esa-sceva/satcom-chunk-collection huggingface dataset:
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) PESQ**} & \multicolumn{4}{c}{**(b) segSNR**} \\ \hline
**SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) & **SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) \\ \hline \(-6\) & 1.53 & 2.00 & 2.12 & 2.06 & **2.22** & \(-6\) & \(-6.87\) & 1.49 & 3.18 & 2.85 & **3.53** \\ \(-3\) & 1.71 & 2.23 & 2.35 & 2.30 & **2.45** & \(-3\) & \(-5.39\) & 3.06 & 4.31 & 3.93 & **4.92** \\ \(0\) & 1.90 & 2.44 & 2.57 & 2.52 & **2.66** & \(0\) & \(-3.65\) & 4.57 & 5.58 & 5.27 & **6.29** \\ \(3\) & 2.11 & 2.64 & 2.76 & 2.72 & **2.85** & \(3\) & \(-1.80\) & 6.08 & 7.03 & 6.79 & **7.86** \\ \(6\) & 2.33 & 2.83 & 2.95 & 2.90 & **3.02** & \(6\) & 0.32 & 7.41 & 8.33 & 8.14 & **9.20** \\ \(9\) & 2.54 & 2.99 & 3.10 & 3.05 & **3.17** & \(9\) & 2.57 & 8.64 & 9.56 & 9.38 & **10.43** \\ \hline Aver. & 2.02 & 2.52 & 2.64 & 2.59 & **2.73** & Aver. & \(-2.47\) & 5.21 & 6.33 & 6.06 & **7.04** \\ \hline \hline \multicolumn{10}{c}{**(c) eSTOI**} & \multicolumn{4}{c}{**(d) SDR**} \\ \hline
**SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) & **SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) \\ \hline \(-6\) & 0.44 & 0.56 & 0.59 & 0.57 & **0.61** & \(-6\) & \(-5.97\) & 7.07 & 7.96 & 7.22 & **8.75** \\ \(-3\) & 0.52 & 0.64 & 0.67 & 0.65 & **0.69** & \(-3\) & \(-3.11\) & 9.63 & 10.42 & 9.85 & **11.10** \\ \(0\) & 0.59 & 0.71 & 0.74 & 0.73 & **0.76** & \(0\) & \(-0.17\) & 11.92 & 12.67 & 12.16 & **13.21** \\ \(3\) & 0.67 & 0.77 & 0.80 & 0.79 & **0.82** & \(3\) & 2.80 & 14.06 & 14.71 & 14.27 & **15.14** \\ \(6\) & 0.74 & 0.82 & 0.84 & 0.84 & **0.86** & \(6\) & 5.78 & 15.81 & 16.42 & 16.03 & **16.81** \\ \(9\) & 0.80 & 0.86 & 0.88 & 0.87 & **0.89** & \(9\) & 8.78 & 17.34 & 17.94 & 17.56 & **18.24** \\ \hline Aver. & 0.63 & 0.73 & 0.75 & 0.74 & **0.77** & Aver. & 1.35 & 12.64 & 13.35 & 12.85 & **13.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (segSNR), extended short-time objective intelligibility (eSTOI), and signal-to-distortion ratio (SDR) values of the proposed and baseline networks in the matched noise conditions, where \(-6\) and \(9\) dB cases are unseen SNR conditions. Additionally, Figure 4 shows the spectrograms of an utterance enhanced by the \(snT\) and \(snDT\) models in the mismatched noise conditions. From this figure, it is shown that the proposed algorithm effectively reduced the noise from the original noisy speech while the speech distortion was minimized.
## 4 The Role of UAV-Base Station in Wireless Communications
### Emergency Services (Pop-Up Networks)  
UAVs are becoming one of the most active areas of research and industrial development due to their promising applications in different domains. One of the application areas for UAVs is emergency response [70]. During large-scale natural disasters such as floods, earthquakes, or major fire outbreaks, hurricanes, etc., several properties and infrastructure are normally destroyed, including cellular infrastructure, thereby leading to loss of communication services in the affected area [71]. In such situations, UAVs can be deployed to such areas to replace malfunctioning or destroyed BSs and provide emergency communication services. This helps to provide coverage and improve the QoS of users in the affected area. UAV parameters such as trajectory, altitude, etc., can be optimized based on the user traffic demand and distribution to maximize their throughput and coverage. In this regard, the work in [14] considered the deployment of UAVs as emergency pop-up networks to restore communication services to users in an area where cellular network infrastructure may have been destroyed due to natural disasters. They applied RL to optimize the trajectories of the UAVs in order to maximize the coverage and throughput of ground users.  
