esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Vision System and Data Collection  
The custom test set-up was built for powder feeding testing [20]. The powder was supplied via a vertical feeding platform and spread on the 80 mm build platform using a blade. The entire setup was controlled using a Siemens (Munich, Germany) S7-1200 PLC and HMI panel, which allows for precise control over layer thickness, recoater speed, and the powder feed rate. The detailed degeneration of the used setup is given in [20]. The video system utilized for testing comprised the following components: a Balluff (Neuhausen, Germany) BVS002C industrial camera, a C-mount KOWA (Nagoya, Japan) HR964 SR lens with a 35 mm focal length, and a Balluff BAE000K ring LED illuminator. The camera was securely mounted in a fixed position above the powder feeding and spreading mechanism. The Balluff BVS002C camera offers a significant advantage with its built-in graphics processor, real-time operating system, and BALLUFF Cockpit 2.0 software, enabling webserver-based communication. Following the programming stage, the camera functions autonomously, eliminating the need for connection to a PC or industrial computer. The camera's development environment supports image capture, region of interest (ROI) definition, and various image processing operations, including filtering, geometric measurements, and barcode recognition.  
The built-in graphics processor further enables the application and execution of classification algorithms. The camera comes standard with Profinet, EthernetIP, IO-link communication interfaces, and traditional digital IO ports operating at 0-24 V logic. One of these ports is configured as a trigger, allowing the camera to capture images at a precise moment, rather than in continuous mode, based on a hardware- or software-defined signal (e.g., the specific position of the powder feeder). The digital signal can be generated directly by a limit switch or a controller. Additionally, the camera software facilitates communication with an FTP server, where captured images are automatically uploaded. This configuration allowed for the collection of a substantial amount of image data (dataset) without requiring operator presence.
# Towards Productive and Ergonomic Order Picking: Multi-Objective Modeling Approach
## 2 Problem Description and Models Development
### Development of a Model with a Single Objective  
The decision variable \(x_{ij}\) in the arrangement of the boxes in the warehouse represents the combination of a specific box \(i\) in a storage place \(j\). The decision variable \(x_{ij}\) occupies the value \(1\) when the box \(i\) is placed on the storage place \(j\) and \(0\) if the box \(i\) is not placed on the storage place \(j\) (1).  
The first step of model development begins with defining different types of boxes that need to be stored (2). Boxes are unambiguously marked with a number from \(1\) to \(M\) and with the frequency for each box type \(f_{i}\). \(M\) is an index for the last box, which is the same as the number of different boxes.  
\[x_{ij}=\left\{\begin{array}{l}1;\text{if the box positioned on storage space }j\\ 0;\text{otherwise}\end{array}\right. \tag{1}\]  
The second step of model development determines the capacity of storage spaces in the considered warehouse. The number of storage place \(N\) is calculated with the Formula (3). \(N\) represents the \(j\) index in the decision variable (2), where each storage place has its index  
Figure 2: Scheme of a theoretical warehouse and order picking cycle.  
determined with the number from 1 to \(N\). \(N\) is the index for the last storage space and is the same as the number of different storage spaces. After this, the determination of the binary decision variable follows.  
\[i=1,2,3,\ldots,M \tag{2}\]  
The third step of model development defines the decision variable \(x_{ij}\) as a binary decision variable (3).  
\[j=1,2,3,\ldots,N \tag{3}\]  
The fourth step of model development constructs the objective function (4), where we define the minimization of the sum of the product between coefficient \(c_{ij}\) and decision variable \(x_{ij}\).  
\[\sum_{i=1}^{M}\sum_{j=1}^{N}c_{ij}\cdot x_{ij} \tag{4}\]  
The coefficient \(c_{ij}\) in the objective function could present a wide range of factors that impact the decision (for example, order picking time, energy expenditure for picking, the weight of boxes, etc.). However, in the case where order picking is optimized for one objective, the meaning of the coefficient \(c_{ij}\) is uniquely determined.  
