esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security and Privacy Challenges
### AI Role in EC Security
AI and EC are mutually beneficial to each other as they enable real-time dynamic adjusting and self-optimizing execution of IoT applications. The bottom-to-top arrow shown in Figure 10 represents optimization and the development of EC that requires the assistance of AI algorithms (e.g., computation offloading optimization). Alternatively, the top-to-bottom arrow indicates the need for EC deployment closer to edge devices, hence meeting the latency-sensitive requirements of AI applications [154]. [PERSON] et al. have placed edge intelligence in two groups. The first group, named \"AI for Edge\", or Intelligence, enables EC and utilizes AI technology in resource allocation, whereas the second group, \"AI on Edge\", or AI models at the Edge, carries out training of the models and inference at the edge [36]. A hierarchical framework proposed in [155] distributes data fusion and AI processing across three levels, i.e., edge nodes, edge servers, and the cloud. Figure 9: AI taxonomy. Data fusion eliminates data redundancy by combining data from multiple sources and thus improving AI performance. The authors of [156] proposed a hybrid learning framework, as current AI-based anomaly detection systems often report false detections. The proposed framework utilizes the Stackelberg game model combined with expert-guided ML rules for higher detection accuracy and minimal false detections. Mitigation techniques against data security and privacy threats are grouped into software-based and hardware-based approaches. In software-based security mechanisms, authentication keys are stored in the non-volatile memories of devices. However, innovations in hardware designs and computational abilities have facilitated data adversaries to breach the security measures adopted under software approaches. Alternatively, hardware-based techniques utilize dedicated hardware-integrated circuits or processors to accomplish cryptographic functions and store access keys. One of the principal challenges with hardware-based security techniques is their susceptibility to man-in-the-middle attacks. In such attacks, hackers can clone the device if the hardware security module becomes compromised. To address these limitations, [PERSON] et al. proposed hardware-based physically unclonable functions (PUFs) as a security primitive [157]. PUFs leverage intrinsic manufacturing alterations within devices to craft a unique fingerprint of the hardware, rendering it extremely challenging for hackers to reproduce these intrinsic characteristics. However, data acquired from PUFs are vulnerable to environmental factors and the physical conditions of the tested devices. Subsequently, numerous versions of PUFs have been proposed in the literature to enable device identification and authentication, compliant with a tolerable margin of error [158].
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 2 Related Work
### Reduction of the Number of Local Updates  
The second category includes studies that aim at reducing the number of local updates during the training process. For example, [PERSON] et al. [29] have proposed a novel FedMed method with adaptive aggregation using the topK strategy to select the top workers who have lower losses to update the model parameters in each round. Likewise, [PERSON] et al. [30] have provided a novel filtering procedure on each local update that allows transferring only the significant gradients to the server. The authors in [11] identify the relevant updates of the participants and upload only them to the server. In particular, at each round, the participants receive the global tendency and check the relevancy of their local updates with the global model, and only upload them if they align. [PERSON] and [PERSON] in [31] propose an FL protocol of two-step client selection based on their resource constraints instead of the random client selection. In addition, a global model update algorithm, namely FedPSO, proposed transmitting the model weights only for the client that has provided the best score (such as accuracy or loss) to the cloud server [32].  
Notice that our proposed FL model falls into the second category. We have been inspired by the studies discussed above, especially by CMFL [11] and FedProx [10], and we explored an approach that applies clustering optimization to bring efficiency and robustness in FL's communication. The most representative updates are uploaded only to the central server to reduce network communication costs.  
The state-of-the-art solutions analyzed mainly conduct experiments considering a CNN model, except for FedMed, which uses an LSTM model, and FedCS, which uses an NN model (cf. Table 1, second category). Hence, we have chosen to assess the performance of our approach (FedCO) by using a CNN model. While there are many datasets used for the evaluation of FL solutions in the literature, the recurrent ones are MNIST, FashionMNIST, and CIFAR-10. Hence, we have evaluated the performance of FedCO training the FL model on the three datasets mentioned above. Additionally, we used datasets from the LEAF FL repository (FEMNIST and CelebA) to benchmark the performance of our FL algorithm against FedAvg [6] and FedProx [10].
