esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The acquisition and interpretation of three-dimensional (3D) spatial data are important assets for scientific and industrial applications, such as 3D city modeling, facility management, construction engineering, navigation and forensic investigations. Direct geo-referencing, which does not require dedicated control points, is an efficient methodology for the fast acquisition of 3D spatial data by means of a 3D laser scanner. It can be performed either using additional backsight targets [1; 2; 3; 4] or using external sensors [5; 6; 7; 8]. The latter requires the fusion of spatial data acquisition sensors and navigation sensors, such as Global Navigation Satellite System (GNSS) sensors. In this contribution, we consider an integrated GNSS navigation system to provide estimates of the position and attitude (orientation) of a 3D laser scanner.  
The use of GNSS for geo-referencing has been explored in various studies. Direct geo-referencing is demonstrated using GNSS integrated with inertial sensors [9; 10] and a digital compass [5]. In this work, we explore pure a GNSS-based navigation solution as in [8; 11]. In [11], a single rotating antenna is used to provide a post-processing navigation solution. As in [7; 8], the proposed multi-sensor system (MSS) consists of multiple GNSS antennas rigidly and symmetrically mounted on the frame of a rotating laser scanner and a reference GNSS station with known coordinates.  
The proposed method uses the multivariate constrained integer least-squares (MC-LAMBDA) method [12; 13; 14; 15; 16; 17] for the estimation of rotating frame ambiguities and attitude angles. MC-LAMBDA makes use of known antenna geometry to strengthen the underlying attitude model, enabling reliable instantaneous ambiguity resolution and attitude determination of the rotating frame. The reliable estimation of rotating frame ambiguities is consequently utilized to enhance the positioning of the rotating frame. This array-aided positioning method [18; 19; 20; 15] naturally yields the estimates of the rotating frame center (centroid of antennas' reference points) and improves ambiguity resolution, as well as the positioning accuracy of the relative position between the rotating frame and the reference station.  
The numerical studies considered in this contribution include performance analyses of the proposed method with GNSS data from two real data campaigns. Comparison studies using epoch-by-epoch processing and filtering confirm the improved performance of the proposed method over the existing method from [8]. This contribution is organized as follows: Section 2 describes the multi-sensor system considered and defines the problem. Section 3 describes our attitude determination and filtering approaches for the rotating frame. Section 4 describes the array-aided positioning and filtering methods for the positioning of the rotating frame. Section 5 presents real data analyses demonstrating the improved performance of the proposed method. Finally, Section 6 contains the summary and conclusions of this contribution.
# Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
## 3 Evaluation of the Proposed La-ACC  
By using microscopic traffic simulation, the proposed La-ACC is evaluated, and its performances for controlling multiple vehicles in typical freeway congested traffic are compared with the existing ACC as described in Section 2.1. Most parameters are kept the same as that of the original ACC, and they are directly taken from [22; 23]. In particular, maximum acceleration and braking are set at \(2\) m/s\({}^{2}\) and \(-4\) m/s\({}^{2}\), respectively. The parameters \(\beta\) and \(h_{\text{max}}\) are determined by applying sensitivity analysis by balancing both performance enhancement and oscillation suppression in transient situations. A long look-ahead horizon (more than the usual time gap of a driver or driving system) may cause speed oscillations of the vehicle in transient traffic conditions. In particular, the typical time gap of ACC, which can be as low as \(1\) s, and the look-ahead parameters of (10) are set at \(\beta=4\) m/s and \(h_{\text{max}}=1\). The parameters of the conditional persistent prediction are tuned as \(\tau=1\) s, \(\alpha=0.45\), and \(\delta=2\) m/s\({}^{2}\), which ensure a mean square speed prediction error of \(0.26\) km/h for the maximum look-ahead horizon of \(h_{\text{max}}=1\) s based on observation on the experimental driving data of about \(8\) km of driving [36]. The maximum speed \(V_{\text{max}}\) is set at the maximum speed limit of the roads. The built-in fuel consumption model in AIMSUN with calibrated parameters based on the Ford Fiesta car, a light-duty vehicle with an internal combustion engine, is used for evaluation in this study [37].  