The last step of model development defines the constraints of the optimization problem, which define the restriction of the problem. When determining storage assignments for boxes in the warehouse rack, there are two restrictions. With the first type of constraint (5), we define the frequency of the individual type of box stored in the warehouse (where \(f_{m}\) represents the frequency of the specific box type). With the second type of constraint (6), we limit the number of stored boxes in one storage space inside the storage rack, where \(l_{n}\) represents the space restriction for each specific box type in one storage space.  
\[\forall m,n\in\mathbb{N},n=1,2,\ldots,N,m=1,2,\ldots,M \tag{5}\] \[\text{and}\] \[\text{x}_{m,j},x_{i,n}\in\{0,1\}\] \[\text{we define}:\] \[\sum_{j=1}^{N}x_{m,j}=f_{m}\] \[\sum_{i=1}^{M}x_{i,n}=l_{n}\]
# Characterization of Tiled Architecture for C-Band 1-Bit Beam-Steering Transmitarray
## 2 Transmitarray Model
In transmitarray antennas, beam steering is achieved by spatially modulating the phase distribution of the emitted wavefront across the array aperture, as follows:  
\[\arg(b_{mn})=-k\,\mathbf{r}_{s}\cdot\mathbf{r}_{mn}, \tag{3}\]  
where \(\mathbf{r}_{s}(\theta_{s},\phi_{s})\) is the unit vector in the beam-steering direction \((\theta_{s},\phi_{s})\), while the array vector \(\mathbf{r}_{nm}=(x_{mn},y_{mn},0)\) comprises the coordinates of the unit cell. For symmetrical unit cells, the required continuous local phase shift follows from (1) and (2) as:  
\[\arg(T_{mn})=\arg(b_{mn})-\psi_{mn}^{fs}+kR_{mn}-\psi_{mn}^{uc}, \tag{4}\]  
where \(\psi_{mn}^{fs}\) is the focal-source phase pattern in the direction of the unit cell (NB: typically, the phase pattern, with respect to the phase center of the antenna, is nearly flat within the angular range of the main lobe). In the proposed 1-bit transmitarray, the phase distribution (4) is discretized according to the following recipe (shown for the wrapped phase):  
\[\arg\!\left(T_{mn}^{d}\right)=\left\{ \begin{array}{c}0^{\circ}\ \forall|\arg(T_{mn})|\leq 90^{ \circ}\\ 180^{\circ}\ otherwise\end{array} \right., \tag{5}\]  
It is important to note that the effect of the 1-bit phase quantization on radiation characteristics was analyzed in [11; 15]. It was shown that a 1-bit resolution results in the antenna gain reduction of up to 4 dB, higher sidelobe level and noticeable beam squint.
## 3 Results  
### Monitoring of the Microclimatic Conditions  
The internal monthly average temperature (\({}^{\circ}\)C) values across the 13 case studies illustrates distinct climatic trends influenced by environmental and architectural factors (Figure 2). Notably, San Romerio exhibited the lowest temperature profile, attributable to its high-altitude location (1800 m) in an open mountainous setting, resulting in consistently cooler conditions throughout the year compared to the other sites. A common seasonal pattern across all the locations was the temperature peak in July and August, highlighting the influence of external climate on indoor thermal conditions. This seasonal pattern highlights the influence of external climatic conditions on internal environments. Heating systems played a crucial role in moderating winter indoor temperatures, particularly in three sites: Basilica of San Fedele, San Vittore Mauro, and Besta Palace.  
To evaluate the interaction between temperature (T) and relative humidity (RH), May was selected as a reference month due to the relatively uniform temperature across all sites, allowing for meaningful comparisons of RH and moisture content (MR) under consistent thermal conditions. Figure 3 reveals patterns in internal RH across the studied sites, with San Romerio emerging as the most extreme case due to its consistently high moisture levels (73-91%). This is due to its exposed high-altitude setting, with cool temperatures and limited climate control, fostering persistent conditions of dampness. A similar pattern was observed in Masegra Castle, San Perpetua Church, Pievani Palace, and the Ethnographic Museum of Tirano. Notably, during the summer months of June, July, and August, there was a humidity increase in San Romerio, Sant'Ignazio, San Giorgio, San Fedele, San Perpetua, Pievani Palace, and the Ethnographic Museum of Tirano, necessitating the continuous monitoring of RH to prevent damage. Winter months (Jan-Mar) showed non-humid conditions (\(\sim\)35-36%), followed by modest humidity in spring (April: 44.14%), a summer increase (May-October: 54-61%), and a decline in late autumn (\(\sim\)70-56% in Nov-Dec). Such fluctuations could lead to shrinkage and internal stress, particularly affecting the wooden structural elements embedded in the buttresses [55].  
