esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Calculations  
The LD dynamics with double OFB are studied numerically by integrating the rate Equations (1)-(3) using the fourth-order Runge Kutta method [27]. AlGaAs LD emitting at a wavelength of 0.780 \(\upmu\)m parameters was used in the calculations, where the numerical values of this LD are given in Ref. [12]. The corresponding threshold current \(I_{thD}\) = 22.5 mA, and the threshold gain \(G_{thD}\) = 2.82 \(\times\) 10\({}^{11}\) s\({}^{-1}\). In this article, a short external cavity has been used in the first external cavity \(L_{ex1}\) = 3 cm, whereas two lengths are considered to be the second external cavity of \(L_{ex2}\) = 3.5 \(\times\) 10\({}^{-2}\) m (the short cavity) and 10\({}^{-1}\) m (approaches to the long cavity). The refractive index of these external cavities is filled with air (\(n_{ex}\) = 1.0). The resonance frequency separations of the external cavities are \(f_{ex1}=1.0/\tau_{1}=5\) GHz and \(f_{ex2}=1.0/\tau_{2}=4.286\) GHz. The states are investigated in terms of the strength of the OFB coefficients (intensity) \(K_{ex1}\) and \(K_{ex2}\). The integration time step was selected as 5 ps and was used over a period of 5 \(\upmu\)s, for which the operation reached a steady state. The phase \(\varphi\) of the OFB is given using  
\[\varphi=\tan^{-1}\frac{\text{Im}U}{\text{Re}U}+p\pi \tag{11}\]  
where \(p\) is an integer and is changed continuously for time evolution since the solution of the arc tangent is defined in the range of \(-\pi/2.0\) to \(+\pi/2.0\) in the simulation.  
The values \(\overline{S}\) and \(\overline{N}\) of the photon and carrier number are specified from the steady-state solutions of the rate equation of the solitary laser [12]:  
\[\overline{S}=\frac{I-I_{thD}}{eG_{thD}} \tag{12}\]  
\[\overline{N}=\frac{N_{thD}+\frac{V}{a_{\overline{S}}}B\overline{S}}{\overline{ S}+1} \tag{13}\]  
where \(N_{thD}\) is the threshold carrier number.  
The spectra of the \(RIN\) are then calculated from the output photon number fluctuations \(\delta S(t)=S(t)-\overline{S}\), obtained instantaneously using fast Fourier transform [28; 29]:  
\[RIN=\frac{1}{\overline{S}^{2}}\left\{\frac{1}{T}\int\limits_{0}^{T}\left[\int \limits_{0}^{\infty}\delta S(t)\delta S(t+\tau)e^{i\omega\tau}d\tau\right]dt \right\}=\frac{1}{\overline{S}^{2}}\left\{\frac{1}{T}\left|\int\limits_{0}^{T} \delta S(\tau)e^{-i\omega\tau}d\tau\right|^{2}\right\} \tag{14}\]  
where \(\overline{S}\) is the dc-value of the photon number \(S(t)\), which is timely averaged and also numerically calculated. The \(RIN\) simulations were carried out as the steady state reached to \(t\approx\) 3-5 \(\upmu\)s.  
Figure 1: Scheme of laser diode under double optical feedback [12].
## 5 Comparative Analysis and Results
The owner of an identity can still obtain their private keys and login information in the event that they misplace or have their primary access device stolen. Protocols and systems do not have any secret goals. An identity network's operational, managerial, and update processes are transparent thanks to the systems used for them. The algorithms are well known and compatible with all types of architectures. Our scheme makes use of only open-source licenses for all software and standards, which can be downloaded for free from the internet. The non-profit self-governing trust framework is made up of standards, or volunteer specialists, in digital identity, privacy, and policy around the world. It is sometimes mentioned that having a portable identity is a requirement that needs to be met. We have designed our proposed identities for maximum usability. Global identities are made possible by our SSI system, enabling them to cross national boundaries and different system deployments. As a result of our identity systems' remarkable scalability, we are able to meet our users' growing needs. The number of end users or the availability of resources, for example, will not significantly affect the effectiveness of our SSI system. It is imperative that the user experience fulfils the expectations and demands of the user. Identity owners need a consistent user experience across various platforms and services. The question of how this will occur remains unanswered. Before the smart cryptographic tools of the identity system are made available to end users, they must be improved in terms of usability. It is crucial that services integrating with the virtual ecosystem and Metaverse developers focus on the user experience as they continue to improve their plans. We conducted a comparative analysis based on the principles outlined in the SSI evaluation framework; the results are shown in Table 1.  
