esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Neural Network Solution
### Proposed Dataset Compilation and Network Selection
In each iteration, \(r\), a different non-linear map in the neighborhood of ICF or SCF is learned by training a new candidate SNN-CF network, \(Net^{(r)}\). Upon training, the performance of \(Net^{(r)}\) is measured by evaluating \(\mathcal{F}^{(r)}\triangleq Pr\{\Omega>\Omega_{\text{0}d}\}\), the complementary cumulative distribution function (CCDF) of the OFDM signal generated by \(Net^{(r)}\). As the iterations advance, the algorithm keeps track of \(Net_{b}\), the SNN-CF network providing the best CCDF value, \(\mathcal{F}_{b}\). The best-performing network is thus updated and saved as the algorithm progresses. Finally, the algorithm outputs the best-performing SNN-CF network found when it stops after the last iteration. ```
Initialize \(r=0,\mathcal{F}_{b}=1\)  Set \(\Omega_{\text{0}d}\), \(E_{b}/N_{\text{0}|}\), \(\mathcal{E}_{t}\), \(\mathcal{R}_{max}\)  While \(r<\mathcal{R}_{max}\) repeat the following steps: 1. Initialize a random SNN-CF network, \(Net^{(r)}\), with parameter set \(\phi\) and apply a hybrid batch of random samples \(\left(d_{m}^{(i)}\), \(d_{m}^{(o)}\right)\) from \(\mathcal{D}\) to update \(\phi\). 2. Compute \(\mathcal{F}^{(r)}\), the CCDF of the OFDM signal generated by \(Net^{(r)}\), at the design threshold \(\Omega_{\text{0}d}\) 3. Compute \(E^{(r)}\), the achievable BER of \(Net^{(r)}\), at the design target \(E_{b}/N_{\text{0}|}t\) 4. Update the best CCDF, \(\mathcal{F}_{b}\), and save its corresponding SNN-CF network, \(Net_{b}\): \[\mathcal{F}_{b}=\left\{ \begin{array}{ll}\mathcal{F}^{(r)}&\mathcal{F}^{(r)}< \mathcal{F}_{b}\\ &E^{(r)}<\mathcal{E}_{t}\\ \mathcal{F}_{b}&\text{Otherwise}\end{array} \right.\] \[Net_{b}=\left\{ \begin{array}{ll}Net^{(r)}&\mathcal{F}^{(r)}< \mathcal{F}_{b}\\ &E^{(r)}<\mathcal{E}_{t}\\ &Otherwise\end{array} \right.\] 5. \(r\gets r+1\)  end While Where * \(\mathcal{R}_{max}\) is the total number of scanned mappings in the neighborhood of conventional SCF or ICF. * \(\left(d_{m}^{(i)}\), \(d_{m}^{(o)}\right)\triangleq\left(\mathcal{D}(s,j,1)\), \(\mathcal{D}(s,j,2)\right)\) is an input-output training data point sampled from \(\mathcal{D}\) at a random entry \(j\) within a random slice \(s\). * \(\mathcal{E}_{t}\) is the maximum allowable BER at the arbitrary input design target \(E_{b}/N_{\text{0}|}t\). ```  
**Algorithm 2**Proposed SNN-CF Network Selection.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)
### Objective Function
#### 3.2.1 Economic Benefits of ADN  
The minimum annual comprehensive cost of ADN is set as the objective function, which is expressed as follows:  
\[\text{min}C_{1}=C^{\text{OPE}}+C^{\text{INV}} \tag{1}\]  
where \(C^{\text{OPE}}\) is the annual operation cost of ADN and \(C^{\text{INV}}\) is the investment cost of DES, which are formulated as follows:  
\[C^{\text{OPE}}=\sum\
olimits_{s\in\Omega_{\text{S}}}\Bigl{(}\sum\
olimits_{t \in\text{N}_{\text{T}}}\lambda_{t}P_{s,t}^{\text{SUB}}\Delta t\Bigr{)}p_{s} \tag{2}\]  
\[C^{\text{INV}}=\frac{d(1+d)^{y_{m}}}{(1+d)^{y_{m}}-1}\sum\
olimits_{m\in \Omega_{\text{type}}}\sum\
olimits_{s\in N_{\text{N}}}\Bigl{(}C^{\text{POW}}_ {m}y_{i,m}S^{\text{unit}}_{m}+C^{\text{ENE}}_{m}z_{i,m}E^{\text{unit}}_{m} \Bigr{)} \tag{3}\]  
where \(\Omega_{\text{S}}\) is the set of scenarios and \(\Omega_{\text{type}}\) is the set of DES types. \(N_{\text{T}}\) is the total periods of the time horizon. \(N_{\text{N}}\) is the total number of the nodes. \(C^{\text{POW}}_{m}\) and \(C^{\text{ENE}}_{m}\) are the capital cost for 1 kW power capacity and 1 kWh energy capacity of the \(m\)th DES. \(S^{\text{unit}}_{m}\) and \(E^{\text{unit}}_{m}\) are the unit power capacity and energy capacity of the \(m\)th DES. \(y_{i,m}\) and \(z_{i,m}\) are the total number of power units and energy units of the \(m\)th DES at node \(i\). \(P_{s,t}^{\text{SUB}}\) is the active power at the substation in the \(s\)th scenario. \(\Delta t\) is the time interval, \(\lambda_{t}\) is the time-of-use electricity price, \(p_{s}\) is the probability of the \(s\)th scenario, \(d\) is the discount rate of DES, and \(y_{m}\) is the lifetime of the \(m\)th DES.  
