esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Image Reconstruction and the MIR Algorithm  
The microwave imaging system is getting attention of researchers in recent years for breast cancer and brain tumor/stroke detection. The microwave imaging system has low-cost components and is lightweight and non-ionizing as compared to other imaging modalities, such as X-ray, CT, and PET scans. Microwave imaging via space-time (MIST) algorithm and its extended/modified versions are most used for the purpose. The MIST beamforming was originally proposed for the detection of breast cancer in 2003 [43]. It had attracted many research groups throughout the world working on breast, brain or lung cancer/tumor detection. Many relevant papers have been published using this algorithm [44; 45] and are evaluated on clinical practices for breast cancer detection [46]. The main advantage includes its understandability and high-resolution image. Moreover, it can detect small tumors up to 2 mm. Although MIST has all these advantages associated with its use, the time it takes to process input data and produce the output images does not lie in an acceptable range due to plenty of iterative operations and accumulations of signal data. Therefore, it needs to be optimized before it is used for real-time imaging in clinical diagnosis.
## 2 Related Work
### Lk-Pan  
While ConvNeXt offers a straight-line structure that effectively captures local features, it may fall short in dedicating sufficient attention to the global context. To address this limitation and enhance the capabilities of ConvNeXt, a path aggregation mechanism, LK-PAN, is introduced. LK-PAN originates from the Path Aggregation Network (PANet), which was initially introduced in the context of instance segmentation to bolster the hierarchy of feature extraction networks. The primary structure of PANet is depicted in Figure 3.  
Figure 2: Incremental improvement through optimization steps in ConvNeXt [32]. The foreground bars represent results from ResNet-50/Swin-T, while the gray bars represent results from ResNet-200/Swin-B.  
In Figure 3, we observe that part (a) represents the classical network structure of the Feature Pyramid Network (FPN), which is named for its pyramid-like arrangement [35]. However, it's important to note that the influence of low-level features on high-level features is limited due to the long paths, as indicated by the red dashed lines in Figure 3. These paths can comprise over 100 layers. As mentioned in Section 1, while the theoretical receptive field of P5 may be quite large, it does not manifest as such in practice due to the numerous convolution, pooling, and activation operations. Therefore, PANet introduced a bottom-up path augmentation, as depicted in Figure 3b. This approach aggregates the top-most features from both the low-level features and features at the same level. Consequently, this mechanism substantially shortens the connection between low-level features and the top-most features to around 10 layers. Thus, it effectively enhances feature expression for local areas and minor details. PANet's contributions also encompass adaptive feature pooling (Figure 3c) and fully-connected fusion (Figure 3d). However, these two mechanisms are more closely related to the task of instance segmentation and will not be elaborated upon here.  
Building upon the foundation of PANet, the Large Kernel-PANet, abbreviated as LK-PAN [36], introduces some improvements. The primary feature of LK-PAN is the enlargement of the convolution kernel size. In contrast to PANet, LK-PAN utilizes 9 \(\times\) 9 convolution kernels instead of the original 3 \(\times\) 3 size. This augmentation is aimed at expanding the receptive field of the feature map, thereby enhancing the ability to discern minor features with greater precision. Another key change in LK-PAN is the adoption of a concatenation operation, replacing Figure 3c, for fusing features from different levels.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 2 CNNs and Normalization Layers
### Batch Normalisation  
Batch normalisation (BN) layers are additional layers that fix the activation distributions. This technique reduces the effect of internal covariate shift, a phenomenon hindering stochastic gradient descent optimisation [26]. Internal covariate shift can be described as a drastic change in the activation distributions due to the changed network parameters. BN normalises each activation separately with activation mean \(\mu_{b}\) and variance \(\sigma_{b}^{2}\) estimated from the mini-batch statistics. Furthermore, BN learns an additional set of scale \(\gamma\) and shifts \(\beta\) parameters by backpropagation. The computations related to BN are shown in Formulas (4)-(7).  
\[\mu_{b}\leftarrow\frac{1}{m}\sum_{i=1}^{m}x_{i} \tag{4}\]  
\[\sigma_{b}^{2}\leftarrow\frac{1}{m}\sum_{l=1}^{m}(x_{i}-\mu_{b})^{2} \tag{5}\]  
\[\widehat{x}_{i}\leftarrow\frac{x_{i}-\mu_{b}}{\sqrt{\sigma_{b}^{2}+\epsilon}} \tag{6}\]  
Figure 1: Convolutional layer computation composed in the matrix multiplication form visualised over two non-overlapping local areas and two channels.  
