esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Setting  
### Data  
We chose the AMPds (Almanac of Minutely Power dataset) [39] to test our proposed method. AMPds contains power consumption readings in a single house in Canada over a long of time (2 years) and provides metered data at the end-use level at 1 min resolution. We considered several typical indoor appliances, including the heat pump, clothes dryer, dish washer, and fridge. Since the measurement data of these appliances were given separately in AMPds, they were suitable for validating our method. As our algorithm does not apply to normally-open appliances (always in ON state), we did not take into account the normally-open appliances and undefined reading. The aggregation powers were attained by summing the powers of each appliance at the same time. The average powers for testing appliances were obtained by statistics as shown in Table 1.  
Figure 2: The flowchart of optimized support vector regression (OSVR).  
Due to the possible seasonality effects on power consumption, we would like to extract the training and testing periods in the same season with a similar climate condition. As mentioned in Section 2.1, we extracted a week's data from April 1 st 2012 to April 7 th 2012 for SVR training, and we chose several weeks' data in different seasons for validation, which were the week from April 8 th 2012 to April 14 th 2012 (period 1), from June 1 st 2012 to June 7 th 2012 (period 2), and December 1 st 2012 to December 7 th 2012 (period 3).  
The length of the time window was set to a day (1440 min). When retrieving, we chose the segment with same length and similar average active power as that of the OSVR training data. Since power values of all the appliances in our tests were greater than 30 watt, the noise threshold was set to 30 watt.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 5 Experimental Results
### Validation of the Proposed Imaging Method
As for \(\beta\), the Doppler center frequency is depended on it and their relationship is formulated in (33). Based on the imaging method shown in (27), the estimation accuracy of \(\beta\) should keep us obtain an accurate Doppler ambiguity. In the simulations, the encoding accuracy for \(\beta\) is given by (34), which could make the estimation error for the Doppler center frequency be less than PRF/10, where PRF is the pulse repetition frequency. \[f_{\alpha c}=-\frac{2}{\lambda}\bigg{(}\beta+\frac{\alpha}{2R_{c}}\bigg{)} \tag{33}\]  
\[\Delta\beta=\lambda\cdot\frac{PRF}{20} \tag{34}\]  
According to (31), (32), and (34), the encoding accuracies for the three parameters are figured out, and the results are: \(\Delta v_{e}\) = 0.03 m/s, \(\Delta\alpha\) = 9000 m\({}^{2}\)/s, \(\Delta\beta\) = 2.25 m/s. In addition, the population size is set to be 50, and the maximum generation is set to be 40. After these parameters being set, the strategy presented in Section 4.2 is utilized to solve the imaging optimization problem. The execution time for each target is measured, which shows that it needs only about 24 min to obtain the optimal solution. The solving processes for the five targets are illustrated in Figure 6, with the optimal image contrasts of each generation being plotted. As can be seen from Figure 6, the proposed method converges quickly. Figure 7 shows the imaging results, with the contours of target energy being plotted. The results are obtained by extracting a window of 64 \(\times\) 64 samples centered on the target and then expanding this \"window\" by a factor of 64 via zero padding the spectrum and inverse transforming. In addition, the image quality parameters are measured and the results are shown in Table 4. The ideal ISLR is measured from the ideal sinc function with the same bandwidth. From Figure 7 it can be seen that the five targets are all finely focused. Moreover, from Table 4 one can see that the image quality parameters are all very close to their theoretical values, which indicates that the targets are accurately focused by the proposed imaging method. Figure 6: Solving processes of the optimization problem. (**a**) T1, (**b**) T2, (**c**) T3, (**d**) T4, and (**e**) T5. Figure 7: Contour plots of the five targets. (**a**) T1, (**b**) T2, (**c**) T3, (**d**) T4, and (**e**) T5.