In addition, UAV-BSs can serve as relays to help connect isolated groups of users that are separated from each other. This is vital in disaster situations such as earthquakes or floods where a single community can be split into isolated groups. Under this scenario, the UAV-BSs allow the mobile devices of separated community members to communicate locally with one another, even though they do not have access to the internet. Similarly, UAVs can provide backhaul services to other existing wireless networks, in essence connecting such isolated communities to the global internet. UAV-BS can also serve as a viable backhaul solution for device-to-device (D2D) communications, as shown in [72]. They can also be equipped with efficient processors to enable real-time data aggregation from different devices and information processing from remote sensors, mobile devices, and other data collection and communication technologies (e.g., IoT) within an affected area.  
This real-time processing of information enables faster decision making by emergency response teams and other stakeholders because the quick response time and accurate decision making are vital to executing effective rescue mission during emergencies [73].
## 3 Results and Discussion
### Bending Test at 16 mm
#### 3.3.4 Energy Absorption  
The trend of absorption energy is shown in Figure 16. For each structure, the calculated mean energy value for the two replicates and their standard deviation were reported. As expected from the analysis of the results analyzed so far, the lozenge shows the highest values, and in the first cycle, it exceeds 40 mJ/g. The roller structure still offers good energy absorption values, exceeding 30 mJ/g. Moreover, the difference between the first and second cycles is relatively small, unlike the lozenge structure, which shows a 27% reduction in energy absorbed between the first and second cycles. The lozenge structure is also a good energy absorber [57], especially when the structure is improved by defining lateral and longitudinal reinforcement ribs. The obtained results are consistent with those reported by [23], in which PLA-based 4D-printed meta-sandwich structures demonstrated stable energy absorption and recovery behavior over multiple loading cycles. As observed for the lozenge and roller geometries in the present study, specific cell configurations were able to maintain structural integrity and performance repeatability even after repeated thermal activation.  
Figure 14: Lozenge (**a**) and roller (**b**) shape recovery bending test at 16 mm.  
Figure 15: Pre–post shape recovery.
# Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires
## 2 The Proposed Experimental Method to Degrade the Wire Samples  
The samples analyzed consisted of two artificially notched insulated twin wires (see Figure 1). The wire degradation process was based on a modified version of the degradation process described in the European Standard EN 3475-603 relating to test methods for determining the resistance to wet arc tracking of aircraft wires [30]. The main difference is that instead of running the entire degradation test for 8 h or to failure, whichever comes first, the degradation process used in this paper consisted of successive cycles of 30 min for each sample. This difference allowed the condition of the insulation to be measured after each 30 min degradation cycle. During each cycle, the two-wire specimens were supplied with 200 V\({}_{\text{RMS}}\) and 400 Hz from an AC power source according to EN 3475-603.  
Simultaneously, a NH\({}_{4}\)Cl solution was applied by the delivery system drop-by-drop to the sample test area, producing instantaneous surface discharges followed by arcing activity that progressively damaged the insulation. After each 30 min degradation cycle, the samples were rinsed with distilled water to ensure that the salt crystals formed on the surface of the samples were dissolved and washed away. The rapid evaporation of the electrolyte solution as a result of the surface discharge activity caused salt crystals to form. The small salt crystals tended to increase the electric field strength, causing surface discharge activity to occur at random locations. This affected the experimental results. After washing with distilled water, the samples were air-dried with forced air until there were no drops of water or moisture left on the wires.  
Figure 1 summarizes the steps of the experimental method proposed in this paper to establish a relationship between insulated wire degradation and surface discharge intensity using a CMOS sensor and a solar blind UV sensor as described in Section 4.  
Figure 1: Flowchart of the proposed wire degradation method.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 6 Conclusions  
This research focuses on the design of a moving-object-tracking scheme for logistics warehouses. It primarily encompasses the design of a deep learning-based detection algorithm for logistics warehouses and the design of experiments for tracking moving targets in logistics warehouses. The main contributions of the dataset, algorithm, and evaluation method are described below:  
* In order to solve the problem of moving-object tracking in logistics warehouses, a specific company was selected as the sampling object to make the dataset. The dataset encapsulated tracking outcomes for both goods and workers in video streams. The tracking model was further strengthened and applied to most logistics warehouse scenarios through the optimization of the dataset.
