esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 2 Related Work
### Lightweight IoT Protocols and V2X
[PERSON] and [PERSON] (2019) couple MQTT with the lightweight cipher ASCON for secure fleet coordination. These architectures typically assume powerful brokers or dense RSU deployment; by contrast, our study evaluates TLS 1.3-secured MQTT on three homogeneous Raspberry Pi 4 devices, offering an end-to-end assessment of latency, goodput, and resource requirements when all MQTT entities run on mid-power IoT hardware--an arrangement directly applicable to infrastructure-light V2X scenarios.
## 4 Simulation Results
### Comparison with the IOSMC  
In order to compare the performance of the FOSMC design presented here, an IOSMC was implemented for the quadrotor's simplified model. Using the small angle approximation discussed above this controller is given by Equations (26), (27)-(29) with  
\[P_{z}=\ddot{x_{d}}-\lambda_{z}\dot{e_{z}}-\sigma_{z}\frac{d}{dt}sgn(s_{z})-\mu_{ z}s_{z}, \tag{52}\]  
\[P_{y}=\ddot{y_{d}}-\lambda_{y}\dot{e_{y}}-\sigma_{y}\frac{d}{dt}sgn(s_{y})-\mu_ {y}s_{y}, \tag{53}\]  
\[P_{x}=\ddot{x_{d}}-\lambda_{x}\dot{e_{x}}-\sigma_{x}\frac{d}{dt}sgn(s_{x})-\mu_ {x}s_{x}. \tag{54}\]  
where, as before, \(\lambda_{z}\), \(\lambda_{y}\), \(\lambda_{x}\), \(\sigma_{z}\), \(\sigma_{y}\), \(\sigma_{x}\), \(\mu_{z}\), \(\mu_{y}\) and \(\mu_{x}\), are real coefficients chosen in such a way that the corresponding solutions of the first order linear differential equations defined by the sliding surfaces  
\[s_{z}=0=\dot{e_{z}}+\lambda_{z}\dot{e_{z}}, \tag{55}\]  
\[s_{y}=0=\dot{e_{y}}+\lambda_{y}\dot{e_{y}}, \tag{56}\]  
\[s_{x}=0=\dot{e_{x}}+\lambda_{x}\dot{e_{x}}. \tag{57}\]  
asymptotically converge to zero. The simulation carried out with this controller corresponds to the one using the FOSMC with the control parameters given in Table 5. The tracking position errors and the Euler angles tracking errors are shown in Figures 27 and 28, respectively, while the 3 dPlot is depicted in Figure 29. One can notice that the tracking errors have a good performance when the disturbance appears. However, undesirable chattering appears in the control signals in particular with the vertical thrust \(u_{1}\), as it can be seen in Figures 30-33.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline \(\epsilon\) & 0.005 \\ \(\sigma_{z}\) & 0.01 \\ \(\lambda_{z}\) & 6.0 \\ \(\mu_{z}\) & 6.0 \\ \(\sigma_{y}\) & 0.01 \\ \(\lambda_{y}\) & 6.0 \\ \(\mu_{y}\) & 6.0 \\ \(\sigma_{x}\) & 0.01 \\ \(\lambda_{x}\) & 6.0 \\ \(\mu_{x}\) & 6.0 \\ \(Kd_{\phi}\) & 1.5 \\ \(Kp_{\phi}\) & 25 \\ \(Kd_{\theta}\) & 1.5 \\ \(Kp_{\theta}\) & 25 \\ \(Kd_{\psi}\) & 1.3 \\ \(Kp_{\psi}\) & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: IOSMC Parameters.  
Figure 27: Tracking position errors using an IOSMC.  
Figure 31: Roll Torque \(u_{2}\).  
Figure 30: Vertical Thrust \(u_{1}\).  
Figure 29: 3 dPlot of IOSMC with gains adjusted.
## 3 Methods  
### General Description  
As supported also by Figure 1, the proposed method involves a complex framework because two data sets were employed for the training and application of clustering and regression. The peculiarity of this work is that each building block of this complex problem was simplified as much as possible, given the knowledge of the critical points of multivariate data-driven wind turbine power curve models and of the limits within which simplifying is acceptable.  
