esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 4 Results and Discussion
### Fine-Grained Image Classification  
Fine-grained image classification aims to recognize similar subcategories of objects under the same basic level category. The difference of fine-grained recognition compared with general category recognition is that fine-grained subcategories often share the same parts and usually can only be distinguished by the subtle differences in the texture and color properties of these parts. CUB-200-2011 [25] is a widely used fine-grained dataset that consists of images of 200 bird species. There are about 30 images for training for each class. We used ResNet-50 to test the performance of our method on CUB-200-2011 to verify the generalization of different types of computer vision tasks. For a fair comparison, the model was trained from scratch for 300 epochs with a batch size of 32, and the learning rate was decayed by a factor of 0.1 at Epochs 150 and 225.  
As shown in Table 6, we improved the accuracy of ResNet-50 from 66.73% to 76.26% (independent-samples \(t\) test, \(p\)-value = 2.8532 \(\times 10^{-7}\)\(<\) 0.001), +9.53% with Feature Mining, which significantly surpassed previous self-distillation methods. It also shows that fully utilizing feature information has a high gain effect even on fine-grained image classification, which requires more subtle differences. Figure 3 shows the validation accuracy comparison among the baseline, BYOT, and Feature Mining on CUB-200-2011 with ResNet-50. The accuracy of BYOT and our method was much higher than the baseline, and the accuracy of Feature Mining was higher than that of BYOT.  
\begin{table}
\begin{tabular}{l c} \hline
**Method** & **Validation Accuracy (\%)** \\ \hline ResNet110 & 62.42 \(\pm\) 0.25 \\ ResNet110+Dropout & 62.32 \(\pm\) 0.05 \\ ResNet110+Spatial Dropout & 62.55 \(\pm\) 0.10 \\ ResNet110+DropBlock & 63.13 \(\pm\) 0.29 \\ ResNet110+BYOT & 63.03 \(\pm\) 0.17 \\ ResNet110+CS-KD & 64.29 \(\pm\) 0.11 \\ ResNet110+FM & 65.22 \(\pm\) 0.06 \\ \hline \end{tabular}
\end{table}
Table 4: The performance comparison on the Tiny ImageNet dataset. The best accuracy is achieved by Feature Mining.  
\begin{table}
\begin{tabular}{l c} \hline
**Method** & **Validation Accuracy (\%)** \\ \hline ResNet-50 & 76.32 \(\pm\) 0.02 \\ ResNet-50+FM & 77.26 \(\pm\) 0.04 \\ \hline \end{tabular}
\end{table}
Table 5: The performance comparison on the ImageNet dataset on ResNet-50.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 2 Background on Federated Learning
### Federated Training Process  
The federated training (FT) process is performed by the OSS, which orchestrates the entire training by repeating the following steps until it is considered successful:  
1. **Client selection**: The OSS samples from a set of clients meeting eligibility criteria. In cross-device settings, the criteria are about connectivity (e.g., a device participates only if it is under Wi-Fi connectivity) and power (e.g., a device participates only if it is connected to a power supply). In the cross-silo setting, usually, all the clients participate in the training process.
2. **Broadcasting**: The clients that will participate in the training process download the current model weights and the training program from the OSS.
3. **Client computation**: Each client locally trains the model on its data and computes the new weights for the model.
4. **Aggregation**: The OSS collects the produced models and aggregates them. This stage is crucial for security and performance improvement.  
Model update: The OSS locally updates the shared model based on the aggregated update computed in the previous step.  
After the model update, each client is ready to retrieve an updated version of the model from the OSS. This training process is formalized by the FedAvg algorithm [9], whose pseudocodes of the procedure of FT in the OSS and the client update are shown in Algorithms 1 and 2, respectively. In these algorithms, \(K\) is the total number of clients, \(B\) is the size of mini-batches, \(T\) is the total number of communication rounds, \(E\) is the local training epochs, and \(\eta\) is the learning rate.  
