingerprinting technology. Fingerprinting involves checking cookies, referrer, and user-agent to determine if the visitor is a person or an anti-phishing bot. To overcome this challenge, the study proposes a method to detect cloaking by comparing website visual similarities and analyzing code structure characteristics based on API calls and abstract syntax trees. To address the limitations of the previously discussed page cloaking, several improvements can be proposed. First, selective page cloaking can be implemented instead of full-scale cloaking. This approach applies context-specific cloaking that is triggered only for known bots or trackers. For instance, when a specific fingerprint matching common tracker characteristics is detected, a decoy page can be displayed. Second, dynamic page generation can be used to modify page elements each time the page is loaded, making it difficult for trackers to gather consistent data over multiple visits. This might involve subtle changes to the HTML structure, CSS, or JavaScript code to confuse scrapers and trackers. Finally, content decoy mechanisms can introduce a tracking decoy system that simulates fake user activities (e.g., clicks, scrolls) alongside cloaking. By introducing false behavioral data, this approach overwhelms trackers with misleading information, rendering their tracking efforts inefficient.