In summary, the steps of the method are the following:  
* Divide \(D_{1}\) into clusters using the \(k\)-means algorithm, with \(k=2,\ldots,20\);
* For each \(k\), compute the average silhouette score;
* Select the value of \(k\) that gives the highest average silhouette score;
* Apply the \(k\)-means algorithm to the data set \(D_{2}\), using the value of \(k\) selected above and using the corresponding centroids \(C\) as initial values;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Data Points** & **Use** & **Measurements** \\ \hline \(D_{1}\) & 18,000 & Training & \(P,v_{\infty},\omega,\beta,T.\) \\ \hline \(D_{2}\) & 19,930 & Validation & \(P,v_{\infty},\omega,\beta,T.\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of the data set.  
Figure 2: Example of the raw data and of the pre-processed (\(D_{1}\)) data.  
* For each cluster of the \(D_{1}\) data set, train a LASSO regression and fix the hyperparameters and the coefficients through \(K\)-fold cross-validation (with \(k=10\));
* For each cluster of the \(D_{2}\) data set, simulate the output using the corresponding regression trained with the \(D_{1}\) data;
* Compute the error metrics by elaborating the difference between the measurements and estimations on the \(D_{2}\) data set.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.6 Cohen's Kappa Coefficient  
Cohen's kappa coefficient \(\kappa\), whose value is \([-1,1]\), uses the confusion matrix. Unlike the _accuracy_ metric, this coefficient considers the distributions of the actual and predicted classes. When the model's precision degrades due to the imbalance in said distributions, the kappa coefficient admits a more objective interpretation of the model's performance, preferentially attending to the minority class [54]. So,  
\[\kappa=\frac{p_{0}-p_{e}}{1-p_{e}}, \tag{5}\]  
where \(p_{0}\) represents the accuracy of the model and \(p_{e}\) is a measure of the agreement between the model predictions and the values of the actual classes (labels). In a specific context of binary classification, as in our work, this measure amounts to \(p_{e}=p_{e_{1}}+p_{e_{2}}\), where \(p_{e_{i}}\) is obtained by multiplying the percentage of the predicted class by the percentage of the actual class, assuming they are independent: \(p_{e}=p_{e_{1},\text{real}}\cdot p_{e_{1},\text{pred}}+p_{e_{2},\text{real}} \cdot p_{e_{2},\text{pred}}\).  
According to Equation (5), the further the distributions are from the predicted and actual classes, the smaller the value of the coefficient \(\kappa\) maximum achievable. The maximum value of \(\kappa\) poses a limiting scenario in which the number of false negatives and false positives in the confusion matrix is zero; all observations are correctly predicted. The coefficient \(\kappa\) reaches its maximum value when the model operates with balanced data, as is the case at hand. The numerator of Equation (5) denotes the difference between the overall precision of the model and the overall precision achieved by chance; the denominator describes the maximum value of the difference of the numerator. An admissible model will have a maximum and observed difference close to each other, leading to a null value of \(\kappa\). In a random model, the overall precision is random and the numerator vanishes, resulting in a value of \(\kappa\) equal to 0. The value of \(\kappa\) can assume negative values when the general precision of the model is even lower than that which can be set at random.  
[PERSON] suggested an interpretation of the degree of agreement according to the value of the coefficient \(\kappa\), which was later adapted by [PERSON] [55], as illustrated in Table 3. In short, the coefficient \(\kappa\) is a measure of the efficiency of the model as opposed to a classifier that behaves randomly. Somehow, it intends to correct the evaluation bias by considering a correct classification by chance. Although the coefficient \(\kappa\) has become a frequently used metric to compare classifiers, its behavior makes it difficult to interpret the values obtained. A heated debate has arisen about the advisability of its use as a performance metric or a model, which is why some authors advise against its use to compare different classifiers [56].
## 4 Results and Discussion
### Energy Considerations for Industrial Application
#### 4.7.3 Implications for Sustainability  
While bone ash improves stiffness and durability, its calcination requires additional energy input that could offset its environmental gains unless sourced from renewable energy or recovered heat. In contrast, WCO can facilitate lower viscosity and may support temperature reductions in future applications. As [PERSON] et al. (2022) [44] demonstrated, reducing the production temperature from 180 \({}^{\circ}\)C to 120 \({}^{\circ}\)C leads to 35% lower electricity consumption and significantly reduced fuel usage and emissions. Thus, WCO-based mixtures offer a promising pathway for more energy-efficient production, partially compensating for the higher energy demand of AB processing.