```
Initialize global model \(\theta_{0}\) for\(t=1,2,\dots,T\)do  Select \(m=C\times K\) clients, where \(C\in(0,1)\) for all\(k=1,2,\dots,m\)in paralleldo  Download \(\theta_{t}\) to Client \(k\)  Client \(k\) performs update and returns \(\theta_{k}\) endfor  Update global model: \(\theta_{t}\leftarrow\frac{1}{m}\sum_{k=1}^{m}\frac{n_{k}}{n}\theta_{k}\) endfor
```  
**Algorithm 1** Procedure of OSS Federated Training
## 2 Methodology
### TV Regularization
The objective function of the TV regularization model can be expressed as:  
\[S(g)=\frac{1}{2}\|Ag-b_{\mathrm{meas}}\|_{2}^{2}+TV_{\beta}(g)=\frac{1}{2}\|Ag -b_{\mathrm{meas}}\|_{2}^{2}+\lambda\sum_{i}\sqrt{\|L_{i\mathcal{G}}\|_{2}^{2} +\beta} \tag{13}\]  
where \(A\) denotes the sensitivity matrix. Equation (13) can be used to solve for the minimal value of the above equation using [PERSON]'s method, where the gradient function can be expressed as follows:  
\[S^{\prime}(g)=A^{T}(Ag-b_{\mathrm{meas}})+\lambda L_{\beta}(g)g \tag{14}\]  
Figure 2: Three different curves connecting points A and B have the same total variation of the same magnitude. where \(L_{\beta}(g)=L^{T}\Big{(}\text{diag}\,\sqrt{\|L_{i}g\|_{2}^{2}+\beta}\Big{)}^{-1}L\) and diag is a diagonal matrix. The Hessian matrix of Equation (13) can be expressed as:  
\[H(g)=A^{T}A+\lambda L_{\beta}(g) \tag{15}\]  
Therefore, the conductivity distribution can be solved for as follows:  
\[TV(f)=\int_{A}^{B}f^{\prime}(x)dx=f(B)-f(A) \tag{8}\]0  
Compared to the Tikhonov regularization method, the TV regularization algorithm effectively captures edge information in discrete media and minimizes the smoothing effect imposed on edges. However, in practical applications, the TV method tends to introduce a'step effect' in smooth areas while resolving sharp edges. This results in a decrease in the overall resolution of the reconstructed image, which limits the applicability of the TV method in EIT. In this study, we propose further improvement and optimization for the TV regularization method to enhance the resolution of reconstructed images.
# Dynamic Behavior of a Simple Rolling Seismic Isolator with a Position Restoring Device
## 2 Equation of Motion  
### Description of Analysis Model  
The RIS consists of a lower rolling plate with a concave surface called a base table, an upper plate called a superstructure, and rollers between the two plates. The rollers and plates that make up the core components have been manufactured in high carbon chromium bearing steel and chromo molybdenum steel for high mechanical strength, fatigue strength, high hardness, and wear resistance. When the base table is excited by an external force, relative movement of the base and the upper structure is generated, and the rolling friction of the roller acts as a damping force. In this system, the damping force can be controlled by adjusting the coefficient of rolling friction. Since the potential energy change of the superstructure is small at the center, when the friction coefficient is increased, the frictional force is increased, and the upper structure is not restored to the original position after the movement, and stops at an arbitrary position. Therefore, it is necessary to provide a PRD for quick restoration to the original position.  
As shown in Figure 1, the proposed model consists of a lower rolling plate, a base table with a concave surface, and a PRD for rapid restoration. The PRD is assembled by providing a top plate with a wedge-shaped (or hemispherical) groove and a spring-damper-sliding unit that is installed on the base table by precompressing the spring. This device prevents the superstructure from moving horizontally due to a weak earthquake or unnecessary vibration, and makes it return to the initial position quickly after the earthquake. Stick-slip friction occurs at the contact surface between the spring tip of the device and the wedge hole.  
In Figure 1, \(R\) is the radius of the concave surface of the base table, \(r\) is the radius of the roller, and \(u\) is the seismic displacement of the base table; \(x\) and \(v\) are the relative and absolute horizontal displacements of the superstructure, respectively, and have the following relationship with the base displacement,  
\[v=\ u+x. \tag{1}\]  
The horizontal displacement \(x\) and the vertical displacement \(y\) of the base table have the following geometric relationships, respectively.  
\[x=L\ \sin\theta \tag{2}\]  
and  
\[y=L\ (1-\cos\theta)\, \tag{3}\]  
where, \(L=R-r\).
