esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response
### Structure Inspection for Preventive Maintenance
[183; 184] used USVs to identify cracks and other structural problems in concrete seawalls and retention ponds, with computer vision methods. The authors prototyped a small and low-cost USV, mounted with a camera which captures images from the concrete structures, performs segmentation (i.e., separates water from concrete), and analyzes the concrete regions for the presence of cracks and water deterioration. [PERSON] et al. [79] proposes a novel solution to structure inspection, using a \"marsupial robot team\": A Sea-RAI USV hosts an UAV, used when necessary. The authors verify the robot team usefulness in several applications, such as littoral inspection, environmental monitoring, port security, and preventive infrastructure maintenance. The tests were performed in incremental complexity, at Bayboro Harbor and Pensacola Naval Station, both in Florida, United States. The authors identify that for marsupial platforms to be effective, better control interfaces and robot autonomy are needed.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 1 Introduction
* To customize the generalized network traffic into SDN flow traffic, this work targets to enrich the benchmark NSL-KDD dataset with unique MC-SDN-specific attack features, resulting in the improved effectiveness of the intrusion detection strategy. * The proposed FedSec optimally selects the potential feature set using the hybrid heuristic algorithm and constructs the malicious activity-aware graph structure with the enriched edge weight computation, offering the generation of high-level abstractive features as the input to the classification model. * The FedSec designs GCNN-GRU as a local model in the federated model, which collaboratively extracts spatial and temporal features in the large-scale and diversified SDN network traffic to determine the attack behaviors in the data plane layer.
# The Duality of Similarity and Metric Spaces
## Appendix A
### Proof of Theorem 6  
A real-valued function \(f(d)\) is said to be convex over the interval \([a,b]\in\mathbb{R}\) if for any \(d_{1},d_{2}\in[a,b]\) and any \(\lambda\in[0,1]\) we have that  
\[\lambda f(d_{1})+(1-\lambda)f(d_{2})\geq f(\lambda d_{1}+(1-\lambda)d_{2}) \tag{10}\]  
The validity of the triangle inequality \(s(x,z)+s(y,y)\geq s(x,y)+s(y,z)\) can be proven from the dual notion of distance \(s(x,y)=f(d(x,y))\) by applying \(d(x,z)\leq d(x,y)+d(y,z)\) and considering possible cases as follows [13].  
**Case 1:**\(d(x,z)\leq d(x,y)\)  
Thus we get \(f(d(x,z))\geq f(d(x,y))\). As \(0\leq d(y,z)\), we have \(f(0)=f(d(y,y))\geq f(d(y,z))\). We sum both expressions  
\[f(d(y,y))+f(d(x,z))\geq f(d(x,y))+f(d(y,z)) \tag{11}\]  
So the claim is proven.  
**Case 2:**\(d(x,z)\leq d(y,z)\)  
The reasoning is analogous to the above, just flipping \(x\) and \(z\).  
**Case 3:**\(d(x,z)>d(x,y)\wedge d(x,z)>d(y,z)\)  
As a metric is assumed, \(d(x,z)\leq d(x,y)+d(y,z)\). Hence  
\[1\leq\frac{d(y,z)}{d(x,z)}+\frac{d(x,y)}{d(x,z)}\implies 0\leq 1-\frac{d(y,z)}{d (x,z)}\leq\frac{d(x,y)}{d(x,z)}\leq 1 \tag{12}\]  
Let us pick any \(\lambda\in\left[0,\frac{d(x,y)}{d(x,z)}\right]\) such that \(1-\frac{d(y,z)}{d(x,z)}\leq\lambda\leq\frac{d(x,y)}{d(x,z)}\). Obviously \(0\leq\lambda\leq 1\). We see immediately that \(\lambda d(x,z)\leq d(x,y)\) and \((1-\lambda)d(x,z)\leq d(y,z)\). From the definition of convexity, we have that  
\[(1-\lambda)f(0)+\lambda f(d(x,z))\geq f((1-\lambda)0+\lambda d(x,z)) \tag{13}\]  
\[f(\lambda d(x,z))\geq f(d(x,y)) \tag{14}\]  
with the last inequality being due to the fact that \(f\) is monotonic decreasing. Similarly  
\[\lambda f(0)+(1-\lambda)f(d(x,z))\geq f(\lambda 0+(1-\lambda)d(x,z)) \tag{15}\]  
\[f((1-\lambda)d(x,z))\geq f(d(y,z)) \tag{16}\]  
By summing all inequalities (Appendix A9-A12) by transitivity \(\geq\) we get  
\[f(0)+f(d(x,z))\geq f(d(x,y))+f(d(y,z)) \tag{17}\]  
so obviously the triangle inequality holds here too.