It is clear that the proposed framework completely fulfils the requirements of all SSI evaluation framework requirements.
###### Abstract
We conduct comparative experiments on three RS image datasets: UCMD, SAT-4 and SAT-6. Extensive experimental results demonstrate that TOCEH generates highly concentrated and compact hash codes, and it outperforms some existing state-of-the-art hashing methods in large-scale RS image retrieval tasks. The rest of this paper is organized as follows. Section 2 introduces the proposed TOCEH algorithm. Section 2.1 shows the important notation. The hash learning problem is stated in Section 2.2. The tensor graph representing the triplet ordinal relation among RS images is introduced in Section 2.3. We provide the formulation of triplet ordinal cross entropy loss and triplet ordinal quantization loss in Sections 2.4 and 2.5, respectively. The extensive experimental evaluations are presented in Section 3. Finally, we set out a conclusion in Section 4. Figure 1: Flowchart of the proposed TOCEH algorithm. Firstly, to represent the image content, we use the Alexnet, including five convolutional (CONV) networks and two fully connected (FC) networks, to learn the continuous latent variable. Secondly, the triplet ordinal relation is computed by the tensor product of the similarity and dissimilarity graphs. Thirdly, two fully connected layers with the activation function of ReLU are utilized to generate the binary code. To guarantee the performance, we define the triplet ordinal cross entropy loss to minimize the inconsistency between the triplet ordinal relations in different spaces. Furthermore, we design the triplet ordinal quantization loss to reduce the loss caused by the relaxation mechanism.
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
## 3 Measurement
### Measurement Setup  
The reflection coefficients of the absorbers were measured using free space (planar wave) method [21] in a bistatic configuration. The setup is presented schematically Figure 8 and a picture is given in Figure 9. In order to be representative of the application (antenna on platform) the emitting antenna was static and directed at the device under test such that that sectors A and B were illuminated with the proper range of incidence angles. For each angle of interest, the receiving antenna was mounted at one meter from the corresponding reflection point and directed toward the specular angle, as represented in Figure 8.  
Figure 8: Bistatic measurement setup in anechoic chamber. Each sector was illuminated with the proper angle range. The Tx antenna was static while the Rx antenna was moved for each angle of interest.  
Figure 7: Pictures of the manufactured prototypes. **(a)** Two panels of sector A absorber. **(b)** FSS patterns of sector A and sector B. **(c)** Single panel dimensions.  
Measurements were performed in an anechoic chamber (Figure 9). Tx and Rx antennas were separated by absorbing foam to limit direct crosstalk. Additional foam was placed at the wedge of the sectorial absorbers to limit both the wedge diffraction and excitation of surface waves for oblique incidence. The measurements were performed using Keysight E5071C VNA and a pre-amplifier in reception. The transmitting antenna was the Hyptra horn antenna which has a gain of 11 dBi and a half power beam width (HPBW) of 40\({}^{\circ}\) and 50\({}^{\circ}\) for E and H planes at 2 GHz. The receiving antenna was the commercial SAS-571 horn antenna, which has a gain of 8 dBi at 2 GHz. The results were normalized with respect to a metallic plate of same size. In addition, time gating (Hann window) was applied to measured data in order to remove parasitic reflection from the setup.