## 4 Results and Discussion
### Architectural Decisions  
After searching for and the evaluation of bibliographic data providers, it became clear that there is a lack of freely accessible, public APIs for bibliometric data retrieval. In addition, the established databases, such as Scopus or Web of Science, limit the use of their bibliometric data for software-as-a-service approaches. Their APIs are also costly, and the allowed number of exported elements is limited. This led to the design decision not to rely on such APIs but instead to set up our own databases and services for bibliometric data.  
For this purpose, the bibliometric data available under the Open Data Commons license from Microsoft Academic was revealed to be suitable. These data are updated every 2 to 4 weeks and also made available in Microsoft Azure. The Microsoft Academic Graph (MAG) is generated automatically using crawlers and other services and, in addition to the bibliometric entities, also provides data on related publications and offers broad support for other languages and the writing systems, such as Cyrillic and traditional Chinese.  
Compared to other bibliometric data providers, such as Web of Science or Scopus, MAG offers a considerable scope with over 260 million publications (as of 2021-05-28). However, it lags somewhat behind the two classical services in terms of data quality, e.g., duplicate detection, according to the authors of [40]. A study from 2021 confirms MAG's high coverage and quality, including of non-English academic writings [41].  
The microservice architecture suggests to vertically split the persistence layer and data model to provide an independent persistence layer and data model per microservice. Bibliometric datasets generated for analysis are large in size, e.g., a single dataset may contain up to 100,000 records. Furthermore, a bibliometric dataset depicts a graph that may be analyzed using graph algorithms, which may run faster on graph databases compared to relational databases due to the nature how relations are stored in a graph database.  
The latter pose a significantly better performance on string matching [42], which is needed to provide a fast-string search of bibliographic data to the user. For these reasons, graph-based computations, such as breadth-first and depth-first searches, are performed on an in-memory graph database that holds a subset of the bibliometric entity attributes.  
In contrast, classical retrieval operations on the whole dataset are performed on a relational database cluster using indexed attributes (e.g., a unique MAG entity id) and SQL joins.  
Full-text search operations, for example, on the title and abstract of publications, are performed based on specially optimized query strategies (e.g., fuzzy or proximity queries) and index structures of a search engine, such as suffix trees or directed acyclic word graphs [43]. For the implementation of the ambalytics prototype, the external Microsoft PAK service was used. Alternatively, a separate search cluster instance based on ElasticSearch could have been used (see Section 3.7).
## 5 Conclusions  
In this study, an investigation into the influence of belt grinding parameters on material removal depth based on the Taguchi parameter design method has been analysed and presented. In the belt grinding experiments, three levels of cutting wheel speed, feed rate, force, grit size, and polymer hardness were applied. Based on the experimental results and summarising the research, the following generalised conclusions are drawn:  
1. ANOVA determined the level of significance of the machining parameters on the material depth of cut. Based on the analysis of variance (ANOVA) results at a 95% confidence level, the highly dominant parameters of material removal are identified. Namely, the grit size grinding parameter is the primary factor that has the highest influence on the material removal, and this parameter is about five times greater than the second ranking parameters (RPM and force imparted). The feed rate and polymer wheel hardness parameters do not seem to have much of an influence on the depth of cut, i.e., material removal. Results from ANOVA interactions also suggests that the experimental trials can further be optimised using Taguchi Interaction instead of orthogonal design.
2. Based on the signal-to-noise ratio results in Figure 9, we can construe that 750 RPM, 10 mm/s feed rate, 30 N force, 90 Shore A hardness, and 60 grit size are the optimal grinding parameters for achieving maximum depth of cut.
3. A method of modelling and calculating the material removal using ANFIS is proposed in this paper. The ANFIS model developed is validated with experimental trials for given conditions. It has been identified that results produced by the designed regression model have acceptable deviations between the predicted and the actual experimental results with 93.5% accuracy. The ANFIS model developed in this research work is viable and could be used to predict the depth of cut, i.e., material removal for an Abrasive Belt Grinding process.  
This work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF), Singapore, under the Corp Lab@University Scheme.  
**Author Contributions:** [PERSON] conducted Data curation, investigation, data processing, formal analysis and original draft preparation. [PERSON] conducted data processing, formal analysis,  
Figure 17: Correlation between actual and predicted values of test and train data set.  
validation, review and editing. [PERSON] provided Funding acquisition, conceptualization, resources, formal analysis, review and editing. [PERSON] conducted Data curation and original draft preparation.  
