esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Geographical Adaptive Fidelity (GAF)
### GAF state transitions  
In the GAF, nodes are in one of three states: _sleeping, discovery, active_. A state transition diagram is shown in Figure 2.  
Initially nodes start out in the discovery state, in which a node turns on its radio and exchanges discovery message to find other nodes within the same cell. The discovery message is a tuple of node ID, cell ID, _estimated node active time_ (_enat_), and node state. As described above, a node uses its location and cell size to determine the cell ID.  
When a node enters discovery state, it sets a timer for \(T_{d}\) seconds. When the timer fires, the node broadcasts its discovery message and enters state active. The timer can also be suppressed by other discovery messages. This timer reduces the probability of discovery message collision. When a node  
Figure 1: Example of virtual grid in GAF.  
Figure 2: State transitions in GAF.  
enters active, it sets a timeout value \(T_{a}\) to define how long this node can stay in active state. After \(T_{a}\), the node will return to the discovery state. While active, the node periodically re-broadcast its discovery messages at an interval of \(T_{d}\).  
A node in discovery or active states can change state to sleeping when it can determine some other equivalent nodes will handle routing. When transitioning to sleeping, a node cancels all pending timers and powers down its radio. A node in the sleeping state wakes up after an application-dependent sleep time \(T_{S}\) and transitions back to discovery.  
The GAF leaves choices of many parameters including _enat_, \(T_{d}\), \(T_{a}\), node tank, \(T_{s}\) to application. Applications may wish to optimize these choices, for example, perhaps trading increased packet loss for greater energy savings. _enat_ can be set to the expected node lifetime, conservatively set by assuming the node will constantly consume energy at a maximum rate until it dies. GAF chose \(T_{d}\) as a uniform random value between 0 and some constant. This approach avoids contention from synchronized discovery message. The GAF uses \(T_{a}\) to accomplish load balancing. _Node ranking_ in GAF is chosen to maximize network lifetime by selecting which nodes handle routing. Rank is determined by several rules. For example, nodes with longer expected lifetime higher rank. GAF employs a load balancing strategy so that all nodes remain up and running together for as long as possible.
###### Abstract
This increases the economic benefits, as the energy exchanged by the INP and activated by the CBRR is additionally financially compensated [19]. A basic schematic diagram of the LFC, INP, and CBRR is given in Figure 1 (left), where the order of the operation is clearly seen. Note here that the correction power is the output of the INP | CBRR block. The main distinction among the INP and the CBRR is in the requirements to compensate for the imbalances among the cooperating CAs. The aim of the INP is to avert the simultaneous activation of RRs with different signs in cooperating CAs, i.e., to net the demand for balancing energy between CAs with different signs of demand power. In contrast, the aim of CBRR is to activate the demand for balancing energy in cooperating CAs with matching signs of demand power. The INP and the CBRR link all the CAs to a joint portal of virtual tie-lines where the INP or CBRR optimization is performed. Note that a virtual tie-line means an additional input of the controllers of the cooperating CAs that has the same effect as a measuring value of a physical interconnector and allows exchange of electric energy between the cooperating CAs [1]. The main objectives of the INP optimization are given in [20; 21], whereas the main objectives of the CBRR optimization are given in [22; 23]. There has been a surge in the application of machine learning and statistical framework to solve similar problems focused in this paper. The authors in [24] explore the influencing  
Figure 1: Schematic diagram of the LFC, INP, CBRR (**left**) and the function for correction power adjustment (**right**). factors of consumer purchase intention of cross-border e-commerce based on a wireless network and machine learning in order to provide decision support for the operation of e-commerce and to promote the better development of cross-border e-commerce. Several model-based experimental design techniques have been developed for design in domains with partial available data about the underlying process. The authors in [25] focus on a powerful class of model-based experimental design called the mean objective cost of uncertainty. To achieve a scalable objective-based experimental design, a graph-based mean objective cost of uncertainty with Bayesian optimization framework is proposed. A thorough review of the issues of data localization and data residency is given in [26], in addition to clarifying cross-border data flow restrictions and the impact of cross-border data flows in Asia.