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 3 Geometric Programming Problem: Basic Concepts
### Basic Concepts  
**Definition 1** (Monomial).: _The word \"monomial\" is derived from the Latin word mono, meaning only one, and mial solely means term. Therefore, a monomial literally means \"an expression in algebra having only one term\"._  
_Thus, if \(x_{1},x_{2},\ldots,x_{n}\) represent the \(n\) non-negative variable, then a real-valued function \(F\) of \(x\), in the following form_  
\[F(x)=cx_{1}^{a_{1}}x_{2}^{a_{2}}\cdots x_{n}^{a_{n}},\]  
_where \(c>0\) and \(a_{i}\in\mathbf{R}\), is known as the monomial function._  
_Illustrative Example 1: If \(a\), \(b\), and \(c\) are non-negative variables, then 6, 0.84, \(9a^{3}b^{-9}\), \(17\sqrt{c/a}\) are monomial, but \(5+a\), \(6a-8c\), and \(7(a+8a^{6}b^{-7})\) are not monomials._  
**Definition 2** (Polynomial).: _The word \"polynomial\" is also derived from the Latin word poly, meaning many, and mial solely means term. Therefore, a polynomial literally means \"an expression in algebra having many terms\", i.e., many monomials._  
_Suppose \(x_{1},x_{2},\ldots,x_{n}\) represent the \(n\) non-negative variable, then the sum of one or more monomials in the following form of a real-valued function \(F\) of \(x\):_  
\[F(x)=\sum_{i=1}^{m}c_{i}x_{1}^{a_{1i}}x_{2}^{a_{2i}}\cdots x_{n}^{a_{ni}},\]  
_where \(a_{ni}\in\mathbf{R}\) is known as a polynomial function or simply a polynomial._Illustrative Example 2: If \(a\), \(b\), and \(c\) are non-negative variables, then 6, 0.84, \(9a^{3}b^{-9}\), \(-5c/a\), \(6a-8c\), and \(7(a+8a^{6}b^{-7})\) are polynomials._  
**Definition 3** (Posynomial).: _If the coefficients \(c_{i}>0\) in the polynomial, then it is called a posynomial. Therefore, the sum of one or more monomials in the following form of a real-valued function \(F\) of \(x\):_  
\[F(x)=\sum_{i=1}^{m}c_{i}x_{1}^{a_{1i}}x_{2}^{a_{2i}}\cdots x_{n}^{a_{ni}},c_{i}>0\]  
_where \(c_{i}>0\) and \(a_{ni}\in\boldsymbol{R}\) is called a posynomial function or simply posynomial._  
Illustrative Example 3: If \(a\), \(b\), and \(c\) are non-negative variables, then 6, 0.84, \(9a^{3}b^{-9}\), \(17\sqrt{c/a}+a^{7}b^{4}\) are posynomial, but \(5-a\), \(6a-8c\), and \(7(a+8a^{6}b^{-7})\) are not posynomial._  
Note 1: The term posynomial is used to suggest a combination of positive and polynomial, that is **POSITIVE + POLYNOMIAL = POSYNOMIAL**.  
**Definition 4** (Degree of difficulty).: _This is defined as a quantity \((N-n-1)\) present in geometric programming called the degree of difficulty. In the case of a constrained geometric programming problem, \(N\) represents the total number of terms in all the posynomials and \(n\) represents the number of design variables._  
Note 2: The comparison and differences between monomial, polynomial, and posynomial are summarized in Table 1.
# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 2 Method
### New Loss Function  
Following are some examples of loss functions that are widely applied to medical image segmentation:  
\[\mathcal{L}_{FL} =-\frac{1}{N}\sum_{i=1}^{N}\sum_{a=1}^{A}\lambda\big{(}1-y_{pred} \big{)}^{\gamma}y_{true}\log(y_{pred}) \tag{19}\] \[\mathcal{L}_{DL} =\frac{1}{A}\sum_{a=1}^{A}\frac{1}{N}\bigg{(}1-\frac{\sum_{i=1}^{N }y_{true}y_{pred}}{N}y_{true}^{2}+\sum_{i=1}^{N}y_{pred}^{2}\bigg{)}\] (20) \[\mathcal{L}_{CE} =-\sum_{i=1}^{N}y_{true}\log y_{pred} \tag{21}\]  
where \(y_{true}\) represents the Ground Truth, \(y_{pred}\) represents the predicted segmentation result, \(N\) is the size of the training batch, and \(A\) is the number of categories separated. The FL loss balance factor is represented by \(\lambda\) while the FL loss exponential factor is represented by \(\gamma\).  
* Focal Loss (FL): Increasing the weight of difficult-to-classify samples in the loss function is the same as using the FL [39] loss function. As a result, the loss of the comparatively simple to anticipate sample is reduced while the loss of the comparatively hard to guess sample is bigger. Consequently, the emphasis is put more on challenging-to-classify samples.
* Dice Loss (DL): A metric function called Dice Loss [40] is used to assess how comparable two samples are. The value is between 0 and 1, and the higher it is, the more related the two are.