\[y_{i}\leftarrow\gamma\widehat{x}_{i}+\beta \tag{7}\]  
In Formulas (4)-(6), \(x_{i}\) denotes the \(i\)-th convolution layer output values in a batch, i.e., \(x_{i}=y_{j}^{i,i}\) as in Equation (1). More explicitly, \(x_{i}\in X=[x_{1\dots m}]\), where \(X\) is the batch with \(m\) convolution layer activations \(y_{j}^{i,i}\). Alternatively, BN can operate on the activation values given by the ReLU-activation function in Equation (3). In Formulas (4) and (5), the mean \(\mu_{b}\) and the variance \(\sigma_{b}^{2}\) are computed from the distribution of the corresponding output values in the batch. Formula (6) shows the normalisation computation, where \(\widehat{x}_{i}\) is the normalised output, and \(\epsilon\) is a small constant introduced for numerical stability. In Formula (7), \(y_{i}\) is the BN layer output and \(\gamma\) and \(\beta\) are weights for scaling and shifting activations, optimised with backpropagation. Furthermore, during training the moving averages of \(\mu_{b}\) and \(\sigma_{b}^{2}\) are collected and utilised during testing.
# Minimum Viable Products for Internet of Things Applications: Common Pitfalls and Practices
## 4 Results
### RQ2: What Are the Challenges Associated with MVP Engineering in Internet of Things Start-Ups? The challenges relating to hardware prototypes were merged into five themes: (1) Management, (2) Requirement, (3) Design, (4) Implementation and (5) Testing. Table 4 presents the thematic map, with the 24 MVP challenges, designated C01 through C24. The challenges are also mapped to temporal dimensions of start-ups, whether they are relevant to early stages (i.e., MVP type 1, type 2) and later stages (MVP type 3). **Management** concerns the aspects of finance, market and human resources during prototype development. MVPs are typically integrated from various components. Component use and reuse are common in software development but are even more prominent in hardware production, as it is more difficult to produce physical elements in-house. Consequently, the selection and evaluation of third-party vendors who provide satisfactory components are essential (C01). Multiple aspects need to be considered, that is the technical quality of the components, their cost and supporting policies of the vendors. The commitment of vendors to the speed (C08) and quality of delivery, for instance, needs to be proven over time. Given that a large number of hardware manufacturers are based in China, finding a suitable vendor is not straightforward for start-ups in other countries. Constraints on human resource are relevant in various stages of Internet of Things start-ups. Finding an appropriate hardware engineer is crucial for the long-term development of the company (C03). The competence of the hardware team not only determines the capabilities of the company but also influences the speed and quality of the products it produces. Another important dimension is finance, as we observe that our cases initiated their businesses by self-funding. Hardware development requires more resources, in terms of both human and financial investment, than software start-ups do. Many start-ups lack sufficient funding for the prototyping phases (C06). This lack can become even more of an issue when close to manufacturing. Start-ups operate under the pressure of speed, which is also a threat to quality-driven development (C07):  
_\"There are new launches from big companies also like Samsung. Then there are some, many kinds of players are launching now (their own stories)... so there will be competition\"_ (Case D)  
With MVP type 3, the managerial concerns shift to the relationship with marketing and manufacturing. Start-ups need to validate their business model, to test the market feedback on their products (C04). They also need to decide the cost of their products. There is a risk here as the manufacturing cost will determine the product cost (C09).