The annual operation cost of an ADN is represented by the cost of total energy purchasing from the external grid under the electricity time-of-use tariff. The benefits brought by DES in power loss reduction can be reflected in the cost of energy purchasing from the external grid. The investment cost includes the cost of energy storage and power inverter.  
As the power source of the distribution network, the substation node can be conveniently equipped with DES to optimize the power flow between the distribution network and the external bulk power grid. However, in this paper we mainly focus on the optimization within the distribution network by the optimal planning of different DESs. For this application, the effectivity of DES that installed at the source node is limited. Therefore, all the nodes except the substation node are considered as candidate locations for DES planning in this paper.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
###### Abstract  
Scene classification in remote sensing is a pivotal research area, traditionally relying on visual information from aerial images for labeling. The introduction of ground environment audio as a novel geospatial data source adds valuable information for scene classification. However, bridging the structural gap between aerial images and ground environment audio is challenging, rendering popular two-branch networks ineffective for direct data fusion. To address this issue, the study in this research presents the Two-stage Fusion-based Audiovisual Classification Network (TFAVCNet). TFAVCNet leverages both audio and visual modules to extract deep semantic features from ground environmental audio and remote sensing images, respectively. The audiovisual fusion module combines and fuses information from both modalities at the feature and decision levels, facilitating joint training and yielding a more-robust solution. The proposed method outperforms existing approaches, as demonstrated by the experimental results on the ADVANCE dataset for remote sensing audiovisual scene classification, offering an innovative approach to enhanced scene classification.  
remote sensing; scene classification; transformer; feature fusion +
Footnote †: journal: Medical Imaging  
0000-0001-6999-2233-11890
# Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials
## Materials and Methods
### Procedures  
We made 2.0 kg of TBAAc aqueous solution by mixing the above reagents and put them in the container. The entire system was confirmed to be stationary at 298 K. \(T_{\text{in}}\) (outlet of the chiller) and flow rate, \(\dot{V}_{\text{av}}\), of the cooling water were controlled at 291 K and 5.0 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/min. Then, we started the thermal energy storage with TBAAc hydrate. We set t = 0 when we started the circulation of the cooling water. The time evolution of \(T_{\text{in}}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Chemical Formula** & **Supplier** & **Purity** \\ \hline Tetrabuthylammonium & (CH\({}_{3}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\))\({}_{4}\)NOH & Sigma-Aldrich Co. LLC & 0.40 mass fraction in aqueous \\  & & & solution \\ \hline Acrylic acid & CH\({}_{2}\)=CHCOOH & Sigma-Aldrich Co. LLC & 0.99 mass fraction in liquid \\  & & & reagent \\ \hline Tetrabuthylammonium & (CH\({}_{3}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\))\({}_{4}\)NOOCHC=CH\({}_{2}\) & Laboratory made from above & 0.36 mass fraction in aqueous \\  & & & solution after the \\  & & & neutralization process \\  & & & The standard uncertainty of \\  & & & mass fraction was \(\pm\) 1.0\(\times\)10\({}^{-4}\) \\ \hline Water & H\({}_{2}\)O & Laboratory made & Electrical conductivity was \\  & & & less than 0.1 \(\mu\)S/cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the reagents used in this study.  