* Cross Entropy Loss (CE): CE [41] expresses the distribution relationship between the predicted value and the true value, and the smaller the cross entropy, the more similar the probability distribution between the two.  
Several experiments show that using only DL or CE on the improved RL-Unet model is not optimal for the segmentation results of the Synapse abdominal multi-organ segmentation dataset. Additionally, this paper also studies the segmentation of the RL-Unet model on the BraTS2021 dataset. When there is only backdrop and foreground, if several pixels of the little target are incorrect, Dice will vary visibly and have an impact on the training results. As a result, the prediction of the DL loss function for small targets is not reliable. Therefore, a novel loss function is designed in this paper, as shown in Equation (22), and Equation (23) is adjusted on the basis of the original loss function.  
Figure 4: Joint channel attention.  
Figure 5: Joint spatial attention.  
\[\mathcal{L}_{1} =\alpha\mathcal{L}_{CE}+\beta\mathcal{L}_{DL} \tag{22}\] \[\mathcal{L}_{2} =4\log\mathcal{L}_{DL}^{2}+\log\mathcal{L}_{FL}^{2} \tag{23}\]  
where \(\alpha\) and \(\beta\) are hyperparameters that can be fine-tuned and, here, we choose 0.3 and 0.7, respectively.
## 4 Results and Discussion
### Discussion
#### 4.5.3 Limitations and Future Work  
While AttenFlow demonstrates strong performance, several limitations remain. First, system effectiveness is sensitive to input quality--real-world deployments revealed that noisy OCR outputs can lead to a 6% drop in retrieval accuracy. Second, the graph construction methods used in AM-HDGAT, such as exact k-NN and \(\varepsilon\)-neighborhood graphs, become computationally expensive and memory-intensive when scaling to very large document corpora (e.g., 500K+ documents). The quadratic complexity in pairwise similarity calculations leads to increased latency and GPU memory consumption, posing practical bottlenecks for real-time or near-real-time applications. To address these challenges, future work will investigate scalable graph construction techniques, including approximate nearest neighbor (ANN) algorithms that reduce computational costs while preserving graph quality, and graph sparsification strategies to limit node degrees without sacrificing relational information. We also plan to develop dynamic graph update mechanisms to incrementally incorporate new documents and evolving inter-document relationships, avoiding costly full graph reconstructions. Furthermore, distributed and parallel graph processing frameworks will be explored to leverage multi-GPU clusters and cloud computing resources, enabling near-linear scalability. These efforts aim to maintain the integrityof relational modeling in AM-HDGAT while significantly improving its scalability and responsiveness for large-scale, evolving document ecosystems.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 4 Performance Evaluation
### Performance Evaluations of MPTCP Schedulers When the CCA Is wVegas  
Figure 10 shows the total data received by the MPTCP schedulers for wVegas as the CCA. Interestingly, significantly different from previous experiments, all the schedulers received almost the same amount of total received data in this case. As we explained previously, rather than focusing on the design goals of MPTCP, wVegas focuses on their proposed \"congestion equality principle\". This limits its functionality and unnecessarily restrains its CWND from growing at times, which is the key reason that it could not utilize the available network resources properly.  
Figure 11 shows recorded throughput and \(\Delta\mathit{PDU}\) received by the UE in real-time during the simulation. The observed SINR was the same as shown in Figure 3. Contrary to the previous experiments, schedulers using wVegas show a greatly reduced number of packet losses. The throughput is substantially low for both the SFs in all the schedulers. This low throughput is because of wVegas's dependency on the \"congestion equality principle.\" As it sends significantly less data, the network remains free. As a result, a small number of packet losses are observed.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Scheduler**} & \multicolumn{2}{c}{**Flow Completion Time (FCT)**} \\ \cline{2-3}  & **ms** \\ \hline Default (SRTT) & 11.284 \\ Round-robin & 11.333 \\ Redundant & 8.001 \\ ECF & 11.269 \\ BLEST & 6.012 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the tested MPTCP schedulers in terms of FCT for BALIA as a CCA.  
Figure 10: Performance of the tested MPTCP schedulers in terms of total received data for wVegas as a CCA.  