## 3 Results and Discussion
### The Concurrent Effect of L-Methioninase and Etoposide on Cell Viability  
The concurrent effect of L-methionineinase and etoposide on glioblastoma U87-MG cells was evaluated using a neutral red assay. These results indicate that the cytotoxicity of etoposide increased with the addition of L-methionineinase. L-methioninease increased the death of glioblastoma cells on the second day in both L-methioninease, L-methioninease, and etoposide combined culture groups. The combined use of L-methionineinase and etoposide resulted in a higher level of cell death in glioblastoma U87 MG and T98G cells compared to the individual use of either treatment in both 24 h and 48 h culture conditions. These findings indicate that L-methioninease can enhance the cytotoxicity of etoposide, leading to increased cell death in the glioblastoma U87 MG and T98G cell lines (\(p\) < 0.001) (Figures 4 and 5).  
Figure 3: IC\({}_{50}\) graph of L-methioninease on T98G cell line (\(p\) < 0.001).  
Figure 2: IC50 graph of L-methioninease on U87 MG cell line (\(p\) < 0.001).  
Evaluation of L-Methioninase with Etoposide on Mouse Embryonic Fibroblast (MEF) and Human Keratinocyte (HaCaT) Cells  
In addition to the cytotoxicity of L-methioninease and etoposide against glioblastoma U87 MG and T98G cells, their cytotoxic activity against normal cells was evaluated using a neutral red assay. Therefore, MEF and HaCaT cells were used. Based on the results obtained from the 24 h culture of MEF cells, L-methioninease exhibited lower cytotoxicity than etoposide.These results indicated that L-methioninease is less cytotoxic against normal cells even though it contributes less cytotoxic activity to etoposide and alleviates the side effects of etoposide against normal mammalian cells (Figure 6).  
Figure 4: The cytotoxicity of L-methioninease, etoposide and the combination of both agents on U87 MG cells was evaluated after 24 and 48 h of incubation. (\(p<\) 0.001).  
Figure 5: The cytotoxicity of L-methioninease, etoposide and the combination of both agents on T98G cells was evaluated after 24 and 48 h of incubation. (\(p<\) 0.001).
## 4 Discussion
### MyFlex-\(\delta\) vs. Their Own Prosthesis
#### 4.1.1 Ground Reaction Forces  
[PERSON] and [PERSON] [118] reviewed from other works that unilateral transtibial amputation results in the loss of vital muscles for body support, propulsion, midlateral balance, and swing phase initiation [119; 120; 121; 122]. This loss generates less symmetry in walking, and causes leg and back pain [124; 125]. According to some studies, amputees present more ground reaction force with the healthy leg [126; 127; 128]. The same results were obtained in this work with the three participants, as shown in the second section of Table 4 (\"Prosthetic Leg Ground Reaction Force/Healthy Leg Ground Reaction Force\"). The smaller the difference between the maximum values of the ground reaction force of the healthy leg and the amputated leg, the better for the symmetry of the gait. In other words, the closer the ratio between the ground reaction force given by the prosthetic leg and the ground reaction force given by the healthy leg to 100%, the greater the gait symmetry. As shown in the third section of Table 4 (\"Ground Reaction Forces Using MyFlex-\(\delta\)/Ground Reaction Forces Using their Own Prosthesis\"), current results of the MyFlex-\(\delta\) on ground reaction force symmetry are contradicting, as one participant improved their symmetry (Participant 1, going from a ratio of 91.4% to 94.0%), when using MyFlex-\(\delta\) instead of using their own prosthesis, as shown in the second section of Table 4), one participant showed similar ratios (Participant 2, from 99.9% to 98.8%) and one showed less symmetry (Participant 3, from 93% to 86%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Participant 1** & **Participant 2** & **Participant 3** \\ \hline \multicolumn{6}{c}{Mid Stance Duration} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 38.2\% & 1.8\% & 52.1\% & 2.9\% & 42.3\% & 3.1\% \\ Healthy leg, MyFlex-\(\delta\) & 42.4\% & 1.4\% & 58.1\% & 2.1\% & 45.6\% & 3.1\% \\ Prosthetic leg, own prosthesis & 38.5\% & 1.1\% & 39.9\% & 1.8\% & 34.5\% & 1.4\% \\ Prosthetic leg, MyFlex-\(\delta\) & 40.1\% & 0.9\% & 38.9\% & 2.2\% & 34.3\% & 1.0\% \\ \hline \multicolumn{6}{c}{Late Stance Duration} \\ \hline Leg and prosthesis used & mean & std & mean & std & mean & std \\ \hline Healthy leg, own prosthesis & 25.8\% & 6.6\% & 16.2\% & 1.7\% & 15.7\% & 1.4\% \\ Healthy leg, MyFlex-\(\delta\) & 22.6\% & 1.9\% & 13.6\% & 1.8\% & 16.4\% & 5.0\% \\ Prosthetic leg, own prosthesis & 16.4\% & 0.8\% & 20.9\% & 1.4\% & 15.4\% & 7.2\% \\ Prosthetic leg, MyFlex-\(\delta\) & 14.8\% & 0.6\% & 21.2\% & 1.0\% & 14.5\% & 0.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._
## 4 Discussion
### Design Challenges and Trade-Offs
#### 4.1.3 System Scalability  
Our solution is designed to scale efficiently as the number of users, data storage needs, and computational demands increase. The system achieves this through a modular service architecture, decentralized storage solutions, and optimized resource management. The platform relies on a service-based design where components such as storage, frontend, backend business logic, expert profile generation, and matchmaking operate independently. This allows for horizontal scaling by dynamically deploying additional backend instances as demand grows, ensuring that the system can handle increasing loads without performance degradation. A load-balancing mechanism can distribute requests across multiple backend servers, preventing bottlenecks and maintaining high availability.  
For data storage, SPARK-IT employs a hybrid approach that combines blockchain, decentralized storage, and traditional databases. Business proposals, mentorship agreements, and intellectual property-related documents are stored using the InterPlanetary file system (IPFS), reducing the reliance on centralized storage while ensuring tamper-proof, efficient file access. Only critical data, such as cryptographic proofs of intellectual property and mentorship agreements, are stored on Hyperledger Besu's permissioned blockchain. User profiles, general metadata, and transactional records are maintained in a distributed MariabB cluster, which is optimized for scalability through sharding and replication techniques. This ensures fast retrieval of essential data while minimizing the computational overhead of storing all records on-chain.
## 3 Experimental Setup
### Characterization of Tests with Sunflower Oil  
In the second set of trials, a ferromagnetic emulsion was prepared with available low-cost materials. The carrier fluid was sunflower oil, which was used alone in certain trails and to which iron filings obtained by grinding were added in others.  
Sunflower oil is employed as a base fluid to evaluate its feasibility for the custom preparation of MRFs, aligning with the low-cost approach considered in this study. This fluid represents an example of a liquid whose viscosity varies significantly with the temperature [36; 37; 38; 39; 40; 41; 42; 43], as represented by Figure 7. In the range of temperatures considered in this study (11.63-17.4 \({}^{\circ}\)C), the dynamic viscosity of sunflower oil ranges from \(\eta\) = 0.095 kg/s/m to \(\eta\) = 0.072 kg/s/m.  
The particle size of the iron fillings was smaller than 0.1 mm. Following the experiments by [PERSON] and [PERSON] [44], iron fillings and oil were mixed with a weight ratio of magnetic particles-to-liquid volume of 0.0128 g/mL. Since this percentage did not permit a clear vision of the sphere flowing through the fluid, the mixture was diluted until it could be assured that the tracking of the spheres was guaranteed, resulting in a proportion of 1/10 of the initial ratio, i.e., 0.00128 g/mL. The sedimentation of particles over time could  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sphere Id** & **Material** & **Diameter (mm)** & **Mass (kg)** & **Density (kg/m\({}^{3}\))** \\ \hline
1 & Lead & 38.0 & 0.31300 & 10,894.2 \\
2 & Glass & 14.5 & 0.00386 & 2418.2 \\
3 & Glass & 24.9 & 0.01980 & 2449.4 \\
4 & Plastic & 9.9 & 0.00048 & 944.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the spheres.  