The authors declare no conflict of interest.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Energy Conservation Techniques** & **Strategy Implemented for Achieving Energy Savings** & **Targeted Components for Energy Reduction** & **Layer of Implementation** & **Suitability in LWSN** \\ \hline \multirow{4}{*}{Power Management} & & Reduces the energy consumption of the sensor node by applying duty cycling on the MCU, radio, and sensor, switching them between active and sleep modes. & Data & Yes \\ \cline{2-6}  & & Reduces the energy consumption of the radio transceiver by dynamically adjusting radio parameters (coding and modulation rate, transmission power, antenna direction) & Radio & Physical & Yes \\ \cline{2-6}  & Radio Optimization & & & & \\ \cline{2-6}  & & using techniques such as transmission power control, cooperative communication schemes (SISO and MIMO), radio sleep/wake-up. & & & \\ \cline{2-6}  & Energy-Efficient MAC Protocol & & Reduces the energy consumption of the radio by affecting radio idle listening, overhearing, over-transmitting, error control, retransmission rate, channel collisions, medium access, etc. & Radio & Data Link & Yes \\ \cline{2-6}  & & Reduces the energy consumption of the microprocessor by dynamically adjusting the power with respect to workload. It enables intelligent trade-offs between energy consumption and operational fidelity using techniques such as dynamic voltage scaling (DVIS), dynamic voltage frequency scaling (DVIS), dynamic power management (DFM), etc. & MCU & Application & Yes \\ \hline \multirow{4}{*}{Data Reduction} & & Reduces the energy consumed during data transmission within the WSN by removing redundancies in the received data from the neighboring nodes and extracting the useful information by means of aggregation functions (maximum, minimum, average, etc.) as data travel over the network. & Radio & Application & Yes \\ \cline{2-6}  & Data Compression & & Reduces the energy consumed during data transmission by reducing the amount of data to be transmitted. This involves minimizing the number of bits required to represent each data block. & Radio & Application & Yes \\ \cline{2-6}  & Data Prediction & Reduces energy consumption by minimizing the number of data transmissions. It involves predicting part of the sensed data without any transmission. & Radio & Application & Yes \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rationale behind energy conservation techniques for extending WSN lifetime.
## 2 Optimization Problem with Constraint on PAPR
Suppose that \(a(t)\) is an even and symmetric function in the interval \([-T_{s}/2,T_{s}/2]\):  
\[a(t)=a_{0}+\sum_{k=1}^{m-1}a_{k}\cos(2\pi kt/T). \tag{7}\]  
The optimization problem (6) can be reduced to the problem of finding a set of coefficients that correspond to the minimum of a function of many variables [19; 20]:  
\[J\Big{(}\{a_{k}\}_{k=1}^{m-1}\Big{)}=T_{s}/2\sum_{k=1}^{m-1}a_{k}^{2}(2\pi k/T _{s})^{2n}. \tag{8}\]  
The value of \(m\) is determined based on the representation accuracy of sought function \(a(t)\), and it is usually between 10 and 14 [20] for FTN signals with a duration of up to \(T_{s}=16\)-32 T.  
The constraints on signal energy and boundary conditions that provide the energy spectrum reduction rate of at least \(1/f^{2(n+1)}\) are as follows [24]:  
Figure 1: Weighting function shape. \[\int_{-T_{s}/2}^{T_{s}/2}a^{2}(t)dt=1,\ a^{(k)}(t)|_{t=\pm T_{s}/2}=0,k=1\ldots(n-1), \tag{9}\]  
where \(a^{(k)}(t)\) denotes the \(k\)th derivative of the function \(a(t)\). The nonlinear inequality constraint on the ISI level can be expressed numerically using the cross-correlation coefficient [20]:  
\[\max_{k=1\ldots(L-1)}\biggl{\{}\int_{-T_{s}}^{T_{s}}a(t)a(t\pm k/R)dt\biggr{\}} <K_{0}. \tag{10}\]  
The bit rate \(R\) of binary channel symbols is included in constraint (10) and affects the cross-correlation coefficient. Introduce an additional constraint on the PAPR magnitude of the \(j\)th realization of a random signal packet \(y_{opt}^{(j)}(t)\). The maximum PAPR of signal packet (3) with a packet length \(N_{p}\), an average power \(P_{average}\), and a peak power \(P_{max}\) can be calculated by:  
\[\max\{PAPR\}=\max\frac{P_{max}}{P_{average}}, \tag{11}\]  
\[y_{1}(t)=(A_{0}/\sqrt{2})u_{1}(t)\cos(2\pi f_{0}t+\phi_{0}),\ y_{2}(t)=(A_{0}/ \sqrt{2})u_{2}(t)\sin(2\pi f_{0}t+\phi_{0}), \tag{4}\]0  