It is assumed that ACC is deactivated by the driver when the required braking rate exceeds \(50\%\) of the maximum braking limits due to the presence of a slow or braking preceding vehicle; subsequently, the human driver takes control of the vehicle. In particular, in this study, we considered IDM+ (_IDM plus_, a modified version of IDM) [38] as the human driver model, which provides better flowing behavior of vehicles in the synchronized traffic, as it was used with ACC in [23]. When the vehicle returns at a steady condition (with acceleration less than 0.2 m/s\({}^{2}\)), ACC is reactivated after waiting for 30 s.
## 5 Temperature Online Estimation
### Temperature Estimation Algorithm Based on State Equation  
After discretizing the state equation of the proposed thermal model, the state equation can be expressed as:  
\[\left[\begin{array}{c}\frac{\Delta T_{S}}{\Delta t}\\ \frac{\Delta T_{R}}{\Delta t}\\ \frac{\Delta T_{E}}{\Delta t}\\ \end{array}\right]=\left[\begin{array}{ccccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\\ \end{array}\right]\left[\begin{array}{c}T_{s}\\ T_{R}\\ T_{E}\\ \end{array}\right]+\left[\begin{array}{ccccc}b_{11}&b_{12}&0\\ 0&0&b_{23}\\ b_{31}&0&0\\ \end{array}\right]\left[\begin{array}{c}T_{w}\\ P_{s}\\ P_{R}\\ \end{array}\right]. \tag{16}\]  
After the parameters of the state equation are determined, the motor stator temperature, motor rotor temperature, and motor end cap temperature can be estimated by sampling the cooling water temperature and calculating the stator loss according to Equation (16).  
However, the obvious disadvantage of such a method is that an open-loop structure is adopted, so the temperature estimation error is easy to accumulate over time and the temperature accuracy is difficult to ensure. Therefore, temperature estimation based on the Kalman filter algorithm is proposed in this paper.
## 3 Experimental Results and Discussion  
All experiments were conducted using MATLAB (R2021b) software installed on a GPU-based system with a 2.70 GHz processor, 8.00 GB RAM, and a 4 Core(s) Intel (R) processor due to the high-speed requirements. NVIDIA GeForce GTX680 is the GPU processor version used in the experiment system. This processor accelerates the development of deep neural network models. According to [11, 28, 29], GPU processors outperform CPU-based counterparts in terms of processing speed and memory usage. Face image features were extracted using the proposed 2D Gabor filters. The proposed SSAE deep neural network was trained on two hidden layers, using the extracted face image features from the OLR and the Extended Yale-B databases.  
The proposed SSAE neural network model was trained on 2356 samples of initial face image features from the Extended Yale-B database and 320 representatives from the OLR databases. The initial input feature of face images in the Extended Yale-B database is 5280 pixels, while the initial input feature in OLR database is 1680 pixels. The training hyperparameters for the proposed SSAE deep neural network model are shown in Table 2.  
The learning cost function was computed using the mean square error (MSE) function. Figures 11 and 12 show the learning curves for the proposed hybrid Gabor filter with the SSAE deep neural network model for the OLR and Extended Yale-B database.  