\(T_{\text{out}}\) (temperature at inlet of the chiller) and \(T_{\text{PCM}}\) were measured every 10 s to evaluate the kinetic aspect of TBAAc hydrate as a thermal energy storage medium.  
We performed such experiments in three systems. First, we did not add any external forces to the apparatus. Second, we used a rotating-impeller agitator to break adhesions between the hydrate and the coil. Third, we used the ultrasonic wave instead of mechanical agitation. In the system where the agitator was used, we also investigated the effect of rotation rate of the agitator by changing the rate to 100 rpm, 300 rpm and 600 rpm. Similarly, we investigated the effect of the frequency of ultrasonic waves on the system by changing the frequency. The frequency was set at either 28 kHz or 56 kHz.
## 2 Test Setup and Simulation Environment
### Technologies and Technology-Specific Parameters  
The cellular network consisted of 4 base stations (BSs) in the case of the fixed infrastructure density scenario or 8 BSs in the case of the varying infrastructure density scenario, a RSU at the intersection connected wirelessly to the BS No.3 and cellular-enabled vehicles following the route depicted in Figure 1. The BS represents a single eNodeB in the case of the 4G-based communication technology or a single gNodeB in the case of the 5G-based communication technology. To limit the probability of poor resource scheduling due to an insufficient signal coverage, cells have been spaced uniformly with a spacing of 800 m between two adjacent BSs (the fixed infrastructure density scenario) and 400 metres in the case of the varying infrastructure density scenario (8 BSs deployment). The locations of the BSs were randomly selected. The technology-specific parameters used within the simulation experiments are listed in Table 3. The corresponding parameters were set up according to these standardization documents [33; 34]. Parameters, which are not explicitly mentioned in the corresponding table, were set to their default values according to the documentation of the respective simulation framework.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Values Considered in Simulations** \\ \hline Intensity of traffic & 250, 500, 750, 1000, 1250 and 1500 vehicles per hour \\ Frequency of message generation & 2, 4, 6, 8 and 10 messages per second (Hz), generated \\ Perimeter of communication & 200, 400, 600, 800, 1000, 1200 and 1400 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables deployed in simulations.
## 7 Key Dependency  
It is important to note that cryptographic keys are interdependent. In order for a certain protocol to be executed, nodes should have the appropriate keys. In this section we recapitulate the dependency between the keys for each of our protocols.  