## 2 Material and Methods
### Laboratory Research
#### 2.3.2 Heavy Metals Research  
The samples for the tests of individual metals were prepared according to the reference methodologies for determining the total metal content in accordance with PN-ISO 11466. Metals in soils were determined by the method of atomic absorption spectrometry with flame atomization. Determination of trace metals was performed after soil mineralization with a mixture of concentrated acids: nitric and perchloric (3:1). The mineralization was carried out with the use of a microwave system, in accordance with the EPA 3052 method. Then, quantitative analyses of the metals: chromium Cr, nickel Ni, copper Cu, cadmium Cd, and lead Pb were carried out by the method of atomic absorption spectrometry with flame atomization (in accordance with the PN ISO 11047 method [40]) using the AAS-F device (AAS PinAAcle 900F PerkinElmer, Krakow, Poland). A 100 mm burner was used and was fed with a stoichiometric mixture of medical air and acetylene. The flame temperature was 2100--2300 \({}^{\circ}\)C. Hollow cathode lamps were used as the radiation source. All reagents used were of analytical grade. Precision and accuracy have been verified using certified soil reference materials (PACS-3 and MESS-4; National Research Council Canada, NRCC). Recovery of chromium Cr, nickel Ni, copper Cu, cadmium Cd, and lead Pb ranged from 84.0 to 118.5%. For the determined elements, the quantitative limits were: lead Pb = 0.05 mg/kg; cadmium Cd = 0.03 mg/kg; chromium Cr = 0.5 mg/kg; nickel Ni, copper Cu = 0.3 mg/kg. To assess the levels that may be trace metals and Igeo anthropogenic influences on the information system in geoaccumulation indices (Igeo) [41; 42].  
The Igeo index originally developed by [PERSON] [43] is used to quantify the level of soil metal contamination resulting from both natural events and/or human activities. The geoaccumulation index is calculated according to the equation:  
\[\text{Igeo}=\log_{2}\left(\frac{C_{n}}{kB_{n}}\right) \tag{5}\]  
where \(C_{n}\) is the metal concentration (\(n\)) in the sample, and \(B_{n}\) is the geochemical compound, the background value of the same element. The constant \(k\) = 1.5 is used as a correction factor for natural fluctuations in the background value as well as for very small anthropogenic influences. The scale for metal contamination is graded at seven levels:  
\[\text{Igeo}\leq 0\ \text{Uncontaminated}\]  
\[0<\text{Igeo}\leq 1\ \text{Uncontaminated}\ \text{to}\ \text{ moderately}\]  
\[1<\text{Igeo}\leq 2\ \text{Moderately}\ \text{contaminated}\]  
\[2<\text{Igeo}\leq 3\ \text{Moderately}\ \text{to}\ \text{ highly contaminated}\]  
\[3<\text{Igeo}\leq 4\ \text{Highly contaminated}\]  
\[4<\text{Igeo}\leq 5\ \text{Highly to extremely contaminated}\]  
Igeo > 5\ \text{Extremely contaminated}\]
# Adaptive Hierarchical Sliding Mode Control for Double-Pendulum Gantry Crane Based on Neural Network
###### Abstract  
Gantry cranes play a pivotal role in industrial production. Gantry cranes exhibit clear double-swing characteristics in actual working conditions, complicating anti-swing control. Most existing anti-swing control methods are based on a simplified single-pendulum model. The present paper puts forward a double-pendulum model for gantry cranes and proposes an adaptive hierarchical sliding mode control based on a neural network according to the actual working conditions. The use of a neural network and adaptive layered sliding mode control can effectively inhibit chattering, thus improving control performance and stability and achieving the goal of anti-shaking control, thus effectively inhibiting residual oscillation. This method has been demonstrated to be effective in achieving the objective of anti-shudder control, thereby effectively suppressing residual oscillation. Compared with hierarchical sliding mode control, the proposed method reduces the maximum residual oscillation angle of the hook and payload by approximately 80%. In comparison with the conventional sliding mode control, the maximum residual oscillation angle is reduced by approximately 84%. Furthermore, the control force amplitude is reduced to 5.23 N, representing decreases of 30.2% and 37.4%, respectively. These comparative results demonstrate the superior oscillation suppression. The system also shows a reliable performance against potential disturbances.  
