esa-sceva/satcom-chunk-collection huggingface dataset:
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 4 Data Retrieval and Results
### CALIPSO Satellite Retrieval
Figure 4: Whisker plots of the monthly AOD and lidar ratio from CALIOP/CALIPSO products at 532 nm over Lorena-SP, for the period of June of 2006 to December of 2020. Figure 3: Whisker plots of the monthly AOD and lidar ratio from CALIOP/CALIPSO products at 532 nm over Volta Redonda-RJ, for the period of June of 2006 to December of 2020.  
this work, the aerosol subtype classification is also important to derive the most relevant types of aerosol present in the atmosphere of the Volta Redonda and Lorena sites. As can be seen in Figure 5, it is possible to retrieve the most significant aerosol subtype from the histogram of aerosol subtype frequency. Volta Redonda presents relevant frequency values for polluted continental aerosol (\(\approx\)28%), polluted dust (\(\approx\)24%) and elevated smoke, with 11% occurrence from June 2006 to December 2020. The Lorena site follows the same pattern of aerosol classification, where most of the aerosol detected is flagged as polluted continental (\(\approx\)30%) and polluted dust (\(\approx\)27%). The MSP-LIDAR II system was the only single remote sensing instrument employed during the measurement campaigns in Volta Redonda and Lorena. In both locations there is no structure for atmospheric monitoring, such as weather stations, sun-photometers,  
Figure 5: Frequency of occurrence of aerosol subtype flagged by CALIOP/CALIPSO data over Lorena-SP and Volta Redonda-RJ for the period of June of 2006 to December of 2020.  
ceilometers, particulate matter sensors, etc. For this reason, CALIOP data products and its aerosol subtype classification were applied as reference to constrain the lidar ratio value applied to the inversion problem and to retrieve the backscatter profiles. In order to retrieve particle backscatter profiles at 532 nm, the Klett-Fernald-Sasano inversion method [62; 63; 64] was applied by tuning the initial lidar ratio assumption with the lidar ratio values from CALIOP aerosol subtype classification algorithms [51].
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Identifying Brain and Tissue Structures
FetalGAN was proposed to segment a fetal functional brain MRI using a segmentor as the generator in GAN architecture and achieved better performance than 3D U-net [98]. Adversarial domain adaptation was used to adapt a pre-trained U-net to another segmentation task in an unsupervised learning manner [103]. Transfer learning and GAN stand for the opportunity of training segmentation algorithms with weakly labeled or unlabeled data, which may greatly reduce the tedious and time-consuming process of creating groundtruth for segmentation tasks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Structure** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline \multirow{4}{*}{[104]} & \multirow{4}{*}{2020} & \multirow{4}{*}{Amygdala} & 171 infants (age 6 months) & & & & Dice score \\  & & & 204 infants (age 12 months) & sMRI & - & U-net & 0.882 \\  & & & 201 infants (age 24 months) & & & & (12-month) \\  & & & & & & & 0.903 \\  & & & & & & (24-month) \\ \hline \multirow{4}{*}{[105]} & \multirow{4}{*}{2020} & Anterior & \multirow{4}{*}{18 subjects} & \multirow{4}{*}{sMRI} & \multirow{4}{*}{-} & \multirow{4}{*}{GAN} & Dice score \\  & & Visual & & & & & 0.602 \(\pm\) 0.201 \\ \cline{1-1}  & & Pathway & & & & & \\ \hline \multirow{4}{*}{[106]} & \multirow{4}{*}{2018} & \multirow{4}{*}{Brain} & 10 adolescent subjects (age & \multirow{4}{*}{sMRI} & \multirow{4}{*}{-} & \multirow{4}{*}{CNN} & F1 score \\  & & & 10–15 years), & & & & 95.21 \(\pm\) 0.94 \\ \cline{1-1}  & & Mask & 25 newborn subjects from & & & (adolescent) \\ \cline{1-1}  & & & dHCP dataset & & & & 90.24 \(\pm\) 1.84 \\ \cline{1-1}  & & & & & & (newborns) \\ \hline \multirow{4}{*}{[99]} & \multirow{4}{*}{2019} & \multirow{4}{*}{Brain} & 10 adolescent subjects, & \multirow{4}{*}{sMRI} & \multirow{4}{*}{-} & \multirow{4}{*}{CNN} & Improve dice \\  & & & 26 newborn subjects from & & & & score after \\ \cline{1-1}  & & Mask & dHCP dataset, 25 other & & & & labeling a \\ \cline{1-1}  & & & subjects (age 0.2–2.5 years) & & & & very small \\ \cline{1-1}  & & & & & & & portion of \\ \cline{1-1}  & & & & & & & target dataset \\ \cline{1-1}  & & & & & & (\(<\)0.25\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identifying brain and tissue structures.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 2 Technology of Indoor Localization  
In this section, we present several modules commonly used for indoor localization across various sectors. Each module is briefly described with its specifications and typical usage scenarios. These modules play a crucial role in enabling accurate positioning in environments where GNSSs are ineffective, such as healthcare facilities, industrial environments, and military operations [1,2]. By understanding the capabilities and applications of these devices, we can better appreciate their contributions to improving tracking, navigation, and asset management.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sector** & **Key Applications** & **Primary Technologies** \\ \hline \multirow{2}{*}{Medical} & Patient tracking, asset management, & BLE, IoT-based RTLS, Wi-Fi, \\  & HAR & hybrid systems \\ \hline \multirow{2}{*}{Industrial} & Asset tracking, worker safety