Figure 7: Sunflower oil: Variation of Dynamic Viscosity with Temperature according to values in literature [36; 37; 38; 39; 40; 41; 42; 43].  
be observed through visual inspection. To ensure the highest possible homogeneity of particle dispersion, the mixture was stirred before the start of each test. Two glass spheres (Sphere Ids 2 and 3 in Table 1) and one plastic sphere (Sphere Id 4 in Table 1) were utilized to drop into this fluid, which is less viscous than the acrylic gel. These spheres also featured non-magnetic properties in order to not interfere with the magnetic field generated and have a lower density than the lead sphere. Their masses and diameters were measured, and the resulting densities were verified.
# Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
## 5 Network Architectures
### Model-A (Proposed 3D CNN Model)  
The goal of the proposed 3D CNN model architecture named Model-A is to recognize micro-expressions in video clips. The architecture is demonstrated in Figure 2. The dimensions height, width, and depth were passed as input into the model. Height and width are kept constant with a size of 128 \(\times\) 128 pixels, and the depth dimension varies based on the frame sequence length of each dataset (36 frames for CASME II, 26 frames for SMIC, and 30 frames for the Combined dataset). The entire model was created using the Keras framework. Initially, a sequential model was constructed to which the layers were added.  
Figure 1: The workflow of pre-processing steps.  
The main block of Model-A consists of two Conv blocks followed by flatten and dense layers. Each Conv block contains a convolution, batch normalization, max pooling, and dropout layer, as shown in Figure 3. For the convolutional layer, a 3D CNN was chosen instead of a 2D CNN because 3D CNNs are capable of capturing temporal information along with spatial information. Each convolutional layer operates with 16 filters and a kernel size of \(3\times 3\times 3\). The model uses a HeNormal initializer for better initial weights. The padding type was kept the same so that edge information was retained. The output from each convolution was passed to the batch normalization in order to converge the model faster and reduce the problem of overfitting. Then, 3D max-pooling with a \(3\times 3\times 3\) kernel was applied to decrease the spatio-temporal size and preserve critical features. Furthermore, to avoid overfitting, dropout layers were added to the model with a dropout rate of 0.4.  
As the dimensions for the dense layer need to be one-dimensional, a flatten layer was applied prior to dense. The first dense layer consists of 128 neurons, which is subsequently connected to another dropout layer. Then, a second dense layer with three neurons performs the classification into the three micro-expression classes. Finally, softmax is applied to the output. Rectified linear unit (ReLU) activation functions were used across all feasible layers. A summary of the sequential model is shown in Table 2, indicating the output feature map size of each layer.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 6 Possible Applications and Improvements
### Possibility of Unique and Novel Applications of the Channel Models
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Model** & **Application** \\ \hline Ref. [14] & A mathematical model for calculating the fading margin is applicable only for the \\  & South China Sea. \\ Ref. [15] & Applicable for over-the-sea communication in the deep sea. \\ Ref. [16] & Especially applicable for transmitting the data gathered from the ad hoc sensor nodes \\  & for environmental monitoring purposes. \\ Ref. [17] & Applicable for highly mobile water vehicles with high speed. \\ Ref. [18] & Applicable for airborne devices to sea vehicles. \\ Ref. [19] & Applicable for ship-to-shore communication. \\ Ref. [20] & Designed for applications requiring high data rate. \\ Ref. [21] & UAV to surface vessel based ocean surveillance. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. **Over-the-sea communication in the arctic sea:** Wireless communication in the arctic sea is quite challenging due to the icebergs. The channel models proposed in [13; 15] can be used in that kind of scenario.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
###### Abstract  
Many IoT use cases can benefit from group communication, where a user requests an IoT resource and this request can be handled by multiple IoT devices, each of which may respond back to the user. IoT group communication involves one-to-many requests and many-to-one responses, and this creates security challenges. In this paper, we focus on the _provenance_ that has been received by an _authorized_ device. We provide an effective and flexible solution for securing IoT group communication using CoAP, where a CoAP client sends a request to a CoAP group and receives multiple responses by many IoT devices, acting as CoAP servers. We design a solution that allows CoAP servers to digitally sign their responses in a way that clients can verify that a response has been generated by an authorized member of the CoAP group. In order to achieve our goal, we leverage Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). In particular, we consider that each group is identified by a DID, and each group member has received a VC that allows it to participate in that group. The only information a client needs to know is the DID of the group, which is learned using DNSSEC. Our solution allows group members to rotate their signing keys, it achieves group member revocation, and it has minimal communication and computational overhead.  