## 5 Conclusions  
In this study, a fully integrated LCCL-S-compensated IPT system was proposed. Specifically, the extended DD coil is integrated into the transmitter coil as a compensation inductor, and the optimization of the distance between the two unipolar coils of the extended DD coil is carried out using the FEM simulation tool. The purpose of the optimization is to eliminate the coupling effect of the extended DD coil to the transmitter and receiver coils when the system is in the aligned state. Moreover, in the door-to-door misaligned state, the optimized extended DD coil and receiver coil can establish a secondary magnetic flux transmission path, generating a second magnetic coupling, thereby increasing energy transfer performance. Two sets of comparative experiments are established in which the compensation inductors are the DD coil and extended DD coil; the aligned efficiency of both is 94.35% at 3.3 kW with a 100 mm air gap. However, when the extended DD coil is used as the compensation inductor and the system is in a door-to-door 150 mm misaligned state, the output power maintains 63.6% of the well-aligned value, which is 1.05 kW higher than that of the DD coil. Our experimental results show that the proposed fully integrated structure maintains good performance in an aligned state and improves the system output power in door-to-door misaligned state, exhibiting good misalignment performance. Finally, the weight and volume advantages of integrated coils have been demonstrated.  
Conceptualization, J.X., G.L. and D.-H.K.; Data curation, J.X.; Formal analysis, J.X.; Funding acquisition, D.-H.K.; Investigation, G.L. and D.-H.K.; Project administration, S.J. and D.-H.K.; Writing--original draft, J.X.; Writing--review and editing, D.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-Automobile industry technology development) (20018829, Vehicle Underbody Charging Type Automatic Charging and Management/Control System Development) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
No new data were created.  
The authors declare no conflict of interest.  
Figure 13: Different types of compensation inductors: (**a**) extra inductor and (**b**) proposed extended DD coil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Occupied Space (cm3)** & **Gross Weight (kg)** & **Size (cm)** **X\(\times\)Y\(\times\)Z** \\ \hline Extra inductor & 75 & 0.39 & 5 cm \(\times\) 3 cm \(\times\) 5 cm \\ Extended DD coil & 0 & 0.12 & 10 cm \(\times\) 16 cm \(\times\) 0.5 cm \(\times\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing the weight and volume of different compensation inductors.
## 3 Results  
We used a stratified shuffle split approach for each dataset with the proportion of 75:15:10 corresponding to training, validation, and testing, respectively. Then, we used the two alternative approaches ADASYN and weighted training/cost-sensitive optimization to address imbalanced classes in the training set, as discussed in the previous section. In order to better evaluate the performance of the ML models for predicting class labels of PA and APS classification, we have used the precision, recall, and F1-score (Equations (3)-(5)) where TP is true positive, FN is false negative, and FP is false positive. Table 6 summarizes F1 score for PA and APS classification using LSTM models. Table 11 summarizes precision, recall, and F1 score for PA classification using LSTM models. Table 12 summarizes precision, recall, and F1 score for APS classification using LSTM models.  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F1\ Score=\frac{2*(Recall*Precision)}{(Recall+Precision)} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **NS-SS** & **NS-SB** & **NS-TR** & **EAS-SS** & **EAS-SB** & **EAS-TR** & **MS-SS** & **MS-SB** & **MS-TR** \\ \hline Before ADASYN & 10,860 & 5641 & 6025 & 96,704 & 2950 & 1684 & 15,088 & 2559 & 3465 \\ \hline After ADASYN & 96,985 & 96,426 & 96,806 & 96,704 & 96,667 & 96,691 & 97,112 & 96,590 & 96,753 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The size of training splits before and after applying ADASYN for balancing the training split of the data for classification tasks.  
