esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed DoA Estimation Algorithm and Beamforming Scheme
### Uav Fsi  
Positions are sourced from the UAV FSI, which incorporates the UAV navigation data. By using a Kalman filter to integrate Global Positioning System (GPS) and on-board sensors' motion information, the UAV's navigator maintains more accurate positions and motion vectors (velocity and acceleration) than simply using GPS systems [31; 32]. UAV's FSI is regulated to be transmitted to the ground controllers periodically, for safety concerns. This telemetry message is exchanged at a minimal frequency of 4-5 Hz [1]. Although the BS could command the UAV to report more frequently, we will demonstrate in Section 4 that this is not an actual need. FSI is a reliable information resource--as specified in the 3 GPP standard, it has high quality of service (QoS) priority (up to \(10^{-3}\) Packet Error Loss Rate) [33]. The first step to calculate the signal DoA consists of extracting flight status data from the telemetry message, which can be realized by creating an interface between the coordination information stack and the beamforming processor.  
To summarize, utilizing the FSI has several benefits. First, UAV's position is deducted from processing UAV's on-board GPS and the inertial measurement unit (IMU) data, which obtains higher accuracy than using the GPS sensor alone, and reduces the computational load on the BS end. Second, FSI is regulated to be transmitted for any type of UAVs, so that the proposed algorithm is applicable for all civil applications. Third, by using FSI, which already exists in the Command&Control Links between UAV and cellular networks, we reduce the use of training sequences and thus reduce the training overhead.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results
### Energy Microbenchmarks
This makes the sending of a packet with this protocol energetically more expensive than with SNW-MAC or PW-MAC. On the sink side, the energy cost of a packet reception is also highly variable and requires on average the listening of one and a half data packets in addition to the transmission of an ACK frame. Using these microbenchmarks, the \(\tau_{i}\) values introduced in Section 3.2 were measured, and \(H\) and \(\xi_{\infty}\) were calculated for the different MAC protocols using the lowest measured values of the \(P_{i}\), leading to the best achievable values of \(H\) and \(\xi_{\infty}\). Table 2 presents the obtained results. It can be observed that using SNW-MAC allows a significantly better use of the energy budget. Indeed, SNW-MAC permits values of \(H\) (respectively \(\xi_{\infty}\)) more than twice smaller (respectively bigger) than with PW-MAC and more than nine-times smaller (respectively bigger) than with UPMA-X-MAC. Energy overhead of the EM:  
The EM is periodically executed by each node and therefore incurs an energy overhead. Using micro-benchmarks, it was measured that each execution of the EM consumes 207.41 uJ at most. For the rest of this work, the duration between two executions of the EM \(T\) is set to 120 s, and the power consumption overhead incurred by the EM is thus equivalent to a constant power draw of 1.73 uW, which is similar to the power consumption of state-of-the-art electronic components for WSN nodes in the sleep state.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Generation Experiment of Pseudo-Side-Scan Sonar Images  
A total of 500 optical remote sensing images (containing aircraft and ship targets) and 123 side-scan sonar target images from the network were collected. Then, the CycleGAN model was used for the style transfer from optical images to sonar images. The parameters of the CycleGAN during the training were set as follows. The batch size was 1, the input image size was 256 \(\times\) 256 pixels, the initial momentum was 0.5, and the learning rate was 0.0002 for the first 150 epochs, then linearly decreased to 0 in the next 100 epochs. Other parameters were set to their default values.  
A total of 448 pseudo-side-scan sonar images were generated in the experiment, of which 204 were images with aircraft targets, and 244 were images with shipw targets. As shown in Figure 7, the CycleGAN model performed well in the image style transfer of optical remote sensing images to side-scan sonar images.  
To demonstrate the validity of CycleGAN, the generated images were compared with those of other style transfer models (PhotoWCT [29], AdaAttN [30], and AdaIN [31]) on the same dataset, as shown in Figure 8.  