Figure 2: Average internal monthly temperature across all case study sites. (a) T\({}^{\circ}\)C trend; (b) T\({}^{\circ}\)C heatmap with max-min of relative humidity.
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 4 Case Study
### Data and Experimental Settings
#### 4.1.2 Experimental Settings  
The method consists of two models: the embedding of urban mobility structures considering multi-scale features and the functional distribution learning based on urban mobility structures. Therefore, a two-stage training strategy is adopted in the experiment. In the first stage, multi-scale contrastive learning is adopted for regional embedding training, with a learning rate of 0.01. A two-layer GCN is selected as the basic model. The random discard rates of edge and node attributes are 0.1 and 0.2, respectively. The weight attenuation rate of the Adam optimizer is 1 \(\times\) 10\({}^{-5}\), and the Epoch is set to 1000. A 384-dimensional regional embedding vector is obtained through training. In the second stage, the embedding vectors of the region are learned for functional distribution. This model consists of three layers of artificial neural networks. The hidden layers are set with 128 and 64 neurons, respectively, the learning rate is 5 \(\times\) 10\({}^{-4}\), the weight attenuation rate of the Adam optimizer is 5 \(\times\) 10\({}^{-4}\), and the Epoch is set to 300. The ratio between the training and test datasets was set at 4:1. The experiment was conducted under the Windows 11/64-bit /i7 operating system. The proposed method was implemented using Python 3.8 in PyTorch 1.12.1, Numpy 1.22.3, and Pandas 2.0.3.
## 2 Related Works
### HiFi-GAN Vocoder  
A vocoder reconstructs waveforms by synthesizing them from input spectral features. Rule-based vocoders, such as Griffin-Lim [25] and WORLD [26] vocoders, were widely used because of their simplicity, but the quality of the resulting speech was not satisfactory. Recently, many deep learning-based neural vocoders with excellent sound quality have been proposed. One such neural vocoder, WaveNet [27], produces high-quality speech signals, but is slow to train and inference due to its autoregressive structure. Many works attempted to minimize the computational overhead of WaveNet while maintaining high-quality outputs [28; 29; 30; 31; 32].  
The recently proposed MelGAN [33] and HiFi-GAN [34] are GAN-based vocoders that can obtain high-quality speech at a much faster speed due to their non-autoregressive characteristics. HiFi-GAN uses two types of discriminators: multi-scale discriminator (MSD) [33] and multi-period discriminator (MPD) [34]. These two discriminators enable HiFi-GAN to produce state-of-the-art performance when generating high quality speech signals. However, it should be noted that the spectral feature conversion model and the  
Figure 2: Overview of the CycleGAN-based voice conversion model. Greyed objects contain trainable parameters. The feature extractor and vocoder are not trained in this approach.  
vocoder model are trained independently in the conventional voice conversion approaches, which means that the vocoder is not trained to handle any residual characteristics of the source speakers that may remain in the outputs of the spectral conversion model, leading to degradation of the output waveform for voice conversion.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 4 Experimental Results and Analysis
### Experimental Settings
#### 4.1.3 Experimental Details  
The model was implemented in the PyTorch library. An L20 graphics card with 100 GB memory was used. The backbone network uses the Res2 Net-50 network [17] pre-trained on the ImageNet dataset [41] and incorporates the PSA (Polarization Self-Attention) mechanism [18]. We use the Adam algorithm as the optimizer to optimize our network. The initial learning rate is set to 1 \(\times\) 10\({}^{-4}\), and it decreases to one-tenth of its original value every 60 epochs. During the training phase, we employ random flipping, cropping, and rotation for data augmentation to enhance the robustness of the model. The resolution of the input images is 352 \(\times\) 352, and the model is trained for 200 epochs. In the testing phase, we first resize the RGB and depth images to a resolution of 352 \(\times\) 352, then input them into the network to obtain saliency predictions while generating a saliency map of the same size as the input image. The model has 145 MB of parameters, 7 h of training time, and 31 ms of inference time. It should be emphasized that we use the output of the branch from the modality integration network as our final predicted saliency map.
