esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussions
### Downstream Transmission  
For the downstream link, we firstly investigate the required number of FFE taps at the Rx side. As shown in Figure 6a, the BER performance is improved as the number of taps is increased but stabilizes when the number exceeds 100. So we fix the number of FFE taps to 100 in the following experiments. Afterward, under the condition that the training sequence length of LUT is 100,000, we use this FFE to generate three LUTs with different memory lengths--3, 5 and 7, respectively, and the generated LUTs are presented in Figure 6b-d.  
Figure 4: (**a**) BER performance under four conditions: without SOA, with SOA-1 only, with SOA-2 only, and with both SOA-1 and SOA-2; (**b–e**) Corresponding receiver time-domain signals and FFE recovered eye diagram at –3 dBm ROP.  
Figure 5: (**a**) SOA-2 gain and OSNR of 100 Gb/s PAM-4 signal versus output power; (**b**) BER of 50 Gbaud PAM-4 signal versus SOA-2 bias current.  
Figure 7a depicts the BER performance with FFE-based LUT pre-compensation. Since the O-band has a very low CD coefficient, the system performance is hardly degraded by CD in a short distance (e.g., 25 km) SSMF transmission. Besides, for a short distance and low launch power, the fiber nonlinearities can also be neglected. It is worth noting that contribution to the interaction between CD and chirp effects of the modulator. The BER performance is slightly improved after 25 km transmission compared with the BtB case. To better mitigate the pattern effect, the calibration ROP point is chosen to \(-8\) dBm where the pattern effect is strong, and the BER is lower than the SD-FEC threshold (\(1\times 10^{-2}\)). As the results shown in Figure 7a demonstrate, thanks to larger memory length, the 7-symbol LUT has a much stronger performance to mitigate the pattern effect than 3- and 5-symbol LUT. However, owing to the large error value stored in 7-symbol LUT, it has a potential risk of sensitivity penalty. Moreover, its higher calculation complexity and huge memory requirement make it difficult to be implemented practically. Notice that for 3- and 5-symbol LUT combined with FFE, a 0.8 dB receiver sensitivity improvement is obtained compared with FFE only and the combination of 7-symbol LUT and FFE. Therefore, a 3 or 5-symbol LUT will be a better choice to trade off the performance and complexity. However, for the SD-FEC threshold, 3- and 5-symbol LUTs have the same performance of sensitivity and dynamic range, so 3-symbol is finally chosen in our experiment. Figure 7b,c show the eye diagrams after 25 km SSMF transmission with 3- and 5-symbol LUT pre-compensation and post FFE, respectively, and the 'eye' between levels '2' and '3' is clearly open.  
Figure 6: (a) BER performance as a function of the number of FFE taps; LUTs with memory length, \(2M+1=3\), 5 and 7 for (**b–d**), respectively.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)
#### 5.2.1 Working Process of Bridges in CrossDeFi  
In the CrossDeFi ecosystem, the functioning of bridges is crucial for asset transfers between different blockchain networks. As illustrated in Figure 3, the asset bridging cycle (ABC) process encompasses both \"lock-and-mint\" and \"burn-and-release\" mechanisms to ensure the stability of token distribution across chains. Specifically, when tokens are transferred from Chain A to Chain B, the bridge locks the specified number of tokens on Chain A while simultaneously minting an equivalent number of tokens on Chain B. This dual-action mechanism maintains the total number of circulating tokens but redistributes them across two chains. For example, if Chain A initially holds fifteen tokens and five are to be transferred, Chain A will lock these five tokens, effectively still displaying fifteen tokens in total by counting the locked ones. Concurrently, Chain B will increase its token count by five, reflecting the new tokens minted. These minted tokens on Chain B are controlled by their owner and can be redeemed anytime. The redemption involves burning the tokens on Chain B, which triggers the simultaneous release (unlocking) of the equivalent locked tokens on Chain A. This process ensures that the total number of tokens across both chains remains constant, thereby stabilizing the market value and providing a reliable transfer mechanism that mirrors traditional financial processes in a digital and decentralized environment.