## 5 Numerical Results  
In this section, we compare the proposed eigenvalue fusion scheme with schemes using sample and decision fusions. Two types of primary signal were considered: a general wideband signal and an OFDMA signal. In the case of a general wideband signal, the performance is relative to sensing at the level of sub-bands. In the case of OFDMA, the performance is relative to sensing at the subchannel level. In all cases, the ROC curves were built from the average of \(P_{\text{fa}}\) and \(P_{\text{d}}\) in all frequency sub-bands of general wideband signals, or in all subchannels of OFDMA signals. The curves were obtained via Monte Carlo simulations, counting a minimum of \(100\) false alarm or detection events (which occurs first) or a maximum of 5000 runs. The code was implemented in MATLAB according to the models and test statistics described throughout the paper. The primary radio signal activity in each sub-band and subchannel was modeled as a Bernoulli random variable with \(50\%\) of the time in the ON state (for \(P_{\text{d}}\) computations) and \(50\%\) in the OFF state (for \(P_{\text{fa}}\) computations).
## 2 Composition of LEO Satellite Network and APINT Model
### Propagation and Transmission Delay  
In the LEO satellite network, each service is transmitted from the source node \(S_{a}\) to the target node \(S_{b}\) with the assumption that the topology remains unchanged during the transmission process. The service transmission path \(P_{z}\) has \(M\) satellites, and the propagation delay between the nodes is given by:  
\[t_{\text{pro}}^{i}=\frac{L_{ij}}{C}, \tag{1}\]  
where \(L_{ij}\) represents the inter-satellite distance from node \(i\) to node \(j\), and \(\forall i,j\in P_{z}\). Additionally, \(C\) is the propagation rate on the link. The time consumed during the process of node \(i\) sending data packet to the next node \(j\) is referred as the transmission delay:  
\[t_{\text{trans}}^{i}=\frac{E_{z}^{i}}{\varphi_{i}}, \tag{2}\]  
where \(E_{z}^{i}\) represents the size of the data for the \(z\)-th service sent by the node \(i\), and \(\varphi_{i}\) is the transmission rate of the switch on the satellite.
###### Abstract  
In the actual operation of urban rail transit (URT), the vibrations of steel-spring floating-slab tracks (SSFSTs) are amplified, and the track structure has strong low-frequency acoustic radiation; therefore, it is necessary to study the acoustic radiation of SSFSTs. In addition, multi-block short track structures are often laid within the URT lines; however, many researchers studying the reduction of vibration track service performance problems only select one or several block tracks to study. In reality, many short track structures will become sound sources when a train passes, and different sound sources will have various acoustic effects during the propagation process; therefore, it is necessary to study the rationality of any track acoustic model that analyzes the acoustic radiation problem. In order to more accurately predict the acoustic characteristics of steel-spring short floating-slab tracks (SSFSTs) on a one-span bridge, train-track-bridge interaction theory and the acoustic boundary element method (BEM) were adopted to study the acoustical differences and mechanism of the float-slab number in the acoustic model. The results showed that with the increase in the floating-slab number in the acoustic model, the acoustic radiation ability of SSSFSTs and the sound pressure in the sound field increased; however, it was not a simple linear increase. Thus, the floating-slab number in the acoustic model not only affected the acoustic radiation ability but also caused acoustic effects during the propagation process, which affected the predicted results. The vibration characteristics of each floating-slab were different, and the acoustic input conditions of different numbers of floating-slabs in the acoustic model led to significant differences in the acoustic analysis. There was also obviously a different acoustic contribution of each floating-slab to the same sound field point, which led to the significant influence of the sound pressure at the sound field points when using different acoustic models. Therefore, using acoustic models with different floating-slab numbers had a significant effect on the acoustic analysis of SSSFSTs. In order to study the acoustic characteristics of SSSFSTs on a one-span bridge, it was necessary to establish a complete acoustic model.  
urban rail transit; elevated box-girder bridge; steel-spring floating-slab track; track structure acoustical model; acoustic and vibration characteristics
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 3 Results  
### Results of the Medical Imaging Data Exchange System  
Using the HAPI FHIR server composed based on the HL7 medical data standard, the results of transmitted XML information from ImagingStudy, Patient, and Endpoint were confirmed. Data were successfully saved only in cases in which the actual resource structure classified by the FHIR was used, and its results can be seen in Figure 6.  