The nonlinear inequality constraint on the PAPR may be expressed as follows:  
\[y_{1}(t)=(A_{0}/\sqrt{2})u_{1}(t)\cos(2\pi f_{0}t+\phi_{0}),\ y_{2}(t)=(A_{0}/ \sqrt{2})u_{2}(t)\sin(2\pi f_{0}t+\phi_{0}), \tag{4}\]1  
where \(\mathrm{PAPR}_{0}\) denotes the PAPR value before optimization with constraint (13) and \(\mathrm{PAPR}_{\mathrm{opt}}\) denotes the PAPR value after optimization with (13). After optimization, the maximum PAPR of a signal packet consisting of \(N_{p}\) symbols is reduced by \(\Delta\mathrm{maxPAPR}\) dB. A feature of solving the optimization problem (8) when using constraint (13) is that the shape of the optimal function \(a(t)\) will depend on the packet length \(N_{p}\). Moreover, with an increase in the pulse duration up to \(T_{s}=16T-32\) T, the value \(N_{p}\) needs to be increased significantly even for binary signals. Consider the solution of the optimization problem in such conditions in detail.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 5 Implementation and Evaluation
### Configuration Time  
Using an 8 \(\times\) 8 mesh NOC architecture and an injection rate of 0.15, we compared it with two typical packet-switched NOC networks, AEthereal [26], dAElite [25], and a hybrid NoC network CTN [34] with configuration time. It can be seen from Table 3 that the system configuration speed of our method is 125 times faster than AEthereal, 10 times faster than dAElite, and 3.8 times faster than CTN. The main reason for this time improvement is the advantage of the fast path used for feedback on exceptions and the use of multicasting.  
Figure 14: Average latency simulation results in the 8 \(\times\) 8 mesh node under (**a**) uniform random traffic, (**b**) CTG-based traffic, (**c**) Rent’s rule traffic.  
Figure 15: Throughput (in Mbps) normalized to PS on 8 \(\times\) 8 mesh under PARSEC benchmark suite.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AEthereal [26]**} & \multicolumn{2}{c}{**dAElite [25]**} & \multicolumn{2}{c}{**CTN [34]**} & \multicolumn{2}{c}{**PCCNoC**} \\ \hline  & ideal & measured & ideal & measured & ideal & measured & ideal & measured \\ \hline
**Cycles** & 246 & 1000 & 60 & 81 & 16 & 30 & 8 & 8 \\ \hline
**Cost factor** & 30.8\(\times\) & 125\(\times\) & 7.5\(\times\) & 10\(\times\) & 2\(\times\) & 3.8\(\times\) & 1\(\times\) & 1\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration time in cycles.
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 2 Related Work
### Self-Supervised Learning  
Self-supervised learning has gained widespread attention in the fields of CV [42; 43; 44], NLP [45; 46], and graph [47; 48] as it can learn valuable feature representations from extensive unlabeled data. Typically, several attempts introduce self-supervised learning into recommendation, effectively addressing the challenge of data sparsity to achieve improvements in recommendation performance [25; 30; 31; 32; 49; 50]. The typical exploration is SGL [25], which leverages three data augmentation operators to construct structured views for contrastive learning in the collaborative filtering framework. AdaGCL [26] designs an adaptive graph generation model and a graph denoising model to create contrastive views to enhance the CF paradigm. In recent explorations, the self-supervised paradigm for knowledge-aware recommendation has shown great potential through data self-discrimination to learn richer latent semantic information. KGCL [27] attempts to leverage a joint contrastive learning paradigm to bridge the knowledge graph and the user-item interaction graphs to address noisy and sparse issues. KGTN [28] introduces a novel local-global contrast mechanism in its contrastive learning module, enhancing the robustness of user and item representation learning. KGRec [31] is the first attempt to unify generative and contrastive learning for extracting implicit semantics from the KG in a rationale-aware manner. HCVLs [29] design a hierarchically coupled view-crossing contrastive learning paradigm to addresses noise issues. Motivated by the above self-supervised learning frameworks, we design a new SSL-based method for knowledge-aware recommendation by hierarchically refining node representations and aligning multi-view semantics across the KG and the UIG.