## 3 Test Results
### Maturity of Metakaolin-Based Geopolymers  
The concept of maturation must be defined in this point. For example, in fruits, maturity is a process that causes them to become more palatable. Similarly, in civil construction, the maturation of a material can be understood as the interval of time that the material needs to be put into service. In the context of reinforced concrete, the regulations require that operating conditions, known as SLS (serviceability limit state), be verified. For this purpose, the mechanical properties (modulus of elasticity for example) of the 28-days are usually presumed.  
In this regard, it is interesting to remember what [PERSON] [27] writes: \"In concrete practice, the strength of concrete is traditionally characterized by the 28-day value, and some other properties of concrete are often referred to the 28-day strength. There is no scientific significance in the choice of the age of 28 days\". This age was chosen by considering the rate of hydration of ordinary portland cement OPC. It is, therefore, a chemical concept (rate of hydration). By chance or not, the question of \"28 days\" is appropriate for concrete cured at different temperatures, as it is on this day that the variations in the strength of the concrete under compression are reduced to acceptable values. The question that arises now is the following: can this context (concrete hydration) be applied to a geopolymer resulting from the alkaline activation of metakaolin? Obviously not. The chemical processes are  
Figure 13: Rupture of cylinder samples in compression tests: MW2, MC, and MN.  
different. Moreover, can be a single time interval for all geopolymers resulting from alkaline activation of a binder? Obviously not. Even new cement materials are being developed and such new cements also have different times of concrete hydration.  
Backing a little to the definition of maturation: suppose that a material is \"mature\" when it has the strength characteristics needed to be put into \"service\". This is a mechanical definition that is logical in the construction context. Figure 14 shows a chart showing the daily evolution of the modulus of elasticity \(\Delta\)E of the last 3 samples and an equivalent cement mortar (OPC_EC2). The \(\Delta\)E value of the geopolymers was evaluated from the polynomial equations, presented in the preceding figures. For equivalent concrete (fc = 25.5 MPa), the modulus of elasticity and the time evolution were estimated on the basis of the suggestions of EuroCode2 [33].  
The value of the modulus of elasticity is a fundamental parameter for checking the deformation safety of beams and slabs in the context of SLS [33]. Therefore, assuming deformation as a criterion for the maturity of a material, equivalent to day 28 (\(\Delta\)E = 0.37%), one can conclude that the concrete maturity is reached by the metkaolin-based geopolymer samples on day 16 (MW2 and MC) or day 15 (MN). In addition, in the case of concrete, the modulus of elasticity continues to rise well beyond day 28. For metakaolin-based geopolymers, their time evolution is expected to be complete by day 20.
## 4 Conclusions  
In this paper, a proof of concept of the idea of the fusion of a sensor tag with a frequency tuning using WPT was presented. A reconfigurable chipless tag powered with WPT and used as a gas sensor was successfully designed and fabricated. This tag was applied to the sensor, which consisted of a rectangular loop coupled to a U-folded resonator loaded with a glide-symmetrical interdigital capacitor with a thin film of tetrasulfonated copper phthalocyanine applied as a sensitive layer. New experimental verification confirmed the sensor's capability to detect chemical vapors (see Table 2). Furthermore, a varactor diode was used to ensure the reconfigurability of the sensor. The varactor diode was biased with a WPT system operating at a frequency of 7.2 MHz. Changing the DC voltage controlled the resonant frequency of the RCS response. A continuous tuning range of 200 MHz was achieved and confirmed experimentally. This tuning helped to set up the maximal sensitivity of the sensor for a particular type of vapor. A possible higher-frequency detuning can be achieved by using a parallel connection of several varactors.  
The proposed external control of the resonant frequency of various resonant circuits can be applied to a wide range of electric resonators that will be used in various sensing applications in the forthcoming IoT era.  