Based on the two databases, two deep neural networks were trained for face image recognition: one using the proposed hybrid Gabor filter with the SSAE deep neural network model, and the other using a conventional SSAE deep neural network model. In order to evaluate the proposed method's performance at classifying new cases, the OLR database was tested with 80 samples of face images, while the Extended Yale-B database was tested with 78 samples of face images that were not used during the training session. Equation (12) was used to calculate the recognition rates:  
\[(r.r)=\frac{number\ of\ face\ samples\
###### Abstract  
This study analyzes the record electricity consumption in Croatia during the July 2024 heatwave and evaluates how the increased deployment of onshore wind and solar photovoltaics (PV) could mitigate a similar event in the future. Electricity demand and generation patterns under current (2024) and projected (2030) scenarios have been simulated using a sub-hourly power system model. The findings show that during the July 2024 heatwave, Croatia imported 35% of the electricity, with prices exceeding 400 \(\upepsilon\)/MWh during peak hours. By 2030, the expanded wind and solar PV sectors (1.5 GW each) will increase the renewable share from 38.8% in July 2024 to 54.7% in July 2030. On the annual level, renewable energy generation increases from 53.8% in 2024 up to 66.9% in 2030. As result, the carbon intensity of the power sector will reduce from 223 \(\mathrm{gCO_{2 eq}/kWh_{el}}\) in 2024 to 197 \(\mathrm{gCO_{2 eq}/kWh_{el}}\) in 2030. The share of fossil fuel generation will increase slightly, from 19.7% in 2024 to 22% in 2030, but more significantly in the summer to meet the heatwave-induced electricity demand. Besides that, short-term energy storage of 2 GWh (400 MW discharge over 5 h) could effectively manage evening peak demands after solar PV ceases production.  
summer peak load; heatwave; wind power; solar PV; power system simulation; electricity price; renewable oversupply; variable renewable energy; energy storage
## 3 Materials and Methods
### Convolutional Neural Network (CNN)  
Convolutional Neural Network (CNN) models are a special type of neural network used to process high-dimensional data, such as video and image data. The term \"convolution\" indicates that this network uses a mathematical operation of the same name. The aspect that differentiates this network from multi-layer artificial neural networks is that it uses convolution. CNNs consist of several layers that are sequential. Each of these layers is designed to perform different operations during model training.  
Input layer: The input layer receives input data, (e.g., images, video, text, and audio) of certain sizes to the network.  
Convolution layer: The convolution layer follows the input layer and helps extract various feature maps by applying filters of the specified size to the data.  
Pooling layer: The pooling layer is generally used to reduce the size of the feature map by selecting the highest or average pixel values within a certain filter size applied to the feature map. This comes after the convolution layer, depending on the preferred pooling operation. The common layer helps reduce the computational load and generalize the network.  
Flatten layer: The flatten layer is the most important and last layer before the fully connected layer. This layer transforms the matrices containing information about the feature maps from the previous layers into a one-dimensional tensor and then transfers it to the fully connected layer.  
Fully connected layer: The fully connected layer is the last layer of the Convolutional Neural Network. The proposed method takes the input tensor from the flatten layer and provides full coupling between all features. This process is transferred to the output layer for classification or regression.  
Batch normalization layer: This layer is typically used after convolutional or dense layers and normalizes the activations in the outputs of these layers. By this mechanism, the model trains faster and becomes more stable.  
Dropout layer: This layer, which is typically used after dense layers, temporarily disables some random neurons, thereby reducing overfitting and increasing the generalizability of the model.  
Dense layer: In this layer, each neuron (node) in the model is fully connected to all neurons in the previous layer. This layer helps the model draw meaningful conclusions from the received data and is often used in the decision-making phase of the model.
## 2 Results and Discussion
### Results from other Cartosat-1 studies  
Considering different test sites, terrain types and elevation ranges, Cartosat-1 proved to be a very good source of data for DSM modeling. [PERSON] and [PERSON] [16] reported for the test field of Aixen-Provence (France) a RMSE less than 7 m for 15-meters Cartosat-1 DSMs. [PERSON] _et al._[40] found height differences in DSMs generated from MTF enhanced Cartosat-1 images up to 10 m for both Hobart (Australia) and Castel Gandolfo (Italy) test sites, respectively at 91.7% and 93.1% confidence level. [PERSON] _et al._[41] investigated the generation of digital terrain models for different test sites using region growing for densification: they found a standard deviation for elevation residuals of 3.1 m with a bias in elevation of -1.0 m for the Catalonia (Spain) test field and a standard deviation for elevation residuals of 7.3 m, with a -3.6 m bias in elevation for a reduced area of the Bavaria (Germany) test fields. [PERSON] _et al._[47] reported an elevation accuracy for Cartosat-1 DSMs generated with point spacing of 10 m and evaluated for different types of terrain all over India better than 5 m LE90. [PERSON] [48] investigating the Warsaw (Poland) test site showed that the Cartosat-1 derived DSMs are extremely close to the reference DSMs/DTMs: for 20-meters DSMs he found a RMSE of 2.3 m (estimated LE90 of 4.5 m), a mean error of 1.0 m and a mean absolute error of 1.7 m. For the same test site (Warsaw), [PERSON] and [PERSON] [50] reported a LE90 between 3.88 m and 4.07 m.  