Table 2 presents the dependency between keys for each of our protocols. The public/private keys of nodes are used to compute the shared symmetric keys using Diffie-Hellman without interaction described in Section 3. These shared keys are then used to deliver public keys in the case of protocols \(IJS\) and \(MKE_{S}\), or to deliver the pre-computed Diffie-Hellman key in the case of protocol \(MKE_{S}-light\). In protocols \(MKE_{T}-a\), \(MKE_{T}-b\), \(MKE_{T}-c\)and \(MKE_{T}-d\), the public keys of \(I\) and \(R\) are delivered using the session keys between these nodes and \(T\). For example, in protocol \(MKE_{T}-a\), the key \(pk(R)\) is encrypted by \(T\) using the key \(K(I,T)\) in order to deliver it to \(I\), which is denoted by \(K(I,T)\stackrel{{ T:I}}{{\longrightarrow}}pk(R)\). Similarly, in this protocol \(pk(I)\) is encrypted by \(T\) using the key \(K(R,T)\) in order to deliver it to \(R\), which is denoted by \(K(R,T)\stackrel{{ T:R}}{{\longrightarrow}}pk(I)\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol Name** & **Delivering** \\ \hline \(DJS\) & \(pk(I)\stackrel{{ S:I}}{{\longrightarrow}}K(I,S)\) \\ \hline \(IJS\) & \(K_{DH}(N,S)\stackrel{{ S:N}}{{\longrightarrow}}pk(I)\stackrel{{ N:I}}{{\longrightarrow}}K(I,N)\) \\ \hline \(MKE_{S}\) & \(K_{DH}(I,S)\stackrel{{ S:I}}{{\longrightarrow}}pk(R)\) \\ \(K_{DH}(R,S)\stackrel{{ S:R}}{{\longrightarrow}}pk(I)\) \\ \hline \(MKE_{S}-light\) & \(K_{DH}(I,S)\stackrel{{ S:I}}{{\longrightarrow}}K_{DH}(I,R)\) \\ \(K_{DH}(R,S)\stackrel{{ S:R}}{{\longrightarrow}}K_{DH}(I,R)\) \\ \hline \(MKE_{T}-a\) & \(K(I,T)\stackrel{{ T:I}}{{\longrightarrow}}pk(R)\) \\ \(K(R,T)\stackrel{{ T:R}}{{\longrightarrow}}pk(I)\) \\ \hline \(MKE_{T}-b\) & \(K(I,T)\stackrel{{ T:I}}{{\longrightarrow}}pk(R)\) \\ \(K(R,T)\stackrel{{ T:R}}{{\longrightarrow}}pk(I)\) \\ \hline \(MKE_{T}-c\) & \(K(R,T)\stackrel{{ R:T}}{{\longrightarrow}}pk(R)\) \\ \(K(I,T)\stackrel{{ T:I}}{{\longrightarrow}}pk(R)\) \\ \hline \(MKE_{T}-d\) & \(K(I,T)\stackrel{{ T:T}}{{\longrightarrow}}pk(I)\) \\ \(MKE_{T}-d\) & \(K(R,T)\stackrel{{ T:R}}{{\longrightarrow}}pk(I)\) \\ \(K(R,T)\stackrel{{ R:T}}{{\longrightarrow}}pk(R)\) \\ \(K(I,T)\stackrel{{ T:I}}{{\longrightarrow}}pk(R)\) \\ \hline \end{tabular}
\end{table}
Table 2: Key dependency in our protocols. \(K_{1}\stackrel{{ A:B}}{{\longrightarrow}}K_{2}\) denotes that \(K_{2}\) is delivered by \(A\) to \(B\) and encrypted with \(K_{1}\).
# Numerical Study of Viscoplastic Flows Using a Multigrid Initialization Algorithm
## 3 Numerical Results  
All the numerical steady-state solutions presented in this section are obtained on Cartesian grids consisting of square control volumes. The Papanastasiou model is employed for the evaluation of viscosity and the stress growth parameter \((m)\) is fixed to 200, a sufficiently high value that allows the ideal Bingham behavior to be approximated with satisfactory accuracy. The approximation of the convection terms in the momentum equations is carried out using the Quadratic Upwind Interpolation for the Convective Kinematics (QUICK) scheme.  
In the present study, Reynolds number \((Re)\) and Bingham number \((Bn)\) are taken as independent parameters. These two numbers are defined as follows:  
\[Re=\frac{\rho UL}{\mu_{p}} \tag{43}\]  
\[Bn=\frac{\tau_{y}L}{\mu_{p}U} \tag{44}\]  
with zero yield stress \((Bn=0)\) in case of Newtonian fluid. The Bingham number standing for the ratio of yield stress to viscous force is a crucial dimensionless number to describe viscoplastic materials.