Figure 8 shows that the AdaAttN model generated the worst results. In these pseudo images, the targets became blurred, and the background was far from the real one. The images using the AdaIN model also had problems, including blurred targets, mosaic-like backgrounds, and incorrect colors retained from the original images. The images generated using the PhotoWCT model were better compared with the results of the AdaAttN and AdaIN models, but these images had no shadows, and the targets and background were difficult to distinguish. Only in the generated images using the CycleGAN model, the shadows of the targets were preserved. The shadows of the targets in side-scan images were one of the most important features used for detection. Moreover, in the images generated using CycleGAN, the targets and background were easy to distinguish, and the  
Figure 7: Experimental results of generating pseudo-side-scan sonar images using CycleGAN. The images in the 1 st and 3 rd rows are optical images, whereas the images in the 2 nd and 4 th are generated pseudo-side-scan images.  
background approached the real one. The results showed that the CycleGAN model had the best performance in generating side-scan images compared with the other models.
###### Abstract  
During geomagnetic storms, which are a result of solar wind's interaction with the Earth's magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth's surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransomers at five substations of the Kola-Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransomer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier's performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer.  
geomagnetically induced currents; autotransomer; continuous wavelet transform; convolutional neural network; binary classification +
Footnote †: journal: _Applied_  
1
Footnote 1: [[https://doi.org/10.3390/accepted](https://doi.org/10.3390/accepted)]([https://doi.org/10.3390/accepted](https://doi.org/10.3390/accepted)): 18 January 2024
## 2 Proposed Single Source Three-Phase Topology
### Capacitance Design and Self-Balancing  
The capacitor (\(C_{X}\)) is used as a dc voltage source to provide the various output levels needed by the proposed SCMLI. Discharging to loads causes a decrease in their voltages.The capacitance of a capacitor typically decreases as the voltage ripple across it increases. Therefore, calculating the voltage ripple across the capacitor is the first task to complete in SC design. The voltage ripple can be computed as follows for a completely passive load, which represents the worstcase scenario because there is no load current flowing back to the capacitors [10].  
\[\Delta V_{CR}=\frac{1}{\omega C_{R}}\int_{t_{a}}^{t_{b}}\frac{2V_{DC}}{R}dt \tag{1}\]  
The capacitor design in an SCMLI is a significant factorto avoid undercharging and-voltage ripples. Consequently, the ideal estimation of capacitance is chosen in such a way, so as to fulfill the following condition [6]:  
\[C_{optimum}\geq\frac{\Delta QC}{\Delta V_{CX}} \tag{2}\]  
where \(\Delta Q_{C}\) represents the amount of discharge from the capacitor, \(K\) is the percentage of extremely tolerable voltage ripple, and \(V_{DC}\) is the supply voltage.  
The stability of the capacitor relies on the magnitude of the load current, power factor, and longest releasing period of the capacitor. Therefore, the maximum discharge amount of capacitors is obtained as: Therefore, the releasemeasure of charge (\(\Delta Q_{C}\)) can be communicated as  
\[\Delta Q_{C}\int_{t_{a}}^{t_{b}}I_{peak}sin(2\pi ft-\varphi)dt \tag{3}\]  
where \(I_{peak}\) is the maximum current flowing through the load, \(f\) is the modulated wave frequency, and \(\varphi\) is the phase angle difference between voltage and current; The longest discharging times for each capacitor is (\(t_{a}-t_{b}\)).  
This discharging period can be calculated from Figure 3b as follows [6]:  
\[t_{a} =\frac{sin^{-1}\Big{(}\frac{1}{2}\Big{)}}{2\pi f} \tag{4}\] \[t_{b} =\frac{\pi-sin^{-1}\Big{(}\frac{1}{2}\Big{)}}{2\pi f} \tag{5}\]  
Taking into account the voltage ripple, the value of capacitance for \(C_{R1}\) is  
\[C_{R}\geq\frac{\int_{t_{a}}^{t_{b}}I_{peak}sin(2\pi ft-\varphi)dt}{\Delta V_{ CR}} \tag{6}\]The other capacitors (\(C_{Y}\) and \(C_{B}\)) can be treated in the same way.  
The capacitor \(C_{R}\) ischarged to \(V_{DC}\) during the voltage level of zero and \(+1V_{DC}\) by activating the switch \(S_{R2}\) through the power diode \(D_{R}\), as portrayed in Figure 3a,b. The capacitor delivers its stored energy to load at the voltage level \(+2V_{DC}\) shown in Figure 3c. This charging and releasing of a sequence of capacitors over every cycle keep it self-balanced automatically.
# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## Appendix B  
This appendix lists the Cypher queries that correspond to the questions Q1, Q2, and Q3 mentioned in Section 3.5. In the queries below, _top_n_ (organizations, people etc.) is set to 100, _top_m_ (topics) is set to 25, the number of deliverables is set to 10, and _profile_ is supplied by the user as a list of topics (e.g., ['sustainability, 'carbon emissions', 'green technologies']). We used a profile of 86 topics related to e-government, banking, sustainable technologies, and cryptocurrency.  
_Q1: What are the topics of interest for each of the top-n organizations that have coordinated (or participated in) the most projects of H2020? MATCH (o:Organization)-[:participates_in ] coordinates]->(p:Project) WITH o.name AS Organization, COUNT(p) AS Amount ORDER BY Amount DESC LIMIT 100 WITH COLLECT(Organization) AS orgsUNWIND_ orgs _AS_ org  
_MATCH_ (o:Organization {name: org})-[:participates_in_|coordinates]>(p:Project)-[:includes]-(k:Keyphrase)  
_WHERE NOT_ k.name _IN_ ['report', 'deliverable', 'task', 'project', 'document', 'available', 'terms']  
_WITH_ o.name _AS_ Organization, k.name _AS_ Topic, _COUNT(k)_ _AS_ NumberOfTopics  
_ORDER BY_ NumberOfTopics _DESC_  
_RETURN_ Organization, _COLLECT_([Topic, NumberOfTopics])[..25]  
Performance: Execution time: 1279 ms, Memory used: 253.69 MB, Database hits: 5,373,808. For hardware specifications see Section 3.1.  
_Q2: For a given list of topics, which deliverables were produced by the top-n organizations that have coordinated (or participated in) the most projects of H2020 were related to pilot applications?_  
_UNWIND_ profile _AS_ search_term  
_MATCH_ (o:Organization)-[:participates_in_|coordinates]>(p:Project)-[:includes]>(k:Keyphrase)  
_WHERE_ k.name _CONTAINS_(search_term)  
_WITH_ o.name _AS_ Organization, _COUNT_(p) _AS_ ProjectCount  
_ORDER BY_ ProjectCount _DESC LIMIT_ 100  
_UNWIND_ ['use case', 'pilot', 'application'] _AS_ del_ type  
_MATCH_ (o:Organization {name: Organization})-[:participates_in_|coordinates]>(p:Project)<-[:belongs]-(d:Deliverable)-[:includes]->(k:Keyphrase)  
_WHERE_ d.title _CONTAINS_ (del_type)  
_RETURN_ o.name AS Organization, _COLLECT_(DISTINCT d.title)[..10]  
Performance: Execution time: 5919 ms, Memory used: 5.35 MB, Database hits: 10,529,877. For hardware specifications see Section 3.1.  
_Q3: Which people have been working on a given list of topics? Have they been working on additional ones?_  
_UNWIND_ profile _AS_ search_term  
_MATCH_ (a:Person)-[:writes]>(d:Deliverable)-[:belongs]>(:Project)-[:includes]->(k:Keyphrase)  
_WHERE_ k.name _CONTAINS_(search_term) _AND NOT_ k.name _IN_ ['report', 'deliverable', 'task', 'project', 'document', 'available', 'terms']  
_WITH_ a.name _AS_ Person, k.name _AS_ Topic, _COUNT_(k) _AS_ NumberOfTopics  
_ORDER BY_ NumberOfTopics _DESC_  
_RETURN_ Person, _COLLECT_([Topic, NumberOfTopics])[..25] _LIMIT_ 100  
Performance: Execution time: 7575 ms, Memory used: 423.29 MB, Database hits: 12,069,554. For hardware specifications see Section 3.1.