Finally, regarding the generation of high-resolution Cartosat-1 DSMs with 5 m spacing for the flat area of the Roma (Italy) test site (elevation range of only 40 m), [PERSON] _et al._[42] reported a RMSE of about 2 - 3 m, while [PERSON] _et al._[43] discussed, for the same test site (Roma), a LE90 of 3.4 m. These last two investigations, however, considered only the urban area of Roma and results were verified comparing the heights of some buildings and spots on road axes with respect to 1:2,000 3D map, while all other differences between the generated DSMs and reference data were not taken into account. So results are not directly comparable with the outcomes of other investigations.
## 4 Experiments
### Ablation Experiment  
Using the trained network structure for target detection, the ViSDrone2019-DET-val dataset used as the validation sample data are shown in Table 3: [EMAIL_ADDRESS] is improved by 3.2% by adding the SE module of the channel attention mechanism in the backbone network; the lightweight convolution technique GSConv module is used instead of the ordinary convolution in the Neck side of the feature fusion, and a single-time convergence module VoV-GSCSC module instead of ordinary C2f module, the [EMAIL_ADDRESS] of LW-YOLO v8 model is improved by 1 percentage point and the computation amount is reduced by 11.11%, which significantly reduces the computational resources; when using the SE module of the channel attention mechanism and the lightweight structure GSConv+VoV-GSCSC module at the same time, it can be performed to reduce the computation amount and improve the mAP at the same time.  
Target detection using the trained network structure is shown in Figure 6, where Figure 6a shows the experimental results of the baseline model YOLO v8n and Figure 6b shows the experimental results of the LW-YOLO v8 model. It can be seen that the detection accuracy of the LW-YOLO v8 model is better than that of the baseline model YOLO v8n in each category, in which the ten categories of pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor are improved by 0.055, 0.046, 0.015, 0.029, 0.043, 0.032, 0.023, 0.021, 0.053, 0.058. The value of IOU is set to 0.5, and the value of [EMAIL_ADDRESS] for the LW-YOLO v8 model is 0.363, which is higher than that of the baseline model YOLO v8n.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **Params (M)** & **GFLOPs Computation** \\ \hline YOLO v8n & 32.5 & 3.01 & 8.1 \\ DSConv+SConv2D & 32.1 & 2.39 & 6.6 \\ DCNv2+C2f\_DCN & 32.7 & 3.20 & 7.4 \\ GSConv+VoV-GSCSC & 33.5 & 2.82 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model performance after Neck improvement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SE** & **GSConv+VoV-GSCSCPC** & **Precision** & **Recall** & **[EMAIL_ADDRESS]/\%** & **Params(M)** & **GFLOPs Computation** \\ \hline ✗ & ✗ & 43.7 & 32.5 & 32.5 & 3.01 & 8.1 \\ ✗ & ✗ & 47.5 & 35.5 & 35.7 & 4.45 & 12.0 \\ ✗ & ✗ & 44.6 & 33.6 & 33.5 & 2.82 & 7.2 \\ ✗ & ✗ & 47.6 & 35.5 & 36.3 & 2.83 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results.  
Combining the experimental results, using the SE module of the channel attention mechanism and the lightweight structure GSConv+VoV-GSCSPC module, the final LW-YOLO v8n model outperforms the baseline model YOLO v8n in terms of accuracy and computation, with an improvement of 3.8% in [EMAIL_ADDRESS] and a reduction in computation by 0.9% GFLOPs on the VisDrone2019-DET-val validation dataset.