## 2 Improved Deep Learning Model for Day-Ahead Solar Irradiance Forecasting  
The historical daily solar irradiance curve always presents high variability and fluctuation since solar irradiance is influenced by non-stationary weather conditions. This makes the forecastingaccuracy of day-ahead solar irradiance strongly depend on the weather statuses no matter what kinds of forecasting models we choose.  
Therefore, as shown in Figure 1, the solar irradiance forecasting models are independently constructed for four general weather types, because according to different weather types, classification modeling could reduce the complexity and difficulty of intro-class data fitting so as to improve the corresponding forecasting accuracy.  
In terms of the proposed model (i.e., DWT-CNN-LSTM model) for day-ahead solar irradiance forecasting, its integrated framework is illustrated in Figure 2. The basic pipeline framework behind data-driven DWT-CNN-LSTM models consists of three major parts: (1) DWT based solar irradiance sequence decomposition; (2) CNN based local feature extractor; and (3) LSTM based sequence forecasting model. As for certain weather types, the raw historical solar irradiance sequence is decomposed into approximate subsequence and several detailed subsequences. Then each subsequence is fed to the CNN based local feature extractor, which leverages the advantage of CNN to automatically learn the abstract feature representation from the raw subsequence data. Since the features extracted by the CNN are also time series data that have rich temporal dynamics, then they are input to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained through the wavelet reconstruction of these forecasted subsequences. More details about three major parts above are respectively illustrated in Sections 2.1-2.3.  
Figure 1: The flowchart of the day-ahead solar irradiance forecasting for four general weather types. The DWT-CNN-LSTM forecasting model is based on discrete wavelet transformation (DWT), convolutional neural network (CNN) and long short term memory (LSTM) network.
## 2 Experimental
### Material Characterization  
The surface morphologies of the synthesized catalysts were investigated using a field emission scanning electron microscope (FESEM; JSM-7500F. JEOL). The crystal structures of the prepared catalysts were determined from the X-ray diffraction (XRD) patterns of the powder samples using an X-ray diffractometer (MiniFlex, Rigaku). The source of the X-rays was Cu-K\(\alpha\), operating at 2.7 kV and 30 mA. The crystallite size, \(d\), of the Pd was calculated from the width at half height of the diffraction peak arising from the Pd crystals at 40\({}^{\circ}\) using [PERSON]'s equation, \(d=0.9\lambda/\beta cos\theta\), where \(\lambda\) is the wavelength of the X-rays (1.54056 A), \(\theta\) is the angle at the peak, and \(\beta\) is the width of the peak at half height.  
Nitrogen adsorption-desorption isotherms were measured at 77 K using a BELSORP MAX (MicrotracBel Corp., Osaka, Japan) to reveal the pore structure of the catalysts. Pd catalysts deposited on the supports were degassed at 200 \({}^{\circ}\)C for 10 h prior to the measurements. The BET surface area and total pore volume were calculated in the relative pressure interval of 0.0 to 0.5 using the Brunauer-Emnet-Teller (BET) method. The pore volumes of micro (>2 nm) and meso (2-50 nm) were obtained using the microporous (MP) method and Barrett-Joyner-Halenda (BJH) method, respectively. The macropore volume was obtained by subtracting the micro- and mesopore volumes from the total pore volume obtained by the BET method.  
X-ray photoelectron spectroscopy (XPS) was recorded using a JEOL JPS-9010 photoelectron spectrometer with an Mg K\(\alpha\) X-ray source to determine the surface properties of the catalyst. Narrow photoelectron scans were recorded of the Pd3d regions and the binding energies were corrected based on the C\({}_{18}\) peak. After Shirley background removal, the peaks were fitted by SpecSurf V1.9.3.2 (JEOL Ltd., Tokyo, Japan) using Gaussian-Lorenzian functions.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 5 Experiment Results  
In this section, we outline the experimental setup and evaluate the performance of Multi-WiIR. To assess the effectiveness of WiSen-Net, we conducted comparisons with various models, utilizing standard evaluation metrics such as accuracy, precision, recall, and F1-score.  