Figure 5: Addition of Phantom Resource using Specim Resource.  
Figure 6: Example of ImagingStudy Resource XML Transmission Results.  
In the case of the ImagingStudy resource, the ID references the Patient, Endpoint, and Specimen resource. The referenced IDs are 70104 for the Patient resource, 70103 for the Endpoint resource, and 70107 for the Specimen resource. The Specimen resource was used as the resource to store information regarding the phantom. Below the respective section, UIDs related to the series and instance are included as information on the medical imaging.  
In the Patient resource sample XML transmission results in Figure 7, the id 70104 can be seen as referenced in the ImagingStudy in Figure 6. Moreover, basic patient-related information is included.  
In the Endpoint resource sample XML transmission results in Figure 8, the data related to Endpoint id 70103 referenced by the ImagingStudy resource in Figure 6 can be seen. In this resource, the server address where the medical image is saved and information regarding the server's connection method can be confirmed.  
Figure 8: Example of Endpoint Resource XML Transmission Results.  
Figure 7: Example of Patient Resource XML Transmission Results.  
In Figure 9, the specimen resource example of the XML transmission results using the Phantom resource includes information on the phantom using the Extension. The Specimen id 70107 referenced by the ImagingStudy resource in Figure 6 can be deduced as the relevant data.  
Each data element complies to the StructureDefinition rules set within the server when designing the Phantom resource. As such, it includes the StructureDefinition URL information that defines each element per extension that relates to the phantom data element.
# RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment
## 3 Proposed Framework for Cricket Game Strategy and Field Placement Optimization  
This section provides a detailed overview of the RunsGuard framework proposed for this research, delineating its utilization from data acquisition through strategic planning to enhance performance in cricket matches. The proposed framework depicted in Figure 1 comprises of four major steps that illustrate the overall architecture. The first step (Section 3.1) is to obtain ball-by-ball commentary data from various sources and compile them through cleaning, normalization, and segmentation. For our study, text commentary data were carefully selected from two major sources, ESPNcriinfo [43] and Cricbuzz [44] based on a set of well-defined criteria to ensure the robustness and relevance of our analysis. The criteria include:  
* Relevancy: Ensuring that commentary data closely reflects match events and player actions.
* Comprehensiveness: Ensuring coverage of every ball across a wide range of matches.
* Accuracy: Relying on known sources for accurate reporting.
* Availability: Prioritizing data sources that offer extensive historical archives.
* Timeliness: Including recent and historical matches.
* Consistency: Maintaining a consistent format and level of detail.
* Diversity: Covering different leagues, tournaments, and match situations.  
By adhering to these criteria, our selection of text commentary data aims to provide a foundation for a detailed analysis, contributing valuable insights to the field of sports analytics.  