## 2 System Design  
The FTN-DCSK system combines a sinc filter functioning at a sub-Nyquist sampling rate with the basic transmit-receive DCSK design. The system is described in this section, where the schematic and mathematical representations of the transmitted signals are provided. The FTN prospect reflects on the possibility of enabling filtering on sub-Nyquist rates, which reduces the separation between successive transmissions to less than the minimum distance guaranteeing orthogonality. The belief proceeding FTN was that such reduction would be disastrous on signal transmission following the theorems of [PERSON] and [PERSON] for optimal filter design and sampling. It is only through the work of [PERSON] [20] that this belief was questioned, where in their work, he proved that a 401 Hz/bit/s was sufficient for a sub-Nyquist filter to guarantee the same Euclidean distance offered by an ideal Sinc filter in regular orthogonal transmissions. As a result, with FTN, compared to a regular orthogonal pulse with a separation period \(T\), the Mazo limit is \(\rho=0.802\ T\).  
Moreover, the DCSK system is based on transmitting a chaotic sequence (reference) of length \(\beta\) and its product with the data signal in two successive time slots. The detection is based on differential detection, where the incoming signal is delayed by \(\beta\). Then the product of the two sequences in the different time slots is taken, followed by a summation over the \(\beta\) duration. Finally, the sign is taken to determine the transmitted data symbol.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 5 Network Architectures and Transmission Technologies
### Wireless Networks
#### 5.2.2 Non-Cellular LPWANs
The choice between one or the other depends on the willingness of the public lighting holder to deploy and manage its own network with a low (if any) communication fee (this is the LoRa case) or, contrarily, to avoid any issue with the installation and management of the communication network, which entails that a communication cost is to be borne for each streetlight (this is the Sigfox case). As previously observed, in the former case the lighting infrastructure holder might also gain revenues from its private network, whereas in the latter case the communication service is simply a cost, which could increase over time. It is worth observing, moreover, that both technologies suffer from the same coverage issue that affects cellular networks, which in fact have the same architecture: differently from mobile phones, which can counteract a poor communication quality thanks to the mobility of users, street lights are static, which make them very vulnerable to obstructions (foliage, buildings,...) that clutter the communication link. Although it is true that both LoRa and Sigfox devices greatly outperform cellular devices in terms of receiver sensitivity, the occurrence of local coverage gaps cannot be excluded and might happen unexpectedly (e.g., when a new building is raised). Mostly important, such networks were not conceived to support real-time communications, which are required to allow the simultaneous dimming of all interested streetlights when traffic adaptive lighting profiles are adopted.
## 3 Results and Discussion
### Mechanical Properties  
#### 3.3.1 Hardness  
Microhardness distribution across the interface of dissimilar Ti-6 Al-4V/Nitinol linear friction welds in the as-welded and PWHT conditions is shown in Figure 10. A higher value of microhardness (371-379 HV) in the as-welded joint just adjacent to the Ti-6 Al-4V side can be attributed to strain hardening that occurred during the linear friction welding process. In contrast, a slight reduction in hardness (349-357 HV) after PWHT on the Ti-6 Al-4V side and was attributed to the reduction in residual stresses. However, a decrease in hardness on the Nitinol side was attributed to a limited amount of deformation. Similar observations are obtained during rotary friction welding of titanium to stainless steel joints [24]. On the Nitinol side, a slight increase in hardness distribution (264-267 HV) in the deformed zone of the as-welded condition when compared to the PWHT condition (254-257 HV) from the weld interface to the base metal is noticed. For the hardness (344-367 HV) of Ti-6 Al-4V base metal, it is also observed that it is higher than Nitinol (232-245 HV). There was no significant difference in hardness values among the base metals in the as-welded and PWHT conditions. The highest hardness observed at the Ti-6 Al-4V/Nitinol weld interface of as-welded and PWHT conditions may be attributed to the formation of intermetallics of Ti and Ni, such as Ti\({}_{2}\)Ni, conformed by XRD analysis.  
Figure 9: X-ray diffraction (XRD) profiles of the Ti-6 Al-4V base metal, Nitinol base metal and fracture surface of the LFW welds in the as-welded and PWHT conditions.