* YOLOv5s was selected as the base model on account of both detection accuracy and detection speed. Despite incorporating the DeepSORT network for multi-object tracking, the tracking speed remained sufficiently fast (within 30 ms). In addition, YOLOv5s boasted a compact size of 7.3 MB, thus holding potential for application in the actual logistics scene.
* The multi-objective evaluation metrics from the MOT Challenge were employed as the performance evaluation metrics of the logistics warehouse object-tracking system, assessing the degree of improvement of the tracking effect.
* In the detection test, the [EMAIL_ADDRESS] achieved an impressive score of 0.976, demonstrating the high accuracy of the object-detection model. Moreover, the tracking test yielded a commendable MOTA of 75.8% and a Rcll of 86.6%, validating the effectiveness of the tracking algorithm. Moreover, the method of transfer learning for trained DeepSORT models is introduced.  
**Author Contributions:** Conceptualization, T.X. and X.Y.; methodology, T.X.; software, T.X.; validation, T.X.; formal analysis, T.X.; investigation, T.X.; resources, T.X.; data curation, T.X.; writing--original draft preparation, T.X.; writing--review and editing, X.Y.; visualization, T.X.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515010095, 2021A1515010506), the National Natural Science Foundation of China, and the Royal Society of Edinburgh (51911530245).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Method Limitations and Ways to Address Them  
One of the method's limitations is that multiple normally-open appliances could not be disaggregated correctly. In the aggregation power clustering step, if multiple normally-open appliances contribute to the total power, the clustering method could not cluster to obtain the total power values when the largest contribution appliance is running. In order to overcome the limitation, after all the ON-OFF appliances are identified, the clustering phase is skipped, and the total power values are treated as one class for identification and estimation.  
Another limitation of the method is it is not applicable to appliances whose features are not extracted. When a new load is added without extracting the feature and training the SVR model, it will be misidentified and estimated as the most similar features appliance whose features have been extracted. To notice misidentification of newly added appliances, a similarity threshold \(\theta\) can be added to recognize whether there is a newly added load. If the minimum DTW distance from Section 2.2.1 is greater than \(\theta\), a newly added load is considered. When a new appliance is added, the features of the appliance need to be extracted to identify it, and the SVR model must be retrained to realize power estimation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Power Estimation Method** & **SDTW** & **OSVR Based Method** \\ \hline Time(s) & 13.14296 & 2.91480 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computation time for power consumption estimation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **OSVR** & **SVR** \\ \hline RMSE & 1.93 & 4.16 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The estimation error of OSVR and SVR.
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 3 Modified Rescaling Algorithm Development
### Applying the Modified Rescaling Algorithm  
Since the amplitude vector \(\mathbf{A}_{u}\) can be calculated analytically in the NFXLMS algorithm, it is convenient to apply the rescaling algorithm in order to apply the amplitude constraints to the target sub-frequency components. Here, the maximally allowed amplitude \(A_{ui,\max}\)rather than the maximally allowed value \(u_{i,\max}\), is chosen as the constraint of the \(i\)th frequency component. Define the output constraint vector:  
\[\mathbf{A}_{u,\max}=\{A_{ui,\max}\}^{T},i=0,1,\cdots N-1. \tag{17}\]  
where \(A_{ui,\max}\) is the \(i\)th amplitude constraint for the \(i\)th frequency component. Then, the implementation process of the modified rescaling algorithm can be described in the following steps:  
(1) Applying the compensated NFXLMS algorithm to produce the output signal component \(u_{i}(n+1)\) and calculate amplitude \(A_{ui,\max}\) for all sub-frequencies by using Equations (15) and (16);  
(2) Comparing the amplitude vector \(\mathbf{A}_{u}\) with the output constraint vector \(\mathbf{A}_{u,\max}\). If \(A_{ui}(n+1)>A_{ui,\max}\) exists, then the \(i\)th output component \(u_{i}(n+1)\) and the \(i\)th controller weight pairs \(w_{si}(n+1)\) and \(w_{ci}(n+1)\) are rescaled by the following equations:  
\[u_{i}(n+1) =u_{i}(n+1)[A_{ui,\max}/A_{ui}(n+1)] \tag{18}\] \[\left\{\begin{array}{l}w_{si}(n+1)=w_{si}(n+1)/[A_{ui,\max}/A_{ ui}(n+1)]\\ w_{ci}(n+1)=w_{ci}(n+1)/[A_{ui,\max}/A_{ui}(n+1)]\end{array}\right. \tag{19}\]  
(3) Summing up all the output components, the output signal \(u(n+1)\) after rescaling can be obtained as:  
\[u(n+1)=\sum_{i=0}^{N-1}u_{i}(n+1) \tag{20}\]  
The modified rescaling algorithm retains the accuracy and flexibility of the original algorithm with new advantages. Constraints can be easily applied to the target sub-frequency components independently. The explicit amplitude constraint can also avoid potential clipping and improve the steady state of the output signal components at target frequencies. This means that less distortion and harmonics will be introduced into the control system.
