esa-sceva/satcom-chunk-collection huggingface dataset:
# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 2 Materials and Methods
### Used Dataset  
The dataset used is related with energy consumption readings for a sample of 5567 London households between November 2011 and February 2014. These readings carried out from UK Power Networks within the scope of the Low Carbon London project (here called the _LCL dataset_) [23]. Readings were taken at half hourly intervals and represent the total electricity consumption for that period in kWh. Despite a large number of consumers available in this study, the authors carefully analysed the dataset and imposed specific assumptions to ensure representative and reliable data. The following rules were created to select the consumers who contribute with data to be used in the further phase related to forecasting application.  
The ratio of null or atypical values in the total amount of records available for the considered period (28 months) must be kept below 5%. Despite some reasons that may cause null values (such as power outages, or temporary absence of the households in the consumption locations) or may cause atypical values (outliers due to sudden and abrupt changes typically caused by data transmission/communication problems), it is important to preserve data integrity to enhance the forecasting exercise.  
For each consumer, the consumption trend should be kept uniform for the different complete civil years available. It is expected that consumption variations may occur during the year due to seasonality effect, but significant changes in annual total consumption may be caused by unexpected consumer behaviour changes, measurement failure or even trouble in data transmission/storage. Based on mean daily consumption, differences above 10% from year to year have triggered less uniformity in the consumption trend.  
After this filtering process, the dataset was considerably summarized to 71 consumers, providing large confidence in the quality of available data (for almost the whole period between November 2011 and February 2014, as some initial and final periods of data were not clearly trustworthy). In the authors' opinion, this selected sample is representative enough to be explored, allowing a benchmark among the proposed forecasting tools. Table 1 summarises the LCL dataset available information, and the selected sample used.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 3 Main Model
### Path Instance Embedding  
Meta-paths provide special scene information, which includes link information between nodes and node attribute information. Learning the display representation of meta-paths can capture the scene information provided by meta-paths and can be used to explore the correlation between meta-paths for better extraction of information in heterogeneous networks for a recommendation. How to obtain high-quality path instances based on meta-paths is the key to efficient extraction of the meta-path information. The existingmeta-path information extraction based on heterogeneous information networks mainly uses a random wandering strategy to generate path instances, which are sampled with equal probability to the output nodes. However, the path instances sampled by such simple random wandering are generally of poor quality and are not suitable for a recommendation. Therefore, the priority is measured by calculating the similarity of feature vectors between different nodes, then the average value of the calculated similarity between nodes is ranked, and finally, the \(K\) path instances with the highest average similarity are obtained and input to the recommendation model.  
The path instance of a meta-path is a sequence of entity nodes, and a convolutional neural network (CNN) is used to convert the path instance into a low-dimensional feature vector. Given a path instance \(x_{m}^{1}-x_{m}^{2},\ldots,x_{m}^{n}\) where \(x_{m}^{i}\in R^{d\times 1}(1\leqslant i\leqslant n)\) is the dimensional embedding of each node in the path instance, \([x_{m}^{1}-x_{m}^{2},\ldots,x_{m}^{n}]^{T}\) denotes the representation matrix stitched together from the node sequence, \(\Theta\) denotes all relevant parameters in the CNN, and the process of learning the path instance representation is as follows:  
\[x_{m}=CNN\Big{(}[x_{m}^{1}-x_{m}^{2},\ldots,x_{m}^{n}]^{T},\Theta\Big{)} \tag{3}\]  
The meta-path can generate multiple path instances, so the embedding of the meta-path is generated by the maximum pooling operation and lets the embedding of \(K\) path instances obtained under the meta-path \(P\) be \(x_{1},x_{2},\ldots,x_{k}\), resulting in the meta-path embedding as  
\[c_{\rho}=max\text{-}pooling(x_{1},x_{2},\ldots,x_{k}) \tag{4}\]
# Experimental Study on Partially Coherent Optical Coherent Detection
## 3 Experimental Study  
### Experimental Setup  
Figure 5 illustrates the layout of an experimental schematic system that was constructed indoors to confirm the impact of partially coherent EGSM beams on system performance with various polarization states. The components that make up the system are as follows: a laser, polarizer, transmitting/receiving antenna, 90-degree mixer, oscilloscope, rotating ground glass, convex lens, beam splitter, etc.  