Root Mean Squared Error equation (RMSE) is used to assess the performance of EE regression, Equation (6):  
\[RMSE=\sqrt{\frac{\Sigma(actual\ value-predicted\ value)^{2}}{n}} \tag{6}\]  
where \(n\) is the number of testing samples. All numerical studies are performed using TensorFlow 2.0 environment. In addition, several other Python libraries were used for data preprocessing [39; 55; 56].  
Additionally, we compared the performance of the multi-task XGBoost classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) to the independent XGBoost classification of APS types (NS, EAS, MS) and the independent XGBoost classification PA types (SS, SB, TR). We used ADASYN to address imbalanced classes in the training set. Table 7 summarizes the F1-score for PA and APS classification using XGBoost models. Table A3 summarizes precision, recall, and F1 score for PA classification using XGBoost models. Table A4 summarizes precision, recall, and F1 score for APS classification using XGBoost models.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 4 Causal State-Masking TD3 Algorithm
### Actor Networks
By minimizing \(L_{c}(\theta)\), the policy network is compelled to respond appropriately to changes in causal features under intervention conditions, reducing dependence on non-causal features. Additionally, to encourage the sparsity of the mask vector, we introduce a mask sparsity regularization term \(L_{m}(\mathbf{m})\) in the loss function:  
\[L_{m}(\mathbf{m})=\left\|\mathbf{m}\right\|_{1}=\sum_{i=1}^{n}\left|m_{i}\right| \tag{24}\]  
L1 regularization helps drive the weights of unnecessary features in the mask vector toward zero, achieving sparsity in feature selection. This reduces the complexity of the model and further reinforces the focus on key causal features. Upon combining the above designs, the loss function of the policy network \(L_{\mathrm{actor}}(\theta,\mathbf{m})\) can be expressed as  
\[L_{\mathrm{actor}}(\theta,\mathbf{m})=-\frac{1}{N}\sum_{i=1}^{N}Q_{\phi_{1}}( \mathbf{s}_{i},\pi_{\theta}(\mathbf{\hat{s}}_{i}))+\lambda_{c}L_{c}(\theta)+ \lambda_{m}L_{m}(\mathbf{m}) \tag{25}\]  
where \(N\) is the batch size and \(\lambda_{c}\) and \(\lambda_{m}\) are hyperparameters used to balance the causal consistency loss and the mask sparsity regularization, respectively, so that the policy network considers both causal relationships and feature selection during the optimization process. By performing gradient descent on \(L_{\mathrm{actor}}(\theta,\mathbf{m})\), we update the policy network parameters \(\theta\) and the mask vector \(\mathbf{m}\):  
\[\theta\leftarrow\theta-\eta_{\theta}\
abla_{\theta}L_{\mathrm{actor}}(\theta, \mathbf{m}) \tag{26}\]  
\[\mathbf{m}\leftarrow\mathbf{m}-\eta_{m}\
abla_{\mathbf{m}}L_{\mathrm{actor}}( \theta,\mathbf{m}) \tag{27}\]  
where \(\eta_{\theta}\) and \(\eta_{m}\) are the learning rates for the policy network and the mask vector, respectively. To ensure that \(m_{i}\in[0,1]\), the updated mask vector is clipped so that each element of the mask vector remains within a reasonable range. This prevents negative values or values exceeding 1, resulting in the effectiveness and interpretability of the mask when weighting features. \[m_{i}\leftarrow\mathrm{clip}(m_{i},0,1) \tag{28}\]  
With the above design, the actor network can generate high-quality policies and adaptively select and reinforce key causal features, enhancing the overall algorithm's convergence and generalization capabilities in path planning within complex dynamic environments.