###### Abstract  
Background: Accurate reconstruction and quantification in the post-therapy SPECT/CT imaging of \({}^{166}\)Ho microspheres for hepatic malignancies is crucial for treatment evaluation. This present study aimed to explore the impact of the OSEM reconstruction parameters on SPECT/CT image features for dose distribution determination, using Hybrid Recon(tm) (Hermes Medical Solutions AB) and full Monte Carlo (MC) collimator modeling. Methods: Image quality and activity quantification were assessed through two acquisitions of the [PERSON] phantom using a Siemens Symbia Intevo Bold SPECT/CT system. The datasets were reconstructed using the OSEM method, with variations in the number of iterations for 15 and 8 subsets, both with and without full MC collimator modeling. Contrast recovery coefficient (Q\({}_{\mathrm{H}}\)), coefficient of variation (CV), contrast-to-noise ratio (CNR), calibration factor (CF), and activity recovery coefficient (ARC) were calculated and used to evaluate image quality and activity quantification. Results: Reconstructions with 5 iterations and 15 subsets, as well as 10 iterations and 8 subsets, were selected as the most suitable for \({}^{166}\)Ho imaging, as they provided higher Q\({}_{\mathrm{H}}\) and ARCs. Incorporating full MC collimator modeling in both reconstructions led to significant improvements in image quality and activity recovery. The CFs remained consistent for a fixed value of 15 and 8 subsets, with values of (14.9 \(\pm\) 0.5) cps/MBq and (14.6 \(\pm\) 0.5) cps/MBq, respectively. However, when applying full collimator modeling, the CF values decreased to a range between 10.9 and 12.1 cps/MBq. Conclusions: For \({}^{166}\)Ho SPECT/CT imaging, OSEM (with either 5 iterations and 15 subsets or 10 iterations and 8 subsets) combined with full MC collimator modeling yielded superior image quality and quantification results.  
Keywords:\({}^{166}\)Ho; radioembolization; SPECT/CT reconstruction; SPECT/CT quantification; image quality +
Footnote †: journal: applied sciences
## 3 Problem Formulation
### Assumptions and Threat Model
#### 3.2.2 Primary Threats \(\mu\)  
1. **Data poisoning attacks \((\mu_{d})\):** The malicious entities may manipulate the local dataset or introduce fake data into the global model \(w_{g}\) updates. We represent this threat as a perturbation \(\delta\) of local updates: \[\psi_{i}^{t+1}=w_{i}^{t+1}+\delta,\quad\delta\sim\mathcal{D}_{\text{adversary}}\] (8) where \(\mathcal{D}_{\text{adversary}}\) is an adversarial distribution.
2. **Privacy breaches \((\mu_{p})\):** The adversaries could try to recreate private information from model updates. In such cases, the differential privacy anticipates and assists the users from the privacy risks: \[\Pr[f(DS)\in S]\leq\epsilon^{\epsilon}\cdot\Pr[f(DS^{\prime})\in S]+\delta\] (9) where datasets \(D\) and \(DS^{\prime}\) differ by one record; here, \(f\) is our learning algorithm, \(\epsilon\) is the privacy budget, and \(\delta\) is the failure probability.
3. **Integrity and authentication attacks \((\mu_{i})\):** The attackers might fabricate the devices or interfere with model updates. In order to solve this, our blockchain security layer \(\mathcal{B}=(B,T,\sigma,V)\) ensures the following: \[V(b_{k},e_{i})=1\iff T(w_{i}^{t+1})\text{ is a valid transaction in }b_{k}\] (10) where \(b_{k}\in B\) is a block in the blockchain, and \(V\) is the validation function.  
With its blockchain-based integrity verification \(\mathcal{B}\), adaptive aggregation function \(f\), and privacy-preserving methods, EdgeGuard confronts these fundamental assumptions and threats. The reliability evaluation function \(R\) and quality assessment module \(Q:\mathcal{D}\rightarrow[0,1]\) are designed to counteract data poisoning attacks and the blockchain layer offers anunchangeable audit trail to guarantee the authenticity and integrity of model modifications. By keeping raw data localized and introducing noise to model updates, the federated learning method, when paired with differential privacy measures, naturally contributes to privacy protection against breaches.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.3 Shannon Entropy (ShE)  
The ShE parameter divulges information regarding the extent to which the probability of a signal is spread out over all the possible magnitudes of the signal. It is often considered a measure of uncertainty. ShE is calculated from the signal amplitude using Equation (5).  
\[\text{ShE}=-\sum_{t=1}^{n}p(x_{t})\cdot\ln\left(p(x_{t})\right) \tag{5}\]  
where \(x_{t}\) represents the possible amplitude values of the signal.