Figure 5's optical path indicates how the local oscillator light and signal light are set up, respectively. The laser used in this experiment has an output wavelength of 1550 nm. During the optical path construction process for the experiment, a 650 nm laser with a visible light wavelength is first used to cross the axis, because light at the former wavelength is not visible to the human eye. Next, a precise two-dimensional adjustment frame made of azimuth and elevation adjustments is used to move the laser forward and backward along the beam propagation axis. After collimation, an observation screen is placed behind the laser and moved back and forth to observe the changes in the spot. It is deemed successful  
Figure 4: Relationship between mixing efficiency, circular polarization, and elliptical polarization.  
collimation and the rough adjustment is finished if there is virtually no change in the spot diameter over a wide range.  
As shown in Figure 6, after passing through the linear polarizer P1, the laser passes through the Mach Zehnder interferometer. At the output of the Mach Zehnder interferometer, two mutually perpendicular polarized beams are combined and subsequently directed towards a rotating ground glass plate. This interaction results in the generation of a random electromagnetic beam. Lens 3 effectively alters the intensity distribution of the uniform light spot emitted from the ground glass, resulting in the creation of an EGSM beam with an approximate Gaussian distribution. The linear polarizer P1's transmission direction and the \(x\)-axis form an angle, and modifying the angle modifies the amplitude ratio of the field Ax/Ay.  
The size of the light spot projected onto the rotating ground glass sheet is adjusted using L1 and L2. Both the size of the light spot on the ground glass sheet and the roughness of the ground glass determine the size of \(\delta_{ij}\). The process of creating ground glass involves gradually pulverizing regular glass using diamond sand, and the size of the diamond sand particles largely dictates the surface roughness of the glass. The co  
Figure 5: Experimental schematic diagram. C1, C2: collimators; P1, P2: polarizers; PBS, polarizing beam splitter; M, mirror; L1–L7, lens; RGG, rotating ground glass.  
Figure 6: Experimental setup for generating EGSM signal light.  
significantly affected by the ground glass's rotational speed, according to research [20; 21]. To construct the optical path for the local oscillator, the same process was used, As shown in Figure 7. The specific parameters of the experimental equipment are shown in Table 1.
## 3 Results and Discussion  
### Crystallinity  
The crystallinity of the prepared samples was investigated by obtaining the corresponding XRD patterns, as exhibited in Figure 1. It is obvious that all samples display diffraction peaks occurring at 20 values of 25.2, 37.9, 48.1, 62.8 and 75.16 degrees, corresponding to \(d_{101}\), \(d_{004}\), \(d_{200}\), \(d_{204}\) and \(d_{215}\) of anatase phase, [16; 42] with no additional peak or peak shifting being observed upon urea addition. However, few peaks of rutile phase with low intensity are also observed appearing at 20 values of 54.64, 56.56, 69.26, 69.83 degrees corresponding to \(d_{211}\), \(d_{220}\), \(d_{301}\), \(d_{112}\) of rutile phase, respectively [43; 44]. Furthermore, all the peaks of anatase phase are increased with the increment of the urea amount. Such an effect can be explained based on the pH increment that occurs with the addition of the urea in solution, promoting the formation of anatase phase. When urea is dissolved in ethanol solvent, the pH value of the solution increases. Further on addition of TITP, the urea can react with H\({}_{2}\)O to produce ammonium hydroxide (NH\({}_{4}\)OH), carbon dioxide and ammonia gas [45]. The ammonium hydroxide can be dissociated to NH\({}_{4}\)\({}^{+}\) and OH\({}^{-}\) to hydrolyze Ti\({}^{4+}\), and the presence of ammonia increases the pH of solution, which finally results in higher anatase phase composition. Thus, urea simultaneously promotes the formation of anatase phase as well as nitrogen doping of TiO\({}_{2}\).  