Conceptualization, H.A., J.M. and M.S.; Methodology, H.A., N.B., J.M. and M.S.; Software, H.A. and M.S.; Validation, H.A., J.M. and M.S.; Writing--original draft preparation, H.A.; Writing--review and editing, J.M., M.S. and N.B.; Visualization, J.M., M.S. and N.B.; Supervision, J.M. and L.L.; Funding acquisition, J.M. and M.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ministry of Education, Youth and Sports of the Czech Republic (program: INTER-EXCELLENCE; subprogram: INTER-COST; project: ITC20012) and the COST - European Cooperation in Science and Technology (COST Action CA18223 SyMat).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
To achieve the anticipated performance of massive multiple input multiple output (MIMO) systems in wireless communication, it is imperative that the user equipment (UE) accurately feeds the channel state information (CSI) back to the base station (BS) along the uplink. To reduce the feedback overhead, an increasing number of deep learning (DL)-based networks have emerged, aimed at compressing and subsequently recovering CSI. Various novel structures are introduced, among which Transformer architecture has enabled a new level of precision in CSI feedback. In this paper, we propose a new method named TransNet+ built upon the Transformer-based TransNet by updating the multi-head attention layer and implementing an improved training scheme. The simulation results demonstrate that TransNet+ outperforms existing methods in terms of recovery accuracy and achieves state-of-the-art.  
CSI feedback; massive MIMO; deep learning; Transformer; attention mechanism +
Footnote †: journal: Applied
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 2 Impedance Characteristics Analysis of D-PMSG
### Impedance Modeling of D-PMSG
#### 2.3.4 Frequency Coupling of the Sub/Supsynchronous Currents  
Furthermore, the relationship between sub/supsynchronous currents could be analyzed. Since there is no (\(2f_{1}-f_{p}\)) harmonic voltage at PCC, phase-domain voltage reference at (\(2f_{1}-f_{p}\)) satisfies:  
\[\mathbf{V}_{aref}[2f_{1}-f_{p}]=\mathbf{V}_{ga}[2f_{1}-f_{p}]=j(2\omega_{1}- \omega_{p})\mathbf{L}\mathbf{I}_{a}[2f_{1}-f_{p}] \tag{23}\]  
Introducing (18) into (23), and eliminating \(\mathbf{V}_{p}\) by (22), the current response is given by:  
\[\mathbf{I}_{p2}[2f_{1}-f_{p}]=\frac{H_{i}(j\omega_{1}-j\omega_{p})I_{1}T_{p}(s )e^{j\omega_{1}}+V_{1}T_{p}(s)}{1-[H_{i}(j\omega_{1}-j\omega_{p})I_{1}T_{p}(s )e^{j\omega_{1}}+V_{1}T_{p}(s)]}\overline{\mathbf{I}}_{p} \tag{24}\]  
For the system parameters setup in 2.2 section, it can be calculated that \(\mathbf{I}_{p2}=0.7754\cdot e^{3.063.}\)(\(-\mathbf{I}_{p}\)). In the simulation experiment conducted in 2.2, the comparison of sub/supsynchronous FFT results is shown in the following Figure 9. It is worth mentioning that for clearly observing, phases of the insignificant frequency components except for 20, 50, and 80 Hz are neglected. The simulation results in Figure 9 verified the correctness of (24) at 20 Hz. By changing the frequency of voltage perturbation, the correctness at (0, 100 Hz) could be validated, too.  
Figure 8: Frequency response of grid-connected D-PMSG’s impedance. (**a**) Magnitude comparison; (**b**) Phase comparison. Solid lines: model prediction; Marking points: simulation results.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 2 Network Slicing: Concept, System Architecture and Deployment
### From a Service Order to a Deployed Network Slice
* Regional PoPs, which represent Central Offices featuring the telco edge cloud [26]. The regional PoPs provide virtualization capabilities closer to service delivery endpoints in order to reduce the delay budget, making them ideal to host delay-critical telco workloads. * Finally, access PoPs, which are associated with far edge sites. Much more distributed and closer to cell sites than regional PoPs, the access PoPs provide execution environments for hosting workloads with real-time requirements, e.g., virtualized DU instances (vDUs). In this regard, commodity hardware is no longer valid; they need to be equipped with advanced, rich-featured CPU architectures (e.g., Intel Xeon) and hardware acceleration solutions (e.g., FPGA, structured ASICs, etc.) instead [27].