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
###### Abstract  
In multipath video streaming transmission, the selection of the best vehicle for video packet forwarding considering the junction area is a challenging task due to the several diversions in the junction area. The vehicles in the junction area change direction based on the different diversions, which lead to video packet drop. In the existing works, the explicit consideration of different positions in the junction areas has not been considered for forwarding vehicle selection. To address the aforementioned challenges, a Junction-Aware vehicle selection for Multipath Video Streaming (JA-MVS) scheme has been proposed. The JA-MVS scheme considers three different cases in the junction area including the vehicle after the junction, before the junction and inside the junction area, with an evaluation of the vehicle signal strength based on the signal to interference plus noise ratio (SINR), which is based on the multipath data forwarding concept using greedy-based geographic routing. The performance of the proposed scheme is evaluated based on the Packet Loss Ratio (PLR), Structural Similarity Index (SSIM) and End-to-End Delay (E2 ED) metrics. The JA-MVS is compared against two baseline schemes, Junction-Based Multipath Source Routing (JMSR) and the Adaptive Multipath geographic routing for Video Transmission (AMVT), in urban Vehicular Ad-Hoc Networks (VANETs).  
junctation-aware; VANETs; multimedia; video streaming; multipath; vehicular network; next forwarding vehicle; vehicle selection Article
## 2 Methodology
### Fuzzy Control System
#### 2.2.1 Fuzzification  
In this phase, the input consists of the final scores calculated for each KPI, while the output includes both the corresponding linguistic evaluations and their fuzzy membership degrees.  
To get a linguistic evaluation, each final score is mapped onto a four-point linguistic scale based on the model proposed by [47]. The linguistic terms used are: Very Low, Low, High, and Very High.  
In this study, as in many other studies [48; 49], trapezoidal functions are adopted to model the fuzzy numbers. The mathematical definition of a trapezoidal fuzzy number is expressed through membership function (2):  
\[\mu(x)=\left\{\begin{aligned} 0,& x<a\\ \frac{x-a}{b-a}a&\leq x\leq b\\ 1,& b\leq x\leq c\\ \frac{x-d}{c-d}c&\leq x\leq d\\ 0,& x>d\end{aligned}\right. \tag{2}\]  
where:  
* \(x\) represents the numerical value of the variable (i.e., the final score against the selected KPI);
* \(\mu(x)\) is the corresponding membership degree;
* \(a\), \(b\), \(c\), and \(d\) are the parameters that define the shape of the trapezoidal fuzzy number, based on decision-makers' judgment or benchmark tables.  
As a result, this phase provides two outputs: the linguistic evaluation and the corresponding membership degree for each KPI.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 4 Metal Telluride Thin Films
### Samarium Telluride Films  
The samarium telluride (Sm\({}_{2}\)Te\({}_{3}\)) thin films were produced by (and could be used in) supercapacitor applications [130]. The obtained films showed orthorhombic phases, cloud-like morphology, lyophilic behavior, and contact angles of about 5.7\({}^{\circ}\). Based on the electrochemical measurements, the energy density and specific capacitance were 10 Wh/kg and 144 F/g, respectively. In the electrochemical impedance spectroscopy technique, negligible change in the resistive could be observed after 1000 cycles.
## 3 System Model
### Energy Model  
We adopt the same radio model to calculate the energy consumption which is mentioned in [46; 47]. The energy of transmitter \(E_{Tx}\) can be calculated by two different equations in accordance with their communication distance. Once the signal is generated by the transmitter, the amplifier will strengthen it using different power according to the transmission distance. Therefore, we adopt two different models for transmission. The free space model is adopted if the distance is less than a threshold value \(d_{0}\), otherwise we adopt multi-path fading model to calculate the energy consumption. The energy used for \(l\) bit data transmission through a distance \(d\) is given by follows:  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}l\cdot E_{elec}+l\cdot\varepsilon_{fs} \cdot d^{2}&if,d<d_{0}\\ l\cdot E_{elec}+l\cdot\varepsilon_{mp}\cdot d^{4}&if,d\geq d_{0}\end{array}\right. \tag{1}\]  
where \(E_{elec}\) denotes the power the transmitter and receiver use. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) denote two different power the amplifier uses.  
The threshold value \(d_{0}\) can be calculated using the following formula:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{2}\]  
Then the energy receiver uses to receive \(l\) bit data is calculated by  
\[E_{Rx}(l)=l\cdot E_{elec} \tag{3}\]
# Mechanical Design of a Bioinspired Compliant Robotic Wrist Rehabilitation Equipment
## 2 Biomechanics of the Hand Joints  
The construction of this type of rehabilitation equipment is based on detailed knowledge of the anatomy of the hand and of the motions its joints are capable of conducting. Further necessary data are the limits to such motions and the generated forces and moments. Figure 1 details the main bones and joints of the hand skeleton [19]:  
The lengths of the metacarpal bones and of the phalanves factor in significantly when conceiving rehabilitation equipment for the hand. Depending on gender, age, and body size the lengths of the bone segments differ, hence the dimensions of a system designed for  
Figure 1: Bones and joints of the hand.  
the rehabilitation of the hand can be established on grounds of statistical studies or based on atlases of human measurements.  