Further investigation of the crystalline phase and Raman modes of vibration were analyzed using Raman spectroscopy as shown in Figure 2. It can be observed that all samples exhibit Raman bands at 148 cm\({}^{-1}\), 400 cm\({}^{-1}\), 515 cm\({}^{-1}\) and 640 cm\({}^{-1}\) which mainly corresponds to anatase TiO\({}_{2}\)[16; 46]. The intensity of all the mentioned peaks intensifies with the increase of urea concentration as it goes from pure TiO\({}_{2}\) to NT-3 sample, thus confirming the urea effect for anatase phase enrichment. Moreover, the increased intensity of all the Raman peaks can be ascribed to the enhanced crystallinity along with enriched anatase phase.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 6 Discussion
### Computational Complexity Analysis  
One of the main advantages of the proposed imaging method is its high computational efficiency. In this subsection, its computational complexity is discussed and compared with that of the method proposed in [19].  
For the proposed method, since the range compression, SRC, and RCMC can all be implemented via phase multiplications in the 2-D frequency domain, one can utilize a phase multiplication to accomplish these operations. Therefore, based on the analysis presented in Section 3, one can see that the proposed method needs only two azimuth FT/IFTs, two range FT/IFTs, and twice phase multiplications. The computational complexity can then be given by  
\[10N_{a}N_{r}\log_{2}(N_{a})+10N_{a}N_{r}\log_{2}(N_{r})+12N_{a}N_{r}, \tag{47}\]  
where \(N_{a}\) and \(N_{r}\) are the number of azimuth samples and range samples of the data, respectively.  
The imaging method proposed in [19] requires four range FT/IFTs, two azimuth FT/IFTs, four phase multiplications, and one azimuth interpolation. Supposing that the length of sinc interpolation kernel is \(M\) (\(M\) is set to be 16 in [19]), the overall computational complexity of this method is then  
\[10N_{a}N_{r}\log_{2}(N_{a})+20N_{a}N_{r}\log_{2}(N_{r})+24N_{a}N_{r}+6M\cdot N _{a}N_{r}, \tag{48}\]  
From (47) and (48) one can see that the proposed method is more efficient, because there are no interpolation and less range FTs/IFTs and phase multiplications.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Database Description  
In this research article, we have used the Wi-Fi crowdsourced fingerprint database created by a research group from Tampere University of Technology (TUT) [13]. The database and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository [55]. The measurements were taken in a five-floor building in Tampere (23.85580\({}^{\circ}\) N, 61.44585\({}^{\circ}\) E), Finland. The five-floor building has a footprint of about 22,570 m\({}^{2}\) (208 m \(\times\) 108 m). Total 4648 fingerprints were collected which were then split uniformly randomly 15% for the training and 85% for the test purpose. Volunteer users installed the android application on their devices and reported the correct location (based on a manual input on the map) to the server. The server stored the location reported by the user, the time stamp, the device model, the MAC addresses of the heard access points and the RSS from each AP. A local coordinate system e.g., \((x,y,z)\) = (123.45, 14.71, 0) was used instead of the global WGS84 commonly used in other applications. During the measurements, a total of 991 AP were heard, the exact position of the APs was not known. Figure 7 shows the fingerprint database.  
According to the benchmark result provided by the TUT research group, the mean 2D and 3D positioning errors of this database were in the order of 8-10 m in the best cases. They made it deliberately challenging by keeping the training data very low, only 15% (i.e., 0.03 fingerprints/m\({}^{2}\)). The fingerprints locations were recorded manually and accurately, so the physical distance between fingerprints (in other senses, devices) according to the coordinate value are true. To the best of the authors' knowledge, this database or any other standard fingerprint database available on the internet does not contain TOA information among the fingerprints. We have emulated true physical distance among the fingerprints as TOA-based distance.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 2 Related Work
### Statistical Heterogeneity and Drift Metrics
#### 2.1.1 Canonical FL Baselines  
Canonical FL baselines serve as yardsticks for assessing new federated-learning methods. We compare our approach to eight widely used algorithms: FedAvg [1], FedProx [3], SCAFFOLD [4], FedNova [8], FedCurv [6], FedDyn [5], the adaptive-server optimisers FedAdam and FedYogi [9], and the personalised scheme Ditto [20]. These baselines differ mainly in how they mitigate client heterogeneity and communication costs, yet all share the goal of learning a global model without centralising data. Concise descriptions of each method are provided in Section 4.  