## 4 Proposed Model
### Merged Similarity Layer Architecture  
The merged similarity layer architecture is shown in Figure 3. This architecture measures the distance between feature vectors using multiple similarity distance methods, and can effectively improve the accuracy of model classification for similar samples. Firstly, pairs of images are processed through two identical feature extraction layers--a residual VGG, which outputs two low-dimensional feature vectors, \(a\) and \(b\). Secondly, the Euclidean and Manhattan distances are used to measure the distance between the two feature vectors, calculating the similarity between the samples, as shown in Equations (7) and (8). Finally, the sigmoid activation function is used to output a similarity probability value between 0 and 1, to determine the similarity of the input samples. It also combines few-shot learning testing methods to classify the true labels of the test samples.  
The Euclidean distance \(ED_{w}\) is shown in Equation (7):  
\[ED_{w}(a,b)=\sqrt{\sum_{i=1}^{r}(a_{i}-b_{i})^{2}} \tag{7}\]  
where \(a\) and \(b\) are the outputs obtained from inputting two images separately into the feature extraction layers of the residual VGG, resulting in low-dimensional feature vectors. The Manhattan distance \(MD_{w}\) is shown in Equation (8):  
Figure 3: Residual VGG-based Siamese network model architecture.  
\[MD_{w}(a,b)=\sum_{i=1}^{r}|a_{i}-b_{i}| \tag{8}\]
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### Multi-Generation Individual Ensemble Strategy  
To avoid the risk of overfitting that might be introduced by a single GP program individual, a multi-generation individual ensemble strategy is proposed. Specifically, starting from the 10 th generation, the best GP program of each generation is continually saved. Upon completion of the GP algorithm iteration, the collection comprising the best GP program throughout the entire algorithm process and the best GP program of each generation is returned, denoted as \(I_{b}=\{i_{1}^{b},i_{2}^{b},\dots,i_{m}^{b}\}\) where \(i_{n}\) represents the best GP program generated by the n-th generation. Then, based on the function set defined in Section 3.2.1, the frequency of different functions contained in each GP program in \(I_{b}\) is calculated and marked as \(Fi\). Based on these frequencies, the difference between the GP programs in the collection \(I_{b}\) and the algorithm's best GP program is calculated, as shown in Equation (6),  
\[D_{i}=|F_{\text{best}}-F_{i}| \tag{6}\]  
where \(F_{\text{best}}\) denotes the function frequency of the algorithm's best GP program, and \(F_{i}\) represents the function frequency of each GP program in the set \(I_{b}\). \(D_{i}\) is defined as the difference in function frequency between the best GP program and each of the best GP programs of every generation. The three GP programs with the highest \(D_{i}\) values, along with the two best GP programs, are then effectively combined into an ensemble GP program. This process can be explained with the following example: assume that the predictive class labels obtained from the three GP programs with the highest \(D_{i}\) values are 001, and from the two best GP programs are 11. These inputs are combined to form the final output 00111. A majority vote based on the outputs generates the predicted class label, with the final output in this example being 1.
## 4 Experimental Results
### Implementation Details  
All of the experiments were carried out on MATLAB R2021a in Intel Core i7-10700F 2.90 GHz processor, RAM 64.0 GB, GPU NVIDIA RTX 3090. In this experiment, 50 images of each defect were randomly selected as the training data, and the remaining images served as the testing data. Notice that the image augmentation (IA) techniques were adopted herein to improve the performance of the proposed method under data-limited scenarios. Based on some experimental results, image reflection operation was heuristically selected only as the image augmentation technique to improve the training progress. Specifically, the training samples were randomly reflected horizontally or vertically with 50% probability. The models were trained for 300 epochs with an initial learning rate of 0.0001, and the mini-batch sizes were set as 300. All of the experiments were optimized by the [PERSON] [45] algorithm and were repeated ten times to obtain reliable results.