## 3 Bag-Grouting-Pile Technology  
After ordinary piles penetrate the hard layer, the problems of necking and broken piles often occur. To address this critical issue, the author co-designed this construction method, which was implemented by the author in this railway project [1]. A bag grouting pile is a cylindrical reinforcement made from a combination of geotextile and grouting slurry. The composition is mainly made up of grouting core pipe, geotextile bag, and cement paste. The construction process (shown in Figure 2) begins with a rig drilling to punch holes and to put the cloth-wrapped grouting core pipe into the hole. After that, cement paste is poured in from the top of the core tube into the cloth. This process will generate grouting pressure that will cause the bag to expand from bottom to top.  
It should be noted that the cloth is bound with wire. When the cloth is filled with cement slurry, the soil around the hole wall is squeezed. This will cause a slight increase in the size of the pore. When grouting is completed after 20 min, the pile body is grouted twice. When the maximum temperature is lower than 25 \({}^{\circ}\)C, the concrete shall be covered and sprinkled for curing within 12 h. In the initial stage, the amount of sprinkling water is increased appropriately. When the seven-day uniaxial compressive strength of the bag pile is greater than 3.5 MPa, curing is stopped. The curing time is not less than 21 days. Finally, a cylindrical object (30-40 cm) is formed. The composite foundation is made up of the bag grouting pile and soil layer to improve the soil-bearing capacity.  
Figure 1: Drilling schematic diagram.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 4 Design Parameters and Guidelines
### Distance between Bow Ties dx and dy  
The distance between the bow ties is represented by the parameters \(dx\) and \(dy\). The exact depiction of \(dx\) and \(dy\) is visible in Figure 2. When adjusting the FSS design for other frequencies, it is best to start with fixed values for \(dx\) and \(dy\); all previous equations were derived for \(dx\) and \(dy\) values of 3 and 2 mm, respectively. Afterwards, when \(w\) and \(t\) are set, minor adjustments of \(dx\) and \(dy\) towards the center frequency can be performed.  
The results for parameter sweeps of \(dx\) and \(dy\) can be seen in Figure 9. The other two parameters \(w\) and \(t\) were fixed at 87 mm and 0.5 mm, respectively. Both \(dx\) and \(dy\) were again limited so that the neighboring unit cells would not touch each other. This would, as mentioned earlier, result in unexpected behavior. In Figure 9, the low band center frequency shift differs a lot from \(dx\) to \(dy\). The \(dx\) curve follows a logarithmic path, but the \(dy\) variation is more akin to a quadratic function. This means that one needs to take a lot of care when adjusting one of the parameters; the results could be surprising. One should not expect linear behavior. Figure 9 shows that the responses for \(dx\) and \(dy\) are more similar to each other; both follow a logarithmic function. The equations for the trend lines and the trend lines themselves are not depicted here, because they will change together when
###### Abstract  
The construction industry produces enormous amounts of information, relying on building information modeling (BIM). However, due to interoperability issues, valuable information is not being used properly. Ontology offers a solution to this interoperability. A complete knowledge base can be provided by reusing basic formal ontology (BFO). In previous studies, domain ontology was developed without BFO. Domain ontology requires loads of effort to reuse because domain ontology is too detailed. To increase the reuse rate and establish a complete knowledge base, it is necessary to develop BFO. This study has developed the BFO in the BIM domain to advance interoperability. First, unnecessary parts were omitted from the existing BFO development process, the process was simplified, and the base of hierarchy was created by extracting the most basic superclasses of the BFO model from Revit, the software of BIM. Based on that hierarchy, each child class was created, and the BFO model was completed by completing the relation of each class. After completion of the model, reliability, in addition to the completeness of the model, was evaluated through a query. Domain experts can reuse the BFO when defining relations between concepts and entities. The proposed BFO will be the foundation of future ontology developments in the BIM domain. This study facilitates future researchers to enhance interoperability in the BIM domain and make the ontology more complete to improve information sharing.  
basic formal ontology; knowledge management; building information modeling +
Footnote †: journal: _Applied Sciences_