Synthesis and Open Gaps  
The above baselines fall into three camps: (i) _variance-reduction methods_ (SCAFFOLD and FedNova) that curb gradient noise but increase communication load; (ii) _regularisation-based defences_ (FedProx, FedDyn, and FedCurv) that tether clients to a reference point yet add tunable hyper-parameters or additional client state; and (iii) _server-side accelerators and personalisation layers_ (FedAdam, FedYogi, and Ditto) that hasten convergence or improve local fit while leaving non-IID robustness largely implicit. None of these families supplies an information-theoretic bound linking update diversity to generalisation, and several require per-client metadata that scales linearly with the federation size.  
ERFO complements existing baselines by adding a _single-scalar_ entropy regulariser that _provably upper-bounds_ the Kullback-Leibler divergence between each round's aggregated update distribution and its pigeonhole-theoretic optimum; (ii) adds no auxiliary vectors, dual variables, or per-client models; and (iii) slots into vanilla _FedAvg_ with one extra gradient computation per local epoch. As such, it addresses the open gap of theory-grounded drift control without increasing communication or storage overhead, positioning ERFO as a lightweight yet principled alternative to the current state-of-the-art method.
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## 3 Simulation Results and Discussion  
### Fluid-Structure Interaction Effect  
Pressure contours caused by the explosive shock wave at various instants during propagation are shown in Figure 3. The gas is detonated at an initial time and the explosive shockwaves expand quickly from one end of the tunnel to the front surface of the chamber. Before the shockwave reaches the chamber, the peak pressure value of the incident wave is about 0.26 MPa at 146 ms (in Figure 3a). As the shockwave reaches the chamber, the peak pressure increase rapidly and the value is about 0.64 MPa at 151 ms (in Figure 3b). This is because of the sudden interaction between the incident wave and the reflected wave. Then, the pressure immediately decreases due to the energy of shockwave absorption by the refuge chamber, multiple reflections, and diffractions by the tunnel wall. At 194 ms, the peak pressure on the chamber decreases to 0.41 MPa (in Figure 3c). Then, the pressure on chamber gradually reduces and closes to a nearly constant value of 0.15 MPa (in Figure 3d).  
In order to obtain the pressure distribution of the shockwave by a gas explosion on each surface of the refuge chamber structure, some testing points are selected on the front, side, and top surfaces of the refuge chamber, respectively. Figure 4 shows the locations of each testing point. Figure 5 shows the pressure-time curves obtained from different testing points. In Figure 5a, the pressure value at the front surface of the refuge chamber reaches a maximum of 0.64 MPa at 150 ms. Figure 5b,c show that the peak pressure on the side and upper surface gradually reduce along the distance and the pressure value which is measured close to the front ranged between 0.15 and 0.19 MPa is almost 1/3 of  
Figure 3: Pressure contour during the blast wave propagation in the tunnel. (**a**) \(t=146\) ms; (**b**) \(t=151\) ms; (**c**) \(t=194\) ms; (d) \(t=500\) ms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Steel**} & **Elastic** & **Poisson’s** & **Density \(\rho\)** & \(\sigma_{y}\) & \(\gamma\) & \multirow{2}{*}{**m**} & **Tangent Modulus** & **Failure** \\  & **Modulus (MPa)** & **Ratio \(\mu\)** & **(kg/m\({}^{3}\))** & **(MPa)** & **(s\({}^{-1}\))** & **Etan (MPa)** & **Strain** \\ \hline Q235-B & \(2.0\times 10^{5}\) & 0.3 & 7850 & 235 & 40.4 & 0.5 & \(2.0\times 10^{3}\) & 0.2 \\ Q345-B & \(2.0\times 10^{5}\) & 0.3 & 7850 & 345 & 40.4 & 0.5 & \(2.0\times 10^{3}\) & 0.2 \\ Q460-B & \(2.0\times 10^{5}\) & 0.3 & 7850 & 460 & 40.4 & 0.5 & \(2.0\times 10^{3}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of steel materials.  
that on the front surface. Since the shockwave diffracts around the refuge chamber, a secondary peak pressure occurs.  