double-pendulum gantry crane; positioning and anti-swing control; RBF neural network control; adaptive hierarchical sliding mode control
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods  
The SecureDrone authentication framework is a strong, multi-layer architecture developed for Internet of Drone (IoD) networks that includes cloud servers, edge servers, ground control stations, user controllers, and drones with domain-specific authentication and cryptography modules. It uses a three-stage authentication process--registration with secure key pairs, authentication via behavior analysis and cryptographic authentication, and session management with periodic re-authentication. The important cryptographic building blocks are SHA-256 for hashing, AES-GCM for encryption, ECC for key exchange, and nonce-timestamp protection for preventing replay attacks. One of the novel aspects is the inclusion of behavior analysis based on a hybrid machine learning technique--Random Forest for categorical and LSTM for time-series attributes--constructing behavioral profiles and calculating a Behavior Deviation Score (BDS) to invoke extra authentication upon detection of abnormalities. The protocol maximizes security through resource-conscious design, offloading model training to cloud servers but allowing real-time inference on edge servers, thereby minimizing computational overhead, latency, and energy consumption. Its multi-layered defense approach includes two-factor authentication, ongoing session monitoring, and adaptive re-authentication to counter several attack vectors. Innovative aspects are the addition of behavioral biometrics in drone verification, an adaptive security system scaling security measures according to anomaly detection, security-resource optimization as a measurable metric, and a distributed processing architecture distributing security functions among cloud, edge, and local units. The protocol has been systematically verified employing AVISPA tools (OFMC, CL-AtSe, SATMC, and TA4 SP) and the outcome affirming its capability to work well against identified threats and guaranteeing all the security properties as SAFE. A flowchart in Figure 1 shows the proposed SecureDrone Authentication Framework, a total security solution for drone operations. The framework connects three major components: User/Controller, Ground Control Station, and the drone-esthemselves. The authentication occurs in two phases: (a) the registration phase (establish the ECC key pairs, store the credentials, and setup the behavioral profiles) and (b) the authentication phase (which involves signing request, challenges, and session management). The system employs advanced cryptographic modules using SHA-256, AES-GCM, and ECC protocols to create secure sessions. Behavioral analysis is also utilized for ongoing security that is based on Random Forest for categorical data, and LSTM time series data, with processing performed on Edge and Cloud servers. This is a total security option in that both cryptographic and behavioral indication are verified and that drone operations are continuously monitored.
## 2 Materials and Methods
### Model Performance Estimation  
Each model was trained for each target (observed) variable using the training dataset with ERA and PCA input variables. Then a time series of each target variable was constructed using the full set of input data. The performance of the models was evaluated against independent validation dataset using the normalized root-mean-square error (_NRMSE_), linear Pearson's correlation coefficient (_CC_), and Kling-Gupta efficiency (_KGE_) [23; 61].  
The _NRMSE_ (4) is the root-mean-square error normalized by observation data range [28]:  
\[RMSE=\frac{100\ \%}{x_{max}-x_{min}}\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-x_{i})^{2}} {n}}. \tag{4}\]  
The CC Equation (5) measures the strength of linear associations between modelled and observed data with value ranges between \(-1\) and \(+1\).  
\[CC=\frac{\sum_{i=1}^{n}((x_{i}-\overline{x})(y_{i}-\overline{y}))}{\sum_{i=1}^ {n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}. \tag{5}\]The _KGE_ Equation (6) is an objective performance metric combining correlation, bias, and variability [23; 61].  
\[KGE=1-\sqrt{\left(CC-1\right)^{2}+\left(\frac{\overline{y}}{\overline{x}}-1 \right)^{2}+\left(\frac{\sigma_{y}}{\sigma_{x}}\frac{\overline{x}}{\overline{y }}-1\right)^{2}}, \tag{6}\]  
where \(x\) and \(y\) represent actual and modelled time series data, \(\overline{x}\) and \(\overline{y}\) represent average \(x\) and \(y\); and \(\sigma_{x},\sigma_{y}\) are the standard deviation of the time series. The _CC_ and _KGE_ values all have their optimum at unity.
## 4 Conclusions  
Flexible energy storage systems allow increasing the generation of electricity by means of intermittent renewable energy sources. A closed coal mine in the Asturian Central Coal Basin (NW Spain) is proposed as a subsurface water reservoir of underground pumped storage hydropower plants. Underground pumped storage hydropower plants provide a large amount of electrical energy with rapid response and low environmental impacts. An underground reservoir conformed by a network of tunnels with an arched roof cross section of 30 m\({}^{2}\) has been designed. For the construction of the underground water reservoir it is necessary to analyze the stability of the excavation of the network of tunnels. Transversal tunnels, the central tunnel and junction zones have been analyzed.  