# Extracellular Vesicles for Regenerative Medicine Applications
## 4 Clinical Development of EVs
### Clinical Trials Testing EV Therapies  
Currently, there are 20 active clinical trials investigating EV therapies and six clinical trials that have been concluded. Given the early stage of EV technology, most of these trials or studies are Phase I/II clinical trials focused on evaluating the safety and early efficacy of EV therapies for a wide range of clinical indications, directly and indirectly related to the traditional scope of TERM. Recently, the SARS-nCOVID-19 pandemic has accelerated the clinical translation of EV therapies in general, and these trials have been primarily \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Company** & \begin{tabular}{c} **Technology and** \\ **Therapeutic Target** \\ \end{tabular} & **Business Status** & **Headquarters** \\ \hline Capricor Therapeutics & \begin{tabular}{c} Exosomes for genetic diseases \\ and vaccines \\ \end{tabular} & Preclinical development & San Diego, CA, USA \\ \hline Codiak Biosciences & \begin{tabular}{c} IL-12 and STING loaded \\ exosomes for immune \\ activation in cancer \\ \end{tabular} & Clinical trials—Phases I–II/III & Cambridge, MA, USA \\ \hline Evox Therapeutics & \begin{tabular}{c} RNA-loaded exosomes for \\ gene therapy \\ \end{tabular} & Preclinical development & Oxford, United Kingdom \\ \hline Exostemtech & \begin{tabular}{c} Adipose-derived exosomes \\ for osteoarthritis \\ \end{tabular} & Clinical Trials—Phase I & Ansan, South Korea \\ \hline Florida Therapeutics & \begin{tabular}{c} Exosome-based \\ therapeutics for \\ neurodegenerative applications \\ \end{tabular} & Preclinical development & Livermore, CA, USA \\ \hline ILIAS & \begin{tabular}{c} Free-load exosomes for \\ drug delivery \\ \end{tabular} & Preclinical development & Daejeon, South Korea \\ \hline Mantra Bio & \
\begin{tabular}{c} Engineered exosomes for \\ targeted drug delivery \\ \end{tabular} & Preclinical development & San Francisco, CA, USA \\ \hline Puretech &
\begin{tabular}{c} Cow’s milk exosomes for \\ gene therapy \\ \end{tabular} & Preclinical development & Phoenix, AZ, USA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Companies developing EV-based products. directed toward testing EV-based therapies in the context of inflammatory conditions with a particular focus on EV-based therapies for SARS-nCOVID-19 [152; 153; 154]. Targeted cancer therapies are also common application of EVs in clinical trials, with these trials using modified EVs to deliver drugs to specific cellular targets as well as to mitigate the effects of aggressive therapies such as chemotherapy and radiation. Table 2 summarizes completed or active clinical trials for EV therapies. While not all of the trials reviewed herein were developed specifically to address questions in TERM, these studies are worth highlighting as the success of EV-based therapies across a wide variety of clinical indications will only help to facilitate the growth and translation of EV-based approaches in TERM.
## 7 Simulation Results and Comparison  
Figure 10 shows the block diagram of the torque control using the TSF method for the studied SRM. The individual phase torque reference is defined from the total torque reference by the TSF block. In the \"Torque-to-Current\" block, the phase torque references are converted into phase current references the rewritten torque-current profile model of the SRM (Equation (18)). A hysteresis current controller is used for the phase current tracking its reference waveforms.  
Matlab/Simulink was used to for the simulation model of the three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage by using the static torque characteristic profiles shown in Figure 9 and obtained using Equation (17). For the phase current tracking its reference waveforms accurately,  
Figure 9: Comparison of the calculated, FEM, and measured static torque characteristics for 0 to 8 A with an increment of 1 A.  
it is appropriate to set the hysteresis band of the current controller to \(\pm\)0.1 A through simulation test. The phase current reference is calculated using Equation (18). The same sampling time \(t_{sample}\) = 1 \(\upmu\)s is set in all simulation. Finally, the evaluation indices in terms of peak phase current, rms phase current, and torque ripple factor are compared between the proposed and the classical TSFs.  
The torque ripple factor is defined as follows:  
\[T.R.\%=\frac{T_{\max}-T_{\min}}{T_{ave}}\times 100\% \tag{19}\]  
where \(T_{max}\), \(T_{min}\), and \(T_{ave}\) are the maximum, minimum, and average torque, respectively.
## 2 Internet of Vehicles and Smart Cities
### Applications
#### 2.3.2 Social Internet of Vehicles  
As an evolution of the paradigm of IoV, the Social Internet of Vehicles (SIov) allows for the creation and management of relationships between vehicles in IoV, based on the context they are inserted, network architecture, or application requirements, behaving like a social network of vehicles [28]. These webs of relationships can introduce vehicles to other participants and enable vehicles to autonomously create new relationships that prove to be beneficial, be it because they improve traffic efficiency (through sharing road information) or because these vehicles have data useful for installed applications [29]. This paradigm opens doors to new applications and can help the development of the applications mentioned above, given the focus on data exchange and trust relationships between vehicles and between infrastructural nodes. Adding to this, the usage of Deep Learning in the application scenario is also a possibility to get better results, as it has been researched and evaluated for IoT [30].