## 3 Results
### Gene Ontology (GO) and Pathway Enrichment Analysis  
We performed functional genes assay of genes from the brown module that have significant metastatic activity. The identified genes were categorized into three functional groups: biological process, cellular component, and molecular function (BP, CC, and MF, respectively). In the BP group, mRNA catabolic process, translation initiation, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, protein targeting to membrane, protein targeting to ER, establishment of protein localization to endoplasmic reticulum, protein localization to endoplasmic reticulum, SRP-dependent co-translational protein targeting to membrane, and co-translational protein targeting to membrane were mainly enhanced; in the CC group, ribosome, ribosomal subunit, cytosolic part, cytosolic ribosome, focal adhesion, cell-substrate adhesion junctions, apical junction complex, lateral plasma membrane, and apicultural plasma membrane increased; and in the MF group, structural constituent of ribosome, cell adhesion molecule binding, cadherin binding, ubiquitin protein ligase binding, unfolded protein binding, ribonucleoprotein complex binding, rRNA binding, threonine-type endopeptidase activity, threonine-type peptidase activity, and mRNA 5\({}^{\prime}\)-UTR binding were enhanced. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, our results demonstrated that these genes were mainly involved in ribosome, spliceosome, proteasome, and Parkinson disease. These results indicate that the genes from the brown module are involved in cell proliferation and cell migration (Figure 6).  
Figure 5: Identification of modules associated with the clinical traits of breast cancer. **(a)** Dendrogram of all differentially expressed genes clustered based on a dissimilarity measure (1-TOM). Eight modules were identified by WGCNA assay. **(b)** Heatmap of the correlation between module eigengenes and clinical traits of breast cancer. Two modules have non-metastasis related gene expression pattern and rest 6 modules have metastasis related gene expression pattern. **(c)** Distribution of average gene significance and errors in the modules associated with metastasis and non-metastasis breast cancer. The brown module was selected for assay because it had the highest gene significance in metastasis statement.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion
### Performance Evaluation
The analysis of the presented scenario revealed that the model's performance remained remarkably close to the scenario where all SU devices employed the same SNR value. This observation demonstrates the generalization capacity of our model, as it successfully adapts to the diverse SNR conditions encountered in practical deployments. However, it is important to note that as the range of SNR values for the SU devices was expanded, a decline in performance became evident. This observation suggests that increasing the range beyond a certain threshold negatively impacts the model's ability to accurately detect signals. Figure 10: Model ROC curves with varied SNR for cooperative SUs. Figure 9: ROC curves of the proposed CNN_BLSTM_MLP approach with different sample lengths at \(-14\) dB.
## 3 Proposed Algorithms Explanation
### Cooperative Localization Free Energy Efficient Routing Protocol (Co-LFEER)
#### 3.2.1 Selection of Destination and Relay Nodes and Data Forwarding  
The selection criteria of the DSN in the Co-LFEER protocol is the same as for the LFEER protocol. The protocol describes the DSN and RLNs having the same parameters for selection: residual energy, number of hops and bit error rate. The source node selects the only one RLN and the DSN to transmit the information bags in the direction of the final DNN. When the source nodes send the information packets in the broadcast nature, all nodes which are in the field of the source nodes receive the data packets. Then, only one DSN is chosen. Based on the function values, the RLNs are selected. A node is chosen as a RLN which has the second highest value of the cost function. The first highest function value is assigned to the DSN. The overall selection and data forwarding mechanism is provided in Algorithm 1.  
```
1\(Z\):Number of Nodes
2\(B_{t}\):Bit Error Rate Threshold
3\(S_{n}\):Sink Node
4\(DSN\):Destination
5\(RLN\):Relay Node
6\(R_{e}\):Residual Energy
7\(n\):A Number of Hops
8\(BER\):Bit Error Rate
9\(R\):Total Number of Rounds
10\(i\):Starting Number
11 for\(i\) = 1:1:Z do
12 Data Reached= false
13 while(Data Reached)
14 if(Sink is Available)
15 Data Reached= true
16 elseif(Sink is not Available)
17 if\(R_{e}\)>0
18 Find the Nearby Nodes
19 Find the \(DSN\) Amongst the Nearby Nodes
20 The \(DSN\) Found on
21 function (f) = \(R_{e}\) / \(n\times BER\)
22 For the First Highest function (f) value
23 Forward the Data
24 if\(BER\leq 0.5\)
25 Data Accepted
26 else
27 Find the \(RLN\) for the Second Highest Value of the function (f)
28 Forward the Data
29 breakbreak
30 end if
31 end if
32 end while
33 end for
```  