Figure 4: The distribution of testing points.  
Figure 5: _Cont._
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 4 Simulator and Field Experiment
### Wireless Sensor Network Experiments
Thus, performing a distance measurement between two nodes prevents them from receiving messages from other nodes on the network. Therefore, the nodes aimed at measuring the distance at different times and as needed. That way, it was possible to facilitate network traffic and reduce the number of instances where nodes were not attentive to the network messages. Figure 10: The payload for the long-distance ranging experiment included the LoRa SX1280 2.4 GHz and the LoRa 433 MHz for backup. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GPS** & **LoRa SX1280** & **Difference** & **Time Shift** & **Accuracy** \\ (**Meters)** & **(Meters)** & **(Meters)** & **(Seconds)** & \\ \hline
1780 & 1980 & 200 & 12 & 92.93\% \\ \hline
2130 & 2404.6 & 274.6 & 29 & 91.26\% \\ \hline
13,210 & 13,453.7 & 243.7 & 17 & 98.75\% \\ \hline
21,530 & 21,698.8 & 168.8 & 11 & 99.64\% \\ \hline
22,170 & 22,325.1 & 155.1 & 11 & 99.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The LoRa SX1280 2.4 GHz distance measurement compared to the GPS using a long-range weather balloon.
## 1 Introduction  
Transparent metal oxide-based thin film transistors (TFTs) have attracted substantial attention as backplane technology for next-generation active matrix display applications. In particular, amorphous InGaZnO (a-IGZO) TFTs have been extensively studied because they show attractive characteristics including desirable channel electron mobility, large-area uniformity, and low off-state leakage compared with conventional a-Si:H TFTs [1, 2]. However, high density localized states originating from oxygen vacancies (O\({}_{\text{V}}\)) exist within the bandgap of the a-IGZO active layer due to the disordered amorphous nature, which would considerably degrade the device performance and reliability [3, 4]. It has been demonstrated that the threshold voltage instability in a-IGZO TFTs induced by electrical, light, and thermal stress is generally related to the O\({}_{\text{V}}\) defects trapping electrons or holes within the a-IGZO active layer and at the device interface region [5, 6, 7]. Hence, suppressing the O\({}_{\text{V}}\) defects in the active layer or at the interface is crucial to enhance the reliability of a-IGZO TFTs.  
In previous reports, the nitrogen has been used to passivate O\({}_{\text{V}}\)-related defects within a-IGZO by forming N-metal (In, Ga and Zn) bonds [8, 9]. For example, the ambient stability of N-doped a-IGZO TFTs can be enhanced by the mitigation of the oxygen absorption/desorption behavior due to the substitution of the O atom by an N atom within the a-IGZO [10]. Moreover, the a-IGZO TFTs fabricated with N-doped a-IGZO layer inserted at the a-IGZO/SiO\({}_{2}\) interface exhibit superior bias stability, which is improved by the passivation of the interface O\({}_{\text{V}}\) defects [11]. However, excess N incorporation into the a-IGZO channel layer would induce extra O\({}_{\rm V}\)-related defects or N-related defects within the active layer or at the channel/dielectric interface, which would cause the degradation of device performance and electrical reliability [12; 13]. In this work, to achieve an optimal level of nitrogen doping in the active layer, a-IGZO TFTs with various nitrogen/oxygen partial pressure ratios (P\({}_{\rm N/O}\)) during active layer deposition are fabricated. The electrical characteristics of the fabricated devices are investigated under positive-bias-stress temperature (PBST) and negative-bias-stress illumination (NBSI). The TFT fabricated with a proper P\({}_{\rm N/O}\) exhibits improved reliability with decreased threshold voltage drift (\(\Delta V_{th}\)) after PBST and NBSI conditions. Such improvements are related to the passivation of O\({}_{\rm V}\) defects at the a-IGZO/SiO\({}_{2}\) interface.