## 3 Results
### Morphological Traits
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variables** & **Stand (S) F (P)** & **Disease Degree (D) F (P)** & **S \(\times\) D F (P)** & _Post hoc_ **Stand** \\ \hline DBH & 12.80 (\textless{}0.001) & **13.60 (0.001)** & 0.61 (0.549) & P1 = C2 \(\leq\) C1 \\ \hline LMF & 0.25 (0.779) & **30.00 (\textless{}0.001)** & 0.20 (0.821) & nd \\ \hline Terminal buds & 1.31 (0.284) & **13.62 (0.001)** & 0.60 (0.554) & nd \\ \hline Dry needles & 1.12 (0.340) & **6.21 (0.018)** & 0.85 (0.439) & nd \\ \hline WP\({}_{\text{pd}}\) & T\({}_{1}\): **5.09 (0.012)** & 0.52 (0.477) & 0.58 (0.567) & C2 \(\leq\) P1 \(\leq\) C1 \\  & T\({}_{2}\): **10.71 (\textless{}0.001)** & 0.05 (0.824) & 1.84 (0.190) & C2 \(\leq\) P1 \(\leq\)C1 \\ \hline Fv/Fm & T\({}_{1}\): **12.54 (\textless{}0.001)** & 0.85 (0.362) & 0.03 (0.970) & C2 \(<\) C1, P1 \\  & T\({}_{2}\): 3.03 (0.076) & 0.39 (0.540) & 0.87 (0.438) & nd \\ \hline Starch & _3.04 (0.076)_ & 2.47 (0.136) & 0.64 (0.542) & nd \\ Soluble sugars & **4.88 (0.022)** & **18.69 (0.001)** & 2.02 (0.166) & nd \\ Total NSC & **5.73 (0.013)** & **19.18 (\textless{}0.001)** & 2.20 (0.143) & C1 \(\leq\) P1\(\leq\) C2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary statistics of Linear Model for disease degree (NS and S) at different pine stands (C1, C2, and P1). Significant differences at \(p<0.05\) are shown in bold and the marginal significant differences in italics. Acronym “nd” represents no differences (i.e., \(p>0.05\)) in the post hoc test between stands, “S” refers to stand factor and “D” refers to disease degree factor.
# How to Isolate Non-Public Networks in B5G: A Review
## 4 RAN Isolation
With these methods, a customized configuration for the RAN protocol can be achieved, as shown in Figure 3.  
The computing resources shared among the RAN software need to be virtualized with the aim that the computing tasks of the NPNs' RANs can proceed within their lifetimes. Accelerated computing resources are focused on since most computing tasks for RAN are delay-sensitive. Although general computing resources are also used to process the delay-insensitive tasks of a RAN, its isolation will be considered in Section 5 on CN isolation, which is also applicable to RANs. Generally speaking, accelerated computing resources can be a digital signal processor (DSP) or a field programmable gate array (FPGA). The virtualization of a DSP or FPGA is usually realized by a scheduling scheme designed according to the computing tasks' execution time and lifetimes. To achieve this, the scheduling scheme can be modeled as a directed acyclic graph (DAG), where the computing tasks of RANs can be modeled as the nodes of the DAG, called task nodes, as shown in Figure 4. For the task nodes, lifetimes can be obtained according to the scheduling periods of the wireless subframe, and execution time can be estimated using their software algorithms [15]. Then, the task nodes of the same RAN need to be connected in order since they are usually serial tasks. In accordance with the DAGs, a scheduling list can be obtained to try to calculate the tasks within their lifetimes. To this end, the system will decide whether the accelerated computing resources of the RAN are shared with other NPNs' RANs or not, in accordance with the scheduling list. If the time-sensitive tasks can be processed within their lifetimes, the accelerated computing resource can be shared with the RANs of other NPNs; otherwise, the sharing will be forbidden. Then, more private or sharing infrastructures need to be deployed and turned on to serve the NPNs' RANs. If there are no more infrastructures, the system needs to reject the request to serve some  
Figure 3: Protocol isolation for RAN. of the NPNs until all the tasks of the RANs can be processed within their lifetime. The advantages, challenges and solutions for RAN isolation are summarized in Table 2.