The second step (Section 3.2) involves the extraction of information using natural language processing (NLP) techniques with conditional random fields (CRF) on the compiled commentary data. The third step (Section 3.3) is a pattern and trend inspection stage, which is carried out by the analyzer to inspect the features and translate them in terms of the strengths and weaknesses of a player. The performance index (PI) in Equation (4) given here is computed in this step based on the total runs scored with specific identified patterns and the frequency ratio (FR) stated by Equation (3) through the occurrence of these patterns. Finally, Strategy Planner Step (Section 3.4) recommends the placement of fielders for bowling strategies based on PI, FR, constraints, and contextual factors using a mathematical model. This model aims to minimize PI while increasing FR, thereby improving the performance during cricket matches. The operational procedure for each step is as follows:
## 3 Materials and Methods
### The APP Feedback Case
#### 3.3.3 LLRs with APP Feedback  
In Section 2.2 it was shown that when extrinsic feedback is utilised at the SISO, LLRs from previous iterations are simply a scaled noisy version of the current LLRs, hence nothing is to be gained using previous data. At this point we address the question \"how does the analysis of Section 2.2 differ for the APP case?\". Following the same line of reasoning shown in Section 2.2, we start with the mean and variance at the PIC output, which for the APP case are given in (36) and (40). We now make the LLR conversion for the APP case utilizing (10) and (11) and then can group similar terms and conclude:  
\[\lambda_{1,k,a}^{(n)}=2\frac{\tilde{n}_{k,a}^{(n)}}{(\hat{\sigma}_{k,a}^{(n)} )^{2}} \tag{41}\]  
\[\omega_{k,a}^{(n)}=\frac{\sqrt{\tilde{n}_{k,a}^{(n)}}}{\hat{\sigma}_{k,a}^{(n)} }\left(\sum_{j\
eq k}c_{jk}(x_{j}-\hat{x}_{j,a}^{(n-1)})+z_{k}\right). \tag{42}\]It is noted that since \(\hat{n}_{k,a}^{(n)}\) is not unity, our terms in (41) and (42) differ from the extrinsic case. If the consistency property of Section 2.2 is maintained, the notion of APP feedback does not affect the expression for \(\Lambda_{3,k,a}^{(n)}\), however we must substitute APP signal terms where appropriate. The expression for the \(\varphi_{k,a}^{(n)}\) and the correlation factor \(\rho_{k,a}^{(n)}\) are evaluated as;  
\[\varphi_{k,a}^{(n)}=\frac{\lambda_{1,k,a}^{(n)}\omega_{k,a}^{(n)}\delta_{1,k}^ {(n)}+\sum_{i=1}^{n-1}\prod_{j=i+1}^{n}\lambda_{1,k,a}^{(i)}\omega_{k,a}^{(i) }\delta_{1,k}^{(i)}\delta_{2,k}^{(j)}}{\lambda_{3,k,a}^{(n)}} \tag{43}\]  
\[\rho_{k,a}^{(n)}=\mathcal{E}\Bigg{[}\frac{\lambda_{1,k,a}^{(n-1)}\omega_{k,a} ^{(n)}\omega_{k,a}^{(n-1)}\delta_{1,k}^{(n-1)}+\sum_{i=1}^{n-2}\prod_{j=i+1}^{ n-1}\lambda_{1,k,a}^{(i)}\omega_{k,a}^{(n)}\omega_{k,a}^{(i)}\delta_{1,k}^{(i)} \delta_{2,k}^{(j)}}{\lambda_{3,k,a}^{(n-1)}}\Bigg{]} \tag{44}\]  
A Table 3 is given with system and maximum number of users. Table 3 gives a brief summary of the type of performance improvements that can be achieved via the application of MRC to a coded multiuser receiver. It is given with a comparison for a maximum number of users in case of PIC for various systems and probabilistic data association (PDA) system for MRC and truncated MRC (TMRC).