[PERSON] and [PERSON] present in [20] a statistical analysis based on measurement data of the hand bone lengths. They assert that the distances between the interphalangeal joints separating the distal, middle, proximal and metacarpal phalanxes, respectively, represent an 18; 26; 44; 70 Fibonacci sequence. Each number of such sequence is the sum of the preceding two (x\({}_{n}\) = x\({}_{n-1}\) + x\({}_{n-2}\)). Based on this finding Figure 2 shows the dimensions of the middle finger used as reference for the construction of the rehabilitation equipment discussed in the paper.  
Various studies related to the biomechanics of the hand joints highlight the essential motions to be reproduced by rehabilitation systems. These are the flexion/extension of the wrist (the radiocarbon joint) and of the joints of the fingers (Figure 3) [21; 22].  
The flexion of the palm is achieved by its downward rotation, starting from a zero position. The zero position is obtained when the forearm is in 90\({}^{\circ}\) flexion and in pronation. The maximum flexion angle of the palm can reach 80\({}^{\circ}\). The extension of the palm or dorsiflexion is obtained starting from the same zero position by its upward rotation. The maximum rotation angle can reach 70\({}^{\circ}\).  
Regarding the fingers, in zero position palm and fingers are stretched out. As in this case several joints are involved, corresponding to each phalanx, Table 1 summarizes the limits of the motions and also indicates the maximum speeds of displacement achievable by rehabilitation equipment currently available [21].  
Figure 3: Flexion/extension of the wrist and joints of the fingers.  
Figure 2: Distances between the interphalangeal joints of the middle finger.  
The data in the table above were taken into account at the conception of a new rehabilitation system of the hand joints. In addition to these targets were also a compact and lightweight construction as well as adaptive behavior to the pain supportability of the patient conducting rehabilitation exercises.
## 2 Existing ASs and the Related Market Design
### AS Resources in Distribution Grid
#### 2.2.1 Distributed Energy Resources  
Despite the advantages of traditional electrical energy systems, the shift toward distributed production resources is on the rise for the following reasons:  
* To address the growing demand for electrical energy, an increasing reliance on fossil fuels is required within conventional energy systems.
* The adoption of the \"consume where you produce\" concept (prosumer) is becoming imperative to meet the rising demand. This shift is prompted by issues such as existing systems lacking desired reliability and causing technical and economic losses during the transmission of produced energy to the consumption region.
* Many nations are opting for RES to reduce reliance on fossil fuels, which contribute to environmental pollution, in alignment with the Kyoto Protocol. The aim is to mitigate climate change and tackle global warming issues by decreasing greenhouse gas emissions.  
RES, including wind, hydro, solar, biomass, geothermal, and cogeneration energy systems, are favored over conventional production systems due to the latter's associated problems. It is anticipated that in the upcoming period, the prevalence of distributed generation plants, specifically those based on renewable sources like solar, river hydro, geothermal, and wind, will significantly increase.  
Moreover, as technologies advance, there is an anticipation for the development of MG systems utilizing storage systems. Microgrids can play a crucial role in supplying rural areas, especially those with long feeders in distribution regions, by:  
* Enhancing resistance to long-term interruptions (improving energy supply continuity).
* Offering benefits in terms of reducing distribution grid investment and operating costs within the region.  
Additionally, storage systems, as well as solar inverters, solar power plants, and certain wind power plants, can provide reactive power support. The growing integration of renewable production sources into the grid has the potential to offer ASs to the grid. In contrast to synchronous generators and voltage regulators, inverters associated with distributed generation systems and rooftop solar systems could potentially provide various services, such as harmonic reduction, voltage regulation, flicker control, reactive power compensation, and active power filtering. Wind turbines and variable-speed generators possess the capability to contribute significantly to the grid.