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 5 Results and Discussion
### Clustering Evaluation  
In this section, we compare our proposed clustering methodology with the SSTFC [14]. The objective of applying the clustering in the present study is to allow the identification of the most relevant mobility flows, i.e., groups of trips with similar origin-destination that happen with a high frequency. Figure 7 shows the distribution of clusters given the amount of trips that they contain (their size) excluding trips considered as noise. The clusters were divided into classes with sizes greater than 1000, between 100 and 1000, between 10 and 100, and smaller than 10. We show results for two configurations of each approach, using \(\alpha\in\{0.5,0.05\}\) for SSTFC, and using Polynomial and Exponential model to classify the most relevant flows in our proposal, OD-MFC. The evaluation was performed with all of the clusters from the 48 \(\langle\)_weekday_, _hour_\(\rangle\) classes in the non-anonymized NYC Green Taxis dataset.  
The SSTFC algorithm showed a trend towards identifying a big amount of small clusters, with less than 1% of clusters sized greater than 10 for both parameters studied.  
Figure 6: Clustered flows from Yellow and Green Taxis datasets. (**a**) Yellow Taxis with geo-coordinates. (**b**) Yellow Taxis without geo-coordinates. (**c**) Green Taxis without geo-coordinates.  
Figure 7: Distribution of flow clusters according to their sizes.  
For both scenarios, SSTFC classified more than 99% of the data as noise. OD-MFC could identify more relevant clusters, with all of the selected clusters containing more than 10 trips, and around 60% of the clusters sized between 100 and 1000. OD-MFC classified 71% and 68% of the data as noise, respectively, for Polynomial and Exponential models. In total, we observed 9.7 and 33.4 thousand clusters for SSTFC with \(\alpha\) equals to 0.05 and 0.5; and, 1.8 and 3.0 thousand clusters for OD-MFC using Polynomial and Exponential models. Given our objective of selecting relevant flows for evaluation, OD-MFC performed better. Not only more relevant clusters were identified, but also a smaller part of the data was filtered out as noise. There is the possibility that better parameter selection could lead SSTFC towards better results, yet increasing the value of \(\alpha\) from 0.05 to 0.5 only increased the amount of smaller clusters identified. Furthermore, initial experiments with smaller \(\alpha\) (e.g., 0.005 and 0.0005) did not result in cluster formation, reducing \(\alpha\) increases the amount of data classified as noise, as expected. After the clustering step, the average flow of each cluster identified, using the exponential model, was evaluated according to our framework, as described in the following sections.
## 3 Fundamentals of GLI-Split
### Security Concerns and Countermeasures
#### 3.8.1 Potential New Attacks  
The Loc/ID-split-related issues can be generally classified as redirection attacks which constitute threats against confidentially, integrity, and availability [34]. The general idea of these attacks is that an attacker manipulates the information in the mapping components like the mapping cache in order to redirect existing flows to a new target. There, the packets of the redirected flow may either be just dropped to cancel the availability of a service, they could be monitored to violate confidentially, or the contents of the packets could be changed to break the integrity of the transferred data. These issues apply to all protocols where a mapping from an identifier to several locators is used. Examples are threats related to IPv6 multihoming [34], multipath TCP [35], or other new routing architectures like LISP [36].  
The threats can be divided into control-plane and data-plane threats. Control-plane threats involve map request and map reply messages. Map request messages could be used for amplifying DoS attacks where an attacker requests mapping information under a spoofed locator address. This locator address is the address of the victim which might then receive a possibly large amount of mapping information. A small map request message from the attacker thus causes a possible large map reply message to the victim. Another threat which involves map request messages is the cache update mechanism that is, for example, used by the mobility extension LISP-MN [37]. This mechanism could be used to either insert false mapping information or to cause a cache overflow. These threats related to the mapping cache could also be achieved by utilizing the locator gleaning concept on the data-plane. Locator gleaning was proposed by LISP and should reduce the amount of necessary mapping lookups and hence speed up the initial communication establishment.  
In the following, we provide more details about the threats related to locator gleaning and mobility update mechanisms and explain, how they can be avoided.