# A Stronger Adaptive Local Dimming Method with Details Preservation
## 1 Introduction
However, the method lacks effectiveness in details preservation as it simply adopts the image mean  
Figure 1: The flowchart of local dimming technique. The dotted line means optional. Figure 2: The diagram of backlight extraction: (**a**) backlight panel; (**b**) Liquid Crystal (LC) panel; (**c**) a Back-Light Unit (BLU); and (**d**) the corresponding image block of (**c**). luminance as the breakpoint for segmentation. To this end, we improve the method from the view of taking more image details in breakpoint selection. We make the IBHE a part of pixel compensation. Combing the proposed backlight extraction with pixel compensation methods, a stronger adaptive local dimming method with details preservation is proposed. Our contributions can be summarized as:  
* A backlight extraction method is proposed to acquire a stronger adaption in processing images with diverse contents by combining advantages of several existing methods. * An IBHE method with Retinex theory is proposed to enhance image quality by preserving abundant details. * A pixel compensation method is proposed to alleviate overcompensation by leveraging information of both the extracted backlight and the original image based on IBHE. Experimental results demonstrate the effect of the proposed approach in improving contrast ratio, preserving image details, and enhancing image quality in the real display. The rest of the paper is structured as follows. Section 2 details the related work of local dimming. Section 3 documents the specific process of the proposed method elaborately. Section 4 describes the experiments and results, and analysis of the results is made in this section. Finally, we conclude the paper in Section 5.
# SCoPE: Service Composition and Personalization Environment
## 5 ECE Environment
### Step: 2--Adherence Calculation
Let us exemplify the Adherence Calculation process for Mr. [PERSON]'s scenario:  
_Sub-step 1--CL calculation for Mr. [PERSON]_. Closeness is calculated assuming that the profile, care needs, care needs relevance, customer requirements and promising solution fragment (\(sp_{ij}\)), are already identified or characterized. Having this as background, let us suppose that Mr. [PERSON] selected three requirements for classifying a solution: _medium_ use facility; delivery time within 24 h; and the possibility of service suspension on holidays without cost. For Mr. [PERSON], the first criterion is identified by _usability level_, corresponding to _situation (b)_ in \(cl_{fuzzy}\) calculation. The second criterion, _delivery time_ represents a _situation (a)_, and the last criterion, _suspension on holiday_ corresponds to a _situation (c)_ of _business process flexibility level_ of service provider. Taking into account the selected requirements, the first one \(re_{1}\) (_usability_, is \"_medium_\", and if we consider that the solution \(sp_{11}\) is \"_low_\", by using balanced linguistic modeling [65] through the linguistic term and its associated trapezoidal fuzzy number, we have two fuzzy trapezoidal numbers with linguist term _Low (\(\ell_{1}\))_ and _Medium (\(\ell_{2}\))_. Thus \(cl_{fuzzy}\) (calculated by Equation (11)) represents here \(1-d(\ell_{1},\ell_{2})\), \(cl_{fuzzy}\) is 0.89. For the second requirement, \(re_{2}\): _time delivery_, the \(sp_{11}\) is available in two hours, so the \(sp_{11}\) features are better than the customer's request (24 h), thus \(cl_{ crisp}\) corresponds to \"1\". The last requirement involves business process changes following a _very high_ exigency level (corresponding to a _very flexible_ solution). The \(sp_{11}\) fragment has a _moderate_ flexibility level, thus the corresponding closeness through the linguistics terms is 0.5. For each {service, provider} fragment and set of requirements, a step-by-step calculation is performed and the corresponding closeness vector \(CL\), for [PERSON] [PERSON] and \(sp_{11}\) fragment, is \(CL=\{0.89,1,0.5\}\), and the corresponding \(G(cl_{m})\) is 0.797. _Sub-step 2--Partial Adherence (PA) calculation_. Considering the two care needs of Mr. [PERSON]: _bloodPressure_ and _homeSafety_, and the service coverage level (\(CO\)) coefficient assigned in the care needs taxonomy to \(sp_{11}\)_fragment_ of \(CO=\{co_{1},co_{2}\}=\{very\,high,low\}\), the _PA_ associated with Mr. [PERSON]'s care needs is the following (based on Equation (14)), \(pa_{1}=0.885\) and \(pa_{2}=0.622\), thus: \(PA_{sp_{11}}=\{0.885,0.622\}\). _Sub-step 3--Adherence calculation_. Considering that all fragments have a _PA_, the Solution's matrix is built. Table 10 shows the matrix for Mr. [PERSON]. Like solution \(sp_{11}\), exemplified above, a similar process is applied to all \(sp_{ij}\) rows.
# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 5 Experimental Evaluation
### Evaluation Results
#### 5.2.3 Parameter Optimization
Figure 7c shows that ACC and SPE reach their maximums when the coverage threshold \(C\) is set to 0.8. However, for SEN, the optimal coverage threshold is 0.9, where ACC and SPE have decreased considerably. To keep all three metrics relatively high, the coverage threshold \(C\) is finally set to 0.85. In Figure 7d, all of the metrics rise sharply at first, and then decline gradually, which can be explained by the fact that utilizing multiple CARs will bring more comprehensive and stable information and hence improve the classification performance; however, too many CARs will lead to over-fitting and thus result in reduced performance. In this study, the number of CARs is set to 3, since the increment of ACC is less than the reduction of SEN when the number of similar CARs varies from 3 to 4.
# Behavior of Two-Chord Steel-Concrete Composite Columns under Axial Compression
###### Abstract  
Experimental and numerical research on axially compressed columns made from built-up two-chord concrete-filled steel tubes (TCCFSTs) is presented in this study. The columns were constructed from two parallel circular high-strength steel tubes connected by five batten tubes. The chord tubes were filled with high-strength concrete. The yield stress of the steel used was 600 MPa, while the cylinder compressive strength of the concrete was 95 MPa. Hollow specimens were also tested to serve as a control group. An experimental analysis investigated the influence of the compressive strength of the concrete fill on the load-bearing capacity of the column and the influence of the concrete fill on the slenderness of the column. The behavior under load, stress and strain development, and the failure modes of the specimens were also analyzed. The results of the tests showed that all parts of the built-up column participated in the load-bearing process. The load-bearing capacity of the hollow two-chord columns was improved by around 1.74 times, and the slenderness increased by 16% with the concrete infill. The columns filled with concrete exhibited almost linear behavior with a higher ultimate strength and stiffness than the hollow built-up steel columns. Furthermore, the application of three calculation codes to forecast the capacity of the TCCFST columns was evaluated. Additionally, finite element method (FEM) modeling was used to investigate the stresses, strains, deformations, and ultimate capacity of the TCCFST column models loaded with axial compressive force. The FEM model showed good predictions of strength, stresses, deformations, and buckling.  
two-chord columns; composite action; load-bearing capacity; experimental investigation; FEM modeling +
Footnote †: journal: Applied sciences
## 2 Preliminaries and Problem Formulation  
### Preliminaries  
Some key definitions and lemmas adopted by the control system design and stability analysis are collected and introduced here.  
**Definition 1**[26].: _Given a system_  
\[\dot{x}=f(t,\,x), \tag{1}\]  
_its equilibrium \(x=0\) is GUAS if and only if_  
\[\|x(t)\|\leq\zeta(\|x(0)\|,\ t),(t>0) \tag{2}\]  
_where \(\zeta(\cdot)\) is a class KL function._  
**Definition 2**[26].: _Given a system_  
\[\dot{x}=f(t,\,x,\,u), \tag{3}\]  
_where \(f:R\times R_{n}\times R_{p}\) is continuously differentiable, the system is input-to-state stable (ISS) if_  
\[\|x(t)\|\leq\zeta(\|x(0)\|,\,t)+\gamma(\|u\|),(t>0) \tag{4}\]  
_for any initial state and any bounded input, in which \(\zeta(\cdot)\) is a class KL function, \(\gamma(\cdot)\) is a class K function._  
**Lemma 1**[27].: _Given the system (1) with the equilibrium \(x=0\), \(f(\cdot)\) is locally Lipschitz in \(x\) uniformly in \(t\). There exists a continuously differentiable, positive definite and radially unbounded function \(V:\ [0,\infty)\times R^{n}\to R\), it satisfies_  
\[\dot{V}=\frac{\partial V}{\partial x}f(x,\,t)\leq-W(x)\leq 0,\ \forall t\geq 0,\ \forall x\in R^{n}, \tag{5}\]  
_in which \(W\) is continuous. Then, all solutions of system (1) are globally uniformly bounded and satisfy \(\underset{t\to\infty}{\lim}W(x(t))=0\). In addition, if \(W(x)\) is positive definite, then the equilibrium \(x=0\) is GUAS._  
**Lemma 2**[26].: _Let \(V:\ [0,\,\infty)\times R^{n}\to R\) be continuously differentiable and satisfy_  
\[\alpha_{1}(\|x\|)\leq V(t,\,x)\leq\alpha_{2}(\|x\|), \tag{6}\]  
\[\frac{\partial V}{\partial t}+\frac{\partial V}{\partial x}f(t,\,x,\,u)\leq-W (x),\ \forall\|x\|\geq\rho(\|u\|)>0, \tag{7}\]  
_where \(\forall(t,\,x,\,u)\in[0,\,\infty)\times R^{n}\times R^{m}\), \(\alpha_{1}\), \(\alpha_{2}\) are class K\({}_{\infty}\) functions, \(\rho\) is a class K function, and \(W(x)\) is a continuous positive definite function on \(R^{n}\). Then, the system \(\dot{x}=f(t,\,x,\,u)\) is ISS with \(\gamma={\alpha_{1}}^{-1}\circ\alpha_{2}\circ\rho\)._  
The following lemmas establish the stability of a cascade system.  