## 3 Proposed Method
### Weighted Bidirectional Feature Pyramid Network (BiFPN)
#### 3.4.2 Cross-Scale Connections  
Cross-scale connections refer to the establishment of bidirectional channels that enable information flow and fusion between features of different scales. This connection allows for more effective flow and fusion of information between features of different scales by  
Figure 7: (**a**) FPN Network Structure. (**b**) PANet Network Structure, which proposes a bidirectional fusion backbone network based on FPN, with a top-down and bottom-up pathway. (**c**) BiFPN Network Structure, which treats each bidirectional (top-down and bottom-up) path as a feature network layer.  
establishing bidirectional connections between the top-down and bottom-up paths. BiFPN optimizes upon the feature pyramid network (FPN) by introducing learnable weights to learn the importance of different input features and repeatedly applying multiscale feature fusion in both top-down and bottom-up directions. This cross-scale connection helps preserve shallower semantic information without sacrificing too much deep semantic information, thereby enhancing the accuracy and efficiency of feature extraction.  
In BiFPN, this is primarily reflected in the following aspects: Firstly, nodes with only one input edge are removed as they lack feature fusion and contribute minimally to network fusion. Deleting these nodes has minimal impact on the network while simplifying the bidirectional structure. Secondly, additional edges are added between the original input and output nodes at the same level to fuse more features without incurring significant computational costs. Finally, to achieve higher-level feature fusion, each bidirectional path is treated as a feature network layer and repeated multiple times to achieve more advanced feature fusion, enhancing the richness and accuracy of features, as shown in Figure 8.  
Traditional feature fusion methods typically involve operations such as concatenation or addition to combine input features (feature maps) without effectively distinguishing between them. However, different feature maps contribute differently to feature fusion, and simply adding or concatenating them for fusion is not the most ideal operation. Therefore, BiFPN proposes an efficient weighted feature fusion mechanism.  
Modeled as follows:  
\[x=\left\{\begin{aligned} P_{i}^{td}=Conv(\frac{w_{1}\cdot P_{i}^{ int}+w_{2}\cdot Resize(P_{i+1}^{nt})}{w_{1}+w_{2}+e})\\ P_{i}^{out}=Conv(\frac{w_{1}^{\prime}\cdot P_{i}^{nt}+w_{2}^{\prime} \cdot P_{i}^{ld}+w_{3}^{\prime}\cdot Resize(P_{i-1}^{out})}{w_{1}^{\prime}+w_{2} ^{\prime}+w_{3}^{\prime}+\epsilon})\end{aligned}\right. \tag{6}\]  
\(p\) is the Feature Map, _Resize_ is the upsampling or downsampling operation, \(w\) is the learning parameter used to distinguish the importance of different features in the feature fusion process.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 2 Materials and Methods
\]  
where \(\overline{R}_{\lambda}\) is the directional hemispherical reflectance or black-sky albedo and \(\overline{R}_{\lambda}\) is the bi-hemispherical reflectance under isotropic illumination conditions or white-sky albedo for spectral band \(\lambda\), \(\theta_{s}\) is the solar-angle at Landsat overpass, \(m\) denotes MODIS resolution parameters, \(l\) denotes the Landsat resolution parameters, \(\Omega_{l}\) is the Landsat sun-view geometry and \(\overline{a}\) and \(\overline{\overline{a}}\) are the AN ratio for black-sky and white-sky albedo, respectively. To increase the opportunity for pure pixel identification and reduce the influence of arbitrary scene edges resulting from the Landsat tiling scheme, the classification algorithm is run on the mosaic of consecutive same-day Landsat scenes (and area of \(\sim\)510 km north-south by 185 km east-west) with the central scene serving as the scene of interest for albedo derivation. The white-sky and black-sky albedo products are produced with per band quality metrics to indicate the quality of both the BRDF input data per pixel and the quality of the correlation between the MODIS and Landsat classification maps and the QA layers are included in the resultant product metadata. The six spectral bands are then used to calculate three broadbands (visible (0.35-0.7 \(\upmu\)m), near infrared (0.7-5.0 \(\upmu\)m) and shortwave (0.35-5.0 \(\upmu\)m)) consistent with the MCD43 albedo product suite. The narrow-to-broadband conversion coefficients used were derived from MODTRAN modelling as presented in [PERSON] et al., 2016 [29]. The internal Landsat snow flag was used to detmination if the snow or snow free coefficients should be applied and the pure pixel snow flags are delivered with the product in an separate image file.