Figure 8: WilR-Net network model.  
Figure 7: Comparison before and after wavelet transform. (**a**) CSI before DWT, (**b**) CSI after DWT.  
_Accuracy_: Accuracy is defined as the ratio of correctly predicted positive and negative cases to the total number of cases, and is calculated using Equation (5), below. The overall accuracy rate represents the average accuracy across all users.  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{5}\]  
_Precision_: Precision measures the proportion of predicted positive samples that are actually positive. It considers the accuracy of positive predictions and is computed using Equation (6) below. The overall precision rate represents the average precision across all users.  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
_Recall_: Recall indicates the proportion of actual positive samples that are correctly predicted as positive. It evaluates the completeness of positive predictions and is computed using Equation (7) below. The overall recall rate represents the average recall across all users.  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
_F1-score_: The F1-score employs a harmonic mean instead of an arithmetic mean to balance precision and recall. Unlike an arithmetic mean, where each side equally contributes, to value growth and decline, a harmonic mean favors smaller values during growth and penalizes extreme cases where the precision and recall greatly differ. It achieves a balanced trade-off between precision and recall. The F1-score is calculated using Equation (8) below. The overall F1-score represents the average F1-score across all users.  
\[F1\text{-}score=\frac{2*Precision*Recall}{Precision+Recall} \tag{8}\]  
Accuracy and F1 score were the primary metrics for assessing the overall performance of the system. Accuracy measured the proportion of samples correctly predicted by the system, while the F1 score combined precision and recall, balancing the classification performance for both positive and negative classes. They provided a comprehensive evaluation of both the prediction accuracy and balance across the entire dataset.
# A Low-Cost Electronic System for Human-Body Communication
## 5 Conclusions  
In this article, we have presented a low-cost electronic system that transfers collected data by a secondary channel: the ionic environment. We designed our electronic system as an experimental setup for studying HBC, thus allowing communication between instruments, sensors, and actuators through human-body tissues. The main contribution of this work is the low-cost and miniaturized galvanic communication platform for HBC with off-the-shelf electronic components, whose schematic, printed circuit board (PCB) layout, and related software are publicly available for the community. We conducted the experimental evaluation using a phantom composed of saline (0.9%) and a real human forearm through adhesive surface electrodes. In both experiments, the new system has shown itself to be fully functional and stable, reaching a data throughput of up to 11 kbps, which allows our prototype to work with the IEEE 802.15.6 standard for WBAN. Moreover, this data rate supports different applications such as the use of cryptography keys, user authentication, and vital signs monitoring and transmission. As future work, we plan to expand the sensing amplifier hardware to achieve the highest possible data throughput with the differential driver. We also intend to expand the sensing amplifier hardware, attaching it to a higher sample rate analog-to-digital converter.  
Conceptualization, A.V.-C., M.M. and J.A.M.N.; Formal analysis, A.V.-C.; Funding acquisition, M.N. and J.A.M.N.; Investigation, K.K.C., M.N. and J.A.M.N.; Methodology, K.K.C. and J.A.M.N.; Project administration, M.N. and J.N.; Resources, M.N. and J.A.M.N.; Software, K.K.C.; Supervision, A.V., A.S. and J.A.M.N.; Validation, K.K.C. and M.N.; Visualization, M.M. and A.S.; Writing-original draft, A.V.-C.; Writing-review and editing, K.K.C., A.V., A.S., M.N. and J.A.M.N. All authors have read and agreed to the published version of the manuscript. This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. The authors also thank Universidade Federal de Vicosa (UFV) and the agencies CNPq (CUIDA project, grant #426701/2018-6, FAPEMIG, and the joint NSF and RNP HealthSense project (grant #99/2017), which also partially supported this study. The authors declare no conflicts of interest.