**Algorithm 1****Selection of Nodes and Data Forwarding.**
## 2 Related Work
[PERSON] et al. [27] performed a comparative analysis of sentence subject classification using rule-based and machine-learning models. The authors used opinion patterns for rule generation. They performed sentence subjectivity evaluation on Indonesian news to classify a news sentence into a subject or an objective. This classification was achieved using two machine-learning models, including a Naive-based classifier (NBC) and a multinomial Support Vector Machine (SVM). The evaluation and analysis of the results proved that the rule-based classifier has better performance with 80.36% accuracy, as opposed to SVM with accuracy 74.0% and NBC with accuracy 71%. This difference in accuracy between the rule-based approach and machine-learning algorithms has prevented the usage of the latter, in real solutions [15]. Similar to clinical domain, other domains also extensively use pattern-based approaches for NLP tasks such as opinion mining in various languages such as Persian [28] and Chinese [29]. [PERSON] et al. [28] proposed a hybrid framework of dependency grammar rules and deep neural networks for Persian opinion mining. The framework identifies polarity of the text by applying linguistic rules. However, if there is no rule to trigger for an unseen instance, the framework switches to the neural network model for the classification. Similarly, [PERSON] et al. [29] uses chines language grammar rules as constraints for Bi-LSTM model which improved chines sentiment classification compared to other deep-learning models such as RNN and LSTM. The aforementioned research initiatives, either use patterns, machine-learning models, POS tags, or UMLS mapping for recommendation statement identification. Each approach has its pros and cons. For example, the existing pattern-based identification methods use single patterns (heuristic, POS, or UMLS) which depend on some pre-specified or extracted patterns and face difficulty in producing a generalized solution. In order to mitigate these limitations and to get generalized patterns, we need a mixed-method approach, which combines multiple techniques. Therefore, we proposed a machine-learning-assisted pattern-based approach by combing heuristic patterns, POS patterns, and UMLS patterns. The mixed-method approach increases the chance of accurate detection of recommendation sentences by providing a complete and synergistic use of various individual patterns.
# Translational Echocardiography: The Dog as a Clinical Research Model of Cardiac Dysfunction
## 3 Cardiac Anatomical Similarities between the Human and the Dog  
The cardiac anatomy of dogs and humans share many similar features [3; 7], as shown in Figure 2. In larger mammals such as canines, the heart is usually located ventral to the mediastinum [7; 8]. Compared to humans, a dog's heart has a less pronounced left hemithorax orientation, and the long axis of the heart has a more ventral orientation. The base of the heart reaches approximately, the dorsal plane adjacent to the first rib and slopes to a variable degree in a craniodorsal direction [7; 9]. Dogs have an ovoid cardiac structure [8]. This differs slightly from human anatomy, where the heart has a trapezoidal shape and, like its canine counterpart, an acute apex. In both species, the apexes of the heart consist solely of the left ventricular cavities and structures [7; 9; 10; 11].  
A dog's mean heart mass weight to body weight ratio is 0.9 to 2.2% of its body weight [10; 11]. These measurements are likely to vary by breed. It has been reported that a human's mean heart mass weight to body weight ratio is 5 g per kilogram [11].  
Figure 2: Right parasternal short axis view in the canine patient, showing pericardium as a hyperechoic line surrounding myocardium.  
The pericardium surrounds the heart in humans and dogs, creating the pericardial cavity. The pericardium is attached to the diaphragm and sternum at the base of the heart. However, both species have different degrees of attachment to the diaphragm [8; 12; 13]. Humans have a firm and broad extension to the central tendons aponeurosis of the diaphragm. On the other hand, dogs have a phrenopericardial ligament attached to the pericardium and, externally, a thin layer of mediastinal pleura attached to the pericardium, allowing the heart to be kept centered in the thorax [11].  
The basic structure of the pericardium is very similar between the two species, but there are distinct differences [14; 15; 16]. Unlike other large mammals, where the thickness of the pericardial wall increases with heart size, humans have a thicker pericardium than animals with similar heart sizes [11]. In the human heart, the thickness of the pericardium is 2 mm or less [17], whereas the thickness of the pericardium in the dog is considerably thinner (0.18-0.20 mm) [11]. The normal pericardial fluid volume in most dogs is between 0.5 and 2.5 mL, and some dogs may have up to 15 mL, compared to volumes of 20-60 mL in adult human hearts [11; 12; 13].