## 3 Analytical Methods
### Mapping Analysis  
The same experimental platform was utilized for both surface scanning and point analysis. The scanning area was first selected, followed by line-by-line scanning analysis. The linear scanning laser beam spot size generally ranged from 15 to 40 \(\upmu\)m. The sample moving speed was adjusted based on the sample size, but it was always equal to or greater than the beam spot size. The scanning time for a single sample was controlled within 1.5 h. The ablation frequency was 7 Hz, and the laser ablation energy was 3 J/cm\({}^{2}\). Prior to sample signal collection, a 30 s standard sample signal was collected, followed by a 20 s background signal. The laser parameters for the standard sample were consistent with those of the measured sample, particularly the beam spot size and ablation frequency. The data processing and post-mapping were performed using the laboratory-designed software LIMS (based on Matlab) [16; 18], which automatically performed background signal deduction, instrument signal sensitivity drift, and other correction work. The element content was calculated using the 100% normalization method.
## 1 Introduction
The proposed reaction mechanism for glucose oxidation to gluconolactone in basic media is shown below:  
\[\text{Cu}\rightarrow\text{Cu}^{2+}+2\text{e}^{-} \tag{1}\]  
\[\text{Cu}^{2+}+2\text{OH}^{-}\rightarrow\text{Cu(OH)}_{2} \tag{2}\]  
\[\text{Cu(OH)}_{2}\rightarrow\text{CuO}+\text{H}_{2}\text{O} \tag{3}\]  
\[\text{CuO}+\text{OH}^{-}\rightarrow\text{CuOOH}+\text{e}^{-} \tag{4}\]  
\[2\text{CuOOH}+\text{glucosa}\to 2\text{CuO}+\text{glucolactone}+2\text{H}_{2} \text{O} \tag{5}\]  
However, the single metal has poor conductivity, which hinders the transport of electrons between the catalyst and the electrode, and thus, it reduces the catalytic performance of the material [16]. Attending to the proposed reaction mechanism, copper oxide provides a more direct glucose oxidation reaction. In this context, previous works studied the effect of different copper oxide morphologies and nanostructures for glucose detection. Thus, recent studies have proposed nanospheres, nanowires, and nanostrips structures as they provide a larger specific surface area and superior physical and chemical properties [17]. The results reported showed high reproducibility, wide linear ranges, and low detection limits for glucose in alkali media. Different nanostructures have been tested in similar conditions, for example, [PERSON] et al. reported a copper oxide nanoparticle on a graphene oxide electrode with high sensitivity to glucose and a linear correlation in the range of glucose concentration from 0.1 to 150.0 \(\upmu\)M [18]. [PERSON] et al. have synthetized mesoporous CuO electrodes with a sensitivity of 0.08 mA\(\cdot\)mM-1 and a linear range of 170-310 \(\upmu\)M [19]. To advance the state of the art, this work intends to develop a simple but useful and robust, copper oxide electrode constituting a step forward in low-cost non-enzymatic sensors. After a rigorous characterization for glucose sensing application, the results achieved have been compared with complex copper oxides electrodes in terms of linear range, sensitivity, and stability. Besides these aspects, the effect of common interferences present in the glucose oxidation reaction has been studied.