Finally, Table 5 summarizes the FCT obtained by considered schedulers for wVegas. Similar to the low throughput results, measured FCTs are also very high for wVegas. The FCTs of all the schedulers are almost the same as each other. This indicates that the CCA, which is wVegas, dominates in this experiment. The results are due to wVegas's dependency on the \"congestion equality principle,\" which restrains it from utilizing the available bandwidth.  
Figure 11: Real-time performance of the tested MPTCP schedulers for wVegas as a CCA in terms of throughput and \(\Delta\)_PDU_ for: (**a,b**) SRTT, (**c,d**) Round-robin, (**e,f**) Redundant, (**g,h**) ECF, and (**i,j**) BLEST, respectively.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 5 Discussion
### Impact of the Corona Shape  
Finally, it is worth noting that the proposed model has been tuned based on the measurements, where triangle-like corona shapes were present. This means that this model is well adjusted to the specific tree shape, i.e., trees with a triangle-like shape of the corona. One may foresee that the measurement with other corona shapes (or with trees with different shapes at all) may result in different tuning parameters. In consequence, the model discussed in this manuscript can generalize only to some extent, i.e., one has to remember that the tuning coefficient may be different when other corona shapes are considered. The application of the proposed model could be treated as some sort of coarse approximation of the foliage attenuation for the FWA scheme. In principle, fine-tuned models could be proposed for different corona types. However, this is a space for interesting new investigations in that domain. Moreover, while referring directly to the electromagnetic scattering theorem, in our work, we made the assumption that the Gaussian phase function is considered for random scatterers. In fact, more research is required to specify the exact phase function for specific corona tree shapes. It is another emerging area of research where we foresee that physics-based machine learning could be used. However, one also has to remember that the other contribution made in this paper is the RTE analysis, where the results do not depend on the shape at all.
## 5 Application Examples
### Robotic Manufacturing System  
In this example, a manufacturing system composed of two robotic cells is considered. The objective is to show the effectiveness of \(RCPetri\) for the implementation of a _decentralized control scheme_ using the OPC UA protocol. For this, the Plant is simulated in the PLC training platform FACTORY I/O (see Figure 13), and two controllers are simulated in different app instances.  
Each robotic cell is composed of a raw parts emitter (_Lemit_ and _Bemit_), an input conveyor belt (_Lrc_ and _Brc_), a robotic arm (_Ls_ and _Bs_), a CNC machine (_Lpro_ and _Bpro_), and two output conveyor belts (_Lec\({}_{1}\)_, _Lec\({}_{2}\)_, _Bec\({}_{1}\)_ and _Bec\({}_{2}\)_). In addition, both cells share an output conveyor belt _Ec_ and a manufactured parts remover _Rem_. Each robotic arm has a predefined program, which consists of moving a part from the entry to the CNC machine, wait for processing, and then moving the part to the exit. Each CNC machine can manufacture either bases or lids. Four sensors, \(Len_{1}\), \(Len_{2}\), \(Ben_{1}\), and \(Ben_{2}\), indicate the presence of raw parts at different positions on the input conveyors, while the sensors \(Lex_{1},\ldots,Lex_{6}\), \(Bex_{1},\ldots,Bex_{6}\) indicate the presence of manufactured parts on the output conveyors (Some sensors send a 1 as a logic signal when no object is present (sending a 0  
Figure 13: Simulation and control of the robotic manufacturing system. Controller A drives the lids production (left cell), while Controller B drives the bases production (right cell). Each control program is running in a different instance.  
in the opposite case), this can be handled in the app by using negation symbols \(\
eg\) in the output labels (e.g., \(\
eg Lex_{1}\))).  
The Plant was modeled according to the methodology of [19]. Output labels were added to places representing actuator states, describing virtual sensors that allow the computation of controllable sequences. The system was required to perform lids production in the left cell and bases production in the right cell. This desired behavior was translated into two different \(IPN\) Specifications, one Specification for the production of lids and the other for the production of bases. Later, each corresponding \(IPN\) Controller was synthesized, obtaining a _decentralized control scheme_, as explained in [19].  
Figure 13 displays the control simulation. Here, the Plant under the two synthesized control programs is running in FACTORY I/O. Both control programs are executed simultaneously, each one running in a different \(RCP Petri\) instance, synchronized through the OPC UA protocol. In this case, the Prosys OPC UA Simulation Server platform was used. This communication architecture made possible to effectively implement a _decentralized control scheme_.