**Lemma 3**[26].: _Consider the cascade system_  
\[\dot{x}_{1}=f_{1}(t,\,x_{1},\,x_{2}), \tag{8}\]  
\[\dot{x}_{2}=f_{2}(t,\,x_{2}), \tag{9}\]_if the system (8) with \(x_{2}\) as input is ISS and the equilibrium point of the system (9) is GUAS, then the equilibrium point of the cascade systems (8) and (9) is GUAS._  
**Lemma 4**[27].: _Consider the cascade system_  
\[\dot{x}_{1}=f_{1}(t,\,x_{1},\,x_{2}), \tag{10}\]  
\[\dot{x}_{2}=f_{2}(t,\,x_{2},\,u), \tag{11}\]  
_if the system (10) with \(x_{2}\) as input is ISS, and the system (11) with \(u\) as input is ISS, then the cascade systems (10) and (11) are ISS._
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Information Extraction and Lossless Recovery
#### 3.3.2 Secret Information Extraction and Image Authentication  
The same key used for the encryption is used to decrypt the two encrypted Shares that are recovered. The compressed data are extracted and then decompressed to obtain ShareA and ShareB following decryption. The detailed steps are as follows, using ShareA\({}^{\prime}\) as an example:  
**Step 1:** If the extracted and calculated authentication bits are the same, the process starts from the third step; if they differ, it proves that the image has been tampered with, and the process starts from the second step.  
**Step 2:** The tampered regions are located by the comparison results of the authentication bits, and only the third, fourth, and fifth steps are performed on the regions that are not tampered with.  
**Step 3:** After the authentication bit is removed, the POBN algorithm is used to convert the 8-bit POB value to the 10-bit POB number, and the embedded secret information is extracted.  
**Step 4:** The random number generated by the hyperchaotic Lorentz system is used for XOR decryption, and the key used for decryption is identical to the encryption key. After decryption, an image composed of repeated filling with compressed data is obtained.  
**Step 5:** After extracting the compressed data from the filled image and decompressing the data, ShareA, composed of the reorganized data, is recovered.  
The same steps are performed for ShareB\({}^{\prime}\) until the embedded secret information is extracted and ShareB is recovered.
## 3 Results and Discussions
### ANN Training Performance  
The developed ANN model was trained with 226 input-output pattern sets and tested by utilizing the remaining 30 sets among 256 combinations. The performance of trained ANN was evaluated using Equation (9).  