CoAP; group management; DNSSEC Article  
ToT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials  
1 Mobile Multimedia Laboratory, Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business, 10434 Athens, Greece; [EMAIL_ADDRESS] (V.A.S.); [EMAIL_ADDRESS] (G.X.); [EMAIL_ADDRESS] (G.C.P.)
# Adaptation in Gait to Body-Weight Unloading
## 4 Discussion
### Kinematics  
The range of ankle and knee joint angular displacement was significantly reduced during unloaded walking as in other locomotor studies that were focused on assessing immediate online changes in kinematics in response to body-weight unloading [24; 46; 47; 49]. There was reduced angular motion of ankle and knee joints in the form of aftereffects following the adaptation session (post-unloading) for up to three minutes. These modifications in kinematics similar to those observed during the adaptation phase were found to slowly decay and return to baseline levels by the end of the testing session. There were no changes in ankle or knee angular movement during or after the control adaptation session. This confirms that the kinematic changes observed after unloading are not caused by muscular fatigue.  
[PERSON] [40] found a significant increase in total ankle and knee angular movement from heel strike to peak knee motion during post-adaptation to unloaded walking. The disparity with the current results could be related to the differences in the nature of the unloading system. Unloading systems using a vertical harness produce inertial forces as a result of the systems mass. Thus, excessive joint excursions are produced to overcome these inertial forces while walking, which could have resulted in an aftereffect manifested as increases in knee and ankle joint excursion after unloaded walking in Reference [40]. Since unloaded walking inside the antigravity treadmill is not influenced by such mechanical constraints, the modifications observed in lower-limb movements in the current study might represent the pure aftereffects of adaptation to unloaded walking. Although the body-weight unloading techniques and the outcome measures used to quantify joint motion in the two studies were different, the fact that there was a distinct recovery curve in both studies indicates that there was an adaptive change in kinematics that was produced by unloaded walking.
## 3 Results and Discussion  
Figure 2 shows the coalescence along with the stretching and breakup of droplets. For two nozzle plates (N1 to N2) with different vertical distances between the camera's focus frame and nozzle plate, the frequency distribution of the measured droplet size was plotted, and their standard deviation, kurtosis, and skewness were calculated, as shown in Table 2. A standard deviation is a measure of how dispersed the data (droplet sizes) are in relation to the mean value. When examining nozzle plates N1 and N2, it became evident, as shown in Table 2, that the standard deviation increased as the vertical distance (H) between the camera's focus frame and the nozzle plate expanded. Furthermore, the arithmetic mean \(d_{10}\) (\(\upmu\)m) exhibited a corresponding increase with the vertical height (H) for both nozzle plates (N1 and N2), ranging from H0 to H2. The increase (10-15%)  
in the arithmetic mean and standard deviation was a consequence of the collisions and coalescence among the droplets.  
Skewness is an important statistical technique that helps determine the asymmetrical behavior of the frequency distribution, or more precisely, the lack of symmetry of tails on both the left and right sides of the frequency curve. A positive value of skewness means the right tail of the frequency curve is longer than the left tail, whereas a negative value indicates the opposite. In the context of the N1H0 and N2H0 experiments, this suggests that the initial droplet size distribution (0 mm) may conform to a log-normal distribution. The results, depicted in Figures 4 and 5, demonstrate a fairly acceptable fit with a log-normal distribution.  
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, whereas those with low kurtosis tend to have light tails. In the case of the N1H0 and N2H0 experiments, the value of kurtosis is high, which means the droplet size distribution curve must have heavy tails.