Empirical analysis and three-dimensional numerical simulations have been carried out in this work. According to the empirical analysis, grouted rock bolts and a layer of fibre reinforced shotcrete were recommended as the support system. The rock mass properties used as input parameters for numerical modeling were obtained from laboratory tests and estimated from rock mass classification systems. In the numerical model, the state of plasticity, the axial load in rock bolts and the deformations around the tunnels were analyzed. In addition, the axial force, the bending moment and the shear force were also checked in the reinforced shotcrete layer. The maximum displacements and thickness of the excavation damage zone were reached in shale formation. As shown by the numerical simulations, the proposed support from empirical analysis was feasible. By applying the designed support system, the area of the excavation damage zone and the maximum displacements significantly decreased. The results of the numerical analysis show that no significant failure is expected. All this shows that a combination of empirical and numerical methods is appropriate to design a proper support system in underground infrastructures.  
Based on further rock lab testing, a softening factor for the rock mass after failure should be applied in order to get more precise predictions for displacements and the extension of the excavation damage zone as well as the support loads.  
Conceptualization, J.M.; investigation, J.M., F.S., H.K., A.B.S. and J.L.; methodology, J.M. and F.S.; software, J.M., F.S.; validation, J.M, F.S., H.K. and A.B.S.; writing original draft, J.M. and F.S.; writing review and editing, J.M., A.B.S. and J.L.; supervision, F.S., H.K., A.B.S. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 9: Axial load in rock bolts. Shale and sandstone rock masses.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 4 Discussion
### Future Work  
We analyzed the advantages and disadvantages of the proposed SDAFormer in detail. In this section, two possible research directions in the future are discussed.  
Based on the aforementioned advantages and disadvantages of SDAFormer, further research is needed in the following two aspects.  
* First, in future experiments, additional relevant datasets from other remote sensing sensors, including an Unmanned Aerial Vehicle (UAV) system, will be used to train the network. The additional data allow our model to learn more key features and verify the universal applicability of SDAFormer in building damage assessment.
* Second, on a larger scale, the patterns of damaged buildings are more diverse and complex than the existing classification criteria, Joint Damage Scale (JDS) [48], especially for buildings in urban areas. The process of label calibration is subjective, and this certain tendency of labeling can affect the training process. Therefore, more detailed categories of damaged buildings can facilitate the model to analyze more useful information to support HADR. We will further explore the classification method for building damage assessment and the application of Transformer architecture in the field of disasters response.
# Graphene Deposition on Glass Fibers by Triboelectrification
## 2 Experimental Part  
### Materials  
PPG 2001 E-glass fibers (bundle of 2400 tex), provided by PPG Industries Fiber Glass, were used as a substrate for the TE based coating process. The fibers, having a density of about 2.5 g/cm\({}^{3}\) (measured with a helium pycnometer) and an average diameter of 24.5 \(\pm\) 1.9 \(\mu\)m, had a multi-compatible sizing agent and were used as received. Mechanical tests on single fibers (not reported for brevity) revealed a Young's modulus of 75.3 GPa, a Weibull scale parameter of 2188 MPa (at 30 mm length) and a Weibull shape parameter of 2.5.  
A graphene oxide (GO) solution provided by Graphene (San Sebastian, Spain), with a concentration of 4 mg/mL of GO in water, was used as a source of GO nanosheets. By diluting this solution using deionized water, GO solutions at three different concentrations (0.005, 0.05, and 0.1 wt%) were produced. These solutions were ultrasonicated for 30 min at room temperature using a Labsonic LBS1 ultrasonic water bath (FALC Instruments S.r.l., Bergamo, Italy). In this way, a homogeneous dispersion of GO in water was achieved. A bicomponent epoxy was used as a polymer matrix for micro debonding tests. The epoxy system consisted of an epoxy base (EC157.1) and an aminic hardener (W342), both provided by Elantas Italia S.r.l. (Collecchio, Italy). These two components were mixed at a ratio of 100:30 and cured for 24 h at room temperature followed by 15 h at 60 \({}^{\circ}\)C, as indicated by the producer's data sheet. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) on the cured epoxy system (not reported for brevity) evidenced a glass transition temperature (T\({}_{g}\)) equal to 83.8 \({}^{\circ}\)C and a thermal degradation temperature (evaluated as the maximum mass loss rate) of 352 \({}^{\circ}\)C.