\[\%\ Max.\ absolute\ prediction\ error=\left|\frac{100*(pred.-exp.)}{pred.}\right| \tag{9}\]  
The maximum absolute prediction error of different steel grade ANN models for the training and testing patterns are summarized in Table 6.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 1 Introduction
As NB-PLC systems operate in a low and relatively narrow frequency range, they seem suitable for SG applications operating at low data rates (up to 144 kbps). In particular, NB-PLC transmission via MV power lines seems to be a promising SGC option as the MV grid runs through longer distances and serves remote and isolated areas, where the use of other SGC options might not be affordable. This work investigates how NB-PLC employing non-branched MV lines can increase the reach of low rate SG data in remote and/or isolated areas. To that end, multipath propagation is ignored. The transmission environment considered takes into account the transfer function of the NB-PLC channel encountered in MV power lines and its noise characteristics. Since PLC transmission employs primarily OFDM, this article investigates the performance of two such modulation schemes, namely filtered-OFDM (F-OFDM) and wavelet-OFDM (W-OFDM). These modulation schemes are only used indicatively to demonstrate the fact that NB-PLC is a viable SGC option via MV grids, in addition to its use over LV grids. Error correction techniques, such as low-density parity-check code or convolution code, which are well established in the usual practice, may also significantly extend the range of NB-PLC systems. Additionally, adaptive modulation or power allocation techniques (e.g., using a water filling algorithm) may enhance the reach of NB-PLC signals through the MV grid. The simulations performed by the authors have shown that low rate data can be transmitted via non-branched MV power lines to distances up to 2.5 km compared to significantly lower distances--of the order of 500-800 m--attainable employing BB-PLC. The simulations have also shown that F-OFDM modulated signals are capable of reaching significantly greater distances than the W-OFDM ones. When a robust modulation scheme is used (e.g., 2-QAM), a bit error ratio (BER) in the order of \(10^{-4}\) is attainable. The rest of the article is organized as follows. In Section 2, NB-PLC transmission in multi-conductor overhead MV power lines is outlined. In Section 3, the corresponding noise model is discussed considering the primary noise mechanisms and sources encountered in MV power lines. In Section 4, the two OFDM options under investigation with regard to NB-PLC transmission are briefly discussed. Section 5 presents the obtained simulation results that determine the maximum reach of NB-PLC signals before having to be repeated. Finally, Section 6 concludes the article.
## 5 Case Study  
### Description of the Test System  
The test system adopted in this paper is the Zhangbei \(\pm\)500 kV four-terminal bipolar DC grid in china. Its schematic diagram is illustrated in Figure 7. Kangbao, Zhangbei, Fengning and Beijing converter stations are abbreviated as s\({}_{1}\)-s\({}_{4}\). Parameters of the Zhangbei DC grid are listed in Table 1. Generally, due to the delay of the communication system, the converters receive the blocking signal and then execute the blocking command with a delay of several cycles. Considering the factors such as short-term DC withstand voltage and thermal stress tolerance, the delay should generally not exceed 10 ms. This paper is suitable for estimating the fault characteristics prior to the blocking, but unfortunately the blocking of MMCs cannot be modeled. Therefore, an identical calculation step of 10 ms and time step of 50 us is set for the EMT simulation performed on PSCAD and the presented method performed on MATLAB.
## 3 Methods
### Integrating the BiFormer Module to Enhance Small Object Feature Extraction Capability
Small object detection has always been challenging in object detection because small objects occupy few pixels in the image, making their visual features less distinct and difficult to distinguish and recognize. This also means that the detailed information on small objects is minimal, leading to more difficult classification and localization. Small objects are easily occluded by other objects or backgrounds, especially in dense scenes. Additionally, background noise can resemble the appearance of small objects, increasing the risk of false positives. The size of objects also varies significantly due to factors such as distance and viewpoint. This scale variation poses a challenge for small object detection because less information is available for detection as the scale decreases. Firstly, dynamic sparse attention enhances the model's content-awareness. The Bi-Former dynamically selects attention regions through bi-level routing rather than fixed patterns. This dynamic nature allows the model to adaptively select the most relevant regions for in-depth analysis based on input features. For small objects, this means the model can more accurately focus on small regions containing the target rather than uniformly distributing attention across a large area, which is crucial for capturing subtle features of small objects. Region-to-region routing ensures efficient information flow by ensuring that only pre-selected, highly relevant regions exchange information through directed graphs and adjacency matrices. This fine-grained information flow is more suitable for capturing small objects dispersed in the image, as the attention for each region is optimized, focusing on specific areas containing helpful information. Regarding optimized performance and efficiency, BiFormer reduces memory usage and computational resource consumption by skipping calculations for many irrelevant regions due to its sparse operations while maintaining high performance. This is particularly important for processing large numbers of images and real-time detection scenarios, such as detecting tomato diseases in agricultural applications. The token-to-token attention within the retained routing regions enhances the model's ability to capture finer details. This fine-grained attention mechanism is particularly suitable for small object detection as it can provide detailed analysis of the target's details, helping to improve detection accuracy and reduce emissions. Lastly, the adaptive multi-scale processing capability enables flexible handling of targets of different scales. BiFormer can handle multi-scale inputs through its structural design, making it more effective in detecting targets of different sizes, especially in common dense tomato detection scenarios in agricultural images. Figure 7: Schematic representation of the specific implementation module of BRA.