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
### Optical Compensation
#### 1.1.3 High Order Mode Fibers  
This procedure is used to remove signals within the fiber with high transmit intensity as the data modulated signals and received output gets affected by the non-linearity associated with high intensity signals. Similar to other techniques, the inverse dispersion property is used to cancel the dispersion effects as it makes use of the high order mode (HOM) [12], which provides a negative dispersion value and a slope over the C band of \(-270\) ps/nm/km at transmission wavelength of 1550 nm and \(-5.6\) ps/nm/km, respectively [13]. HOM has a wide band operation range and is used for dense wavelength division multiplexing (DWDM) systems where multiple wavelengths (covering a large span) are combined with channel spacing of several GHz [14]. Besides the above-mentioned usage of HOM in fiber optics, it includes, not exclusively, usage for space-division multiplexing in OCNs, group velocity dispersion (GVD) compensation, sensing, etc. Initially, the use of HOMs for optical communication was considered within conventional single mode fiber (SMF). However, as researchers exploited and optimized multiplexing in time, wavelength, polarization and phase to reach more capacity for the ever-growing traffic demand [15], HOMs are now used in other types of fibers, which support spatial combination of several modes. Generally, space-division multiplexing operates with two regimes: multicore fibers and multimode fibers. A crucial factor in both schemes is the ability to separate the channels to limit the cross talk. The multicore fiber is not strictly within the regime of HOMs but is nonetheless briefly reviewed. The multicore fiber has a number of single mode cores, which may either work in a decoupled manner or a coupled manner with a number of super modes. Up to 30 separate cores [16] within a limited fiber diameter (200 \(\upmu\)m) have been demonstrated. This is, however, a constantly improving number [17]. In this paper, multi-channel OCN is proposed, where CD and PMD losses are mitigated by implementing decision feed forward equalization (FFE) [18] and decision feedback equalization (DFE) [19] techniques. FFE and DFE techniques are combined in an electronic equalizer block to be used for dispersion mitigation in the presented model of an OCN.
## Nomenclature  
\begin{tabular}{l l} _Sets/Indices_ & \\ \(l/\Omega^{l}\) & Set/index for feeder \\ \(g/\Omega^{g}\) & Set/index for power unit \\ \(t/\Omega^{T}\) & Set/index for hour \\ \(b,m/\Omega^{b,m}\) & Set/index of bus bar \\ _Limitations_ & \\ Z & Solar energy \\ \(RL^{loss}\) & PV losses \\ \(U_{l}^{V}/S\) & Wind speed/density \\ \(X_{min}^{V},X_{wind}^{V}\) & Tidal cut-in speed and rated speed \\ \(Q\) & Direct irradiation \\ \(\gamma\) & Water density \\ \(P\) & Seawater density \\ \(\lambda\) & Brushed area of the turbine blades \\ \(C\) & Rotor blade area \\ \(X_{l}^{t}\),\(R_{l}\),\(X_{l0}^{t}\) & Technical characteristics of line \\ \(S_{L_{g^{\prime}}}D_{t,g}\) & Unit start-up, shut down \\ \(E_{l}^{PV}\) & PV Capacity \\ \(P_{g,t}\), \(Q_{g,t}\) & Smart grid demand \\ \(R_{t}\) & Power transaction price \\ \(X_{l}^{V}\) & Current speed of tidal \\ \(P_{lT}^{T}\) & Rated generation of tidal \\ \(V^{min}\), \(V^{max}\) & High/low limits for storage system \\ \(P_{l}^{load-S}\) & Microgrid demand \\ \(P_{min}^{min}\), \(P^{max}\) & Active power limitations \\ \(P_{l}^{min}\), \(P_{l}^{max}\) & Line active power limitations \\ \(Q_{l}^{min}\), \(Q_{l}^{max}\) & Line reactive power limitations \\ \(Q_{l}^{min}\), \(Q_{l}^{max}\) & Reactive power limitations \\ \(D_{G}^{+}\), \(D_{G}^{-}\) & Up/down Limits of reserve \\ \(TV^{min}_{min}TV_{max}\) & Limits of voltage \\ \(\eta_{min}^{min}\), \(\eta_{b}^{max}\) & Limits of angle \\ \(P_{h,t}^{load}\) & Smart grid active demand in each bus \\ \(Q_{c,t}^{Load}\) & Smart grid reactive demand in each bus \\ \(r^{c}\) & Generation price of the generator. \\ \(RW_{t}\), \(RT_{t}\), \(RV_{t}\), \(RB_{t}\) & Bidding offer for WT, tidal, PV and battery \\ \(P_{g,t}^{max}\), \(P_{g}^{min}\), \(Q_{g}^{max}\), \(Q_{g}^{min}\) & High or low limit of the power transaction \\ \(z,w\) & Mean and variance \\ \(W^{0}\) & weight of the mean value \\ \(A_{aa}\) & covariance matrix \\ \(c\) & Number of uncertain parameters \\ \(R\) & vector of stochastic inputs \\ _Variables_ & \\ \(P_{t}^{B}\), \(P_{t}^{W}\), \(P_{t}^{TI}\), \(P_{t}^{PV}\) & Generation amount of storage, WT, tidal and PV \\ \(B_{b}^{k}\) & Line outage index of bus b \\ \(VIB_{b}^{k}\) & Bus index \\ \(L_{l}^{k}\) & Line outage index of line l \\ \(VII_{l}^{k}\) & line index \\ \(G_{b}^{k}\) & unit outage active index of line l \\ \(VIG_{g}^{k}\) & unit index \\ \(Q_{g}^{k}\) & unit outage reactive index of line l \\ \(VIQ_{g}^{k}\) & unit index \\ \(Q_{l,g^{\prime}}\), \(Q_{l,t}\) & Generator and feeder reactive power at time t. \\ \(P_{t,g^{\prime}}\), \(P_{l,t}\) & Generator and line active power at time t. \\ \(MP_{t}^{TS}\) & Power trade between microgrid and smart grid. \\ \end{tabular}  
\[\begin{array}{ll}P_{t}^{b},P_{t}^{dis}\\ zx_{t,g}\\ TV_{b},\eta_{b}\\ V_{t}^{B}\\ \end{array}\begin{array}{ll}&\text{Charging/Discharging rate.}\\ &\text{Binary variables of the generator.}\\ &\text{Bus voltage or angle.}\\ &\text{Energy of battery.}\\ &\text{Operation cost functions of smart grid and microgrid.}\\ \end{array}\]
# Structural Optimization Design of Spaceborne Microwave Probe Antenna
The conclusion emphasizes the combined benefits of topology and size optimization, resulting in reduced mass and rotational inertia, improved dynamic stiffness, and better control precision, which are suitable for high-stability satellite attitude control. This work presents a novel method combining topology and size optimization to enhance dynamic stiffness and reduce the rotational inertia of the antenna's reinforcements. By maximizing the first-order frequency and minimizing torque disturbances, this approach improves both performance and stability. It offers a more precise and scientifically supported design, moving beyond traditional, experience-based methods. This contributes to better satellite attitude control and system stability.
###### Abstract  
Current methods for identifying unknown Internet of Things (IoT) devices are relatively limited. Most approaches can identify only one type of the unknown IoT devices at a time and with a relatively low accuracy. Herein, we propose a method for unknown IoT device identification (UDI) based on cost-sensitive contrastive loss (CSCL)-Siamese networks and a weighted-voting clustering ensemble (WVE). First, we integrate data visualization techniques with a permutation sample-pairing strategy to generate a complete and nonredundant set of positive-negative sample pairs. Then, we present an algorithm to generate permutation positive-negative sample pairs to provide a rich set of contrastive training data. To overcome the bias in the decision boundary caused by an insufficient number of positive sample pairs, we developed a Siamese network based on CSCL. The CSCL-Siamese network is used to identify known IoT devices and establish an embedded vector database for known IoT devices. Next, we extract the embedding vectors of unknown IoT devices using the trained CSCL-Siamese network and the embedded vector database. Finally, combining weighting factors with a voting ensemble strategy, we develop a UDI algorithm based on a WVE. This presented algorithm integrates the clustering capabilities of multiple unsupervised clustering algorithms to perform clustering on the extracted embedding vectors of unknown IoT devices, thereby enhancing the identification capability of the CSCL-WVE-UDI method. Experimental results demonstrate that the CSCL-WVE-UDI method can effectively identify multiple types of unknown IoT devices at the same time.  
network traffic; positive and negative pair samples; ensemble learning; unknown IoT device identification +
Footnote †: journal: Applied