## 3 Results and Discussion
### Electrospinning of Recovered PLA
Materials containing HA (PLA/PEG/2.5 HA and PLA/PEG/5.0 HA) are also stiffer than those prepared without HA, possessing a Young modulus of 1.2 \(\pm\) 0.6 and 0.7 \(\pm\) 0.5 MPa, respectively. A decrease in the Young modulus is observed when the concentration of HA increases to 5.0 wt.%, which is explained on the basis of HA dispersion in the PLA-based fibers. Materials formulated with a low concentration of HA allow an adequate dispersion of HA in the polymeric fibrous matrix; thus, an efficient stress transfer is observed during tensile testing. On the contrary, a high load of HA could produce an aggregation of this component in the polymeric matrix, acting as weak points [43; 44]. Figure 4: Tensile stress–strain plot (**I**), and Young modulus for PLA-based fibers (**II**): (A) PLA10, (B) PLA/PEG, (C) PLA/PEG/2.5 HA, and (D) PLA/PEG/5.0 HA. Figure 3: SEM images (**I**) and size distribution of fiber diameter (**II**) of PLA-based electrospun fibers: (**A**) PLA10, (**B**) PLA/PEG, (**C**) PLA/PEG/2.5 HA, and (**D**) PLA/PEG/5.0 HA. Scale bar is 5 \(\upmu\)m.  
Both PEG and HA are well-known compounds that are frequently added to polymeric material formulations to produce hydrophilic and wettable materials [24; 45]. In Figure 5 is shown the contact angle of a water drop on the surface of the PLA electrospun mats. PLA is a hydrophobic polymer, producing a high contact angle of water measured in PLA10 material (128.8 \(\pm\) 2.2\({}^{\circ}\)). A small decrease of the contact angle (121.3 \(\pm\) 4.9\({}^{\circ}\)) is observed in PLA/PEG as compared with pristine PLA electrospun mats. The trend is also observed in PLA/PEG/HA mats, 118.7 \(\pm\) 2.1\({}^{\circ}\) and 116.2 \(\pm\) 3.0\({}^{\circ}\) for materials prepared with 2.5 and 5.0 wt.% HA, respectively. Although it is well-known that PEG is a hydrophilic polymer, the small decrease in the contact angle of PLA materials formulated with PEG is due to the low concentration of PEG used to prepare the fibrous materials.
# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 3 Our Approach
### Problem Definition  
In our recommendation module, it is assumed that there is a user set \(\mathbf{U}=\{u_{1},u_{2},\cdots,u_{M}\}\), and an item set \(\mathbf{V}=\{v_{1},v_{2},\cdots,v_{N}\}\), where \(M\) and \(N\) are the number of users and the number of items in the RS, respectively. The user-item interaction matrix \(\mathbf{Y}^{M\times N}=\{y_{uv}|u\in\mathbf{U},v\in\mathbf{V}\}\) is defined as follows:  
\[y_{uv}=\left\{\begin{array}{ll}1,&\text{user $u$ interacts with item $v$,}\\ 0,&\text{otherwise,}\end{array}\right. \tag{1}\]  
where \(y_{uv}=1\) indicates that user \(u\) has interacted with item \(v\), i.e., has purchased, browsed, clicked, etc. \(y_{uv}=0\) here, simply indicates that user \(u\) has not clicked on item \(v\). It does not indicate that the user does not like the item. It is also possible that the user has not seen the item or has unintentionally skipped it. In addition, we construct a KG \(\mathbf{G}=\{(h,r,t)|h,t\in E,r\in R\}\) containing a large number of triplets, where \(E=\{e_{1},e_{2},\cdots,e_{i}\}\) denotes a large number of entities in the KG and \(R=\{r_{1},r_{2},\cdots,r_{j}\}\) denotes a large number of connection relationships between entities in the KG. Any of the triplets \((h,r,t)\) in \(G\) consists of an entity-relationship-entity, representing the head entity \(h\), the tail entity \(t\), and the relationship \(r\) between them, respectively. For example, the triplet (_Fast & Furious 9_, _film_, _film_, _Star_, _[PERSON]_), indicates that _[PERSON]_ starred in the film _Fast & Furious 9_. Then, we use \(D=\{(v,e)|v\in\mathbf{V},e\in E\}\) to represent the one-to-one correspondence between items and entities in the KG, where \((v,e)\) denotes the correspondence between \(v\) and \(e\). In most recommended scenarios, an item often corresponds to only one entity.  
In short, after obtaining the implicit interaction matrix \(\mathbf{Y}^{M\times N}\), and the KG \(\mathbf{G}\), our main task is to build a prediction function to predict the possible interactions between users and their uninteracted items. The prediction function \(\mathcal{F}(\cdot)\), is defined as follows:  
\[\hat{y}_{uv}=\mathcal{F}(u,v|\Theta,\mathbf{G}\,), \tag{2}\]  
where \(\hat{y}_{uv}\) denotes the possibility that user \(u\) interacts with item \(v\), and \(\Theta\) denotes the argument of function \(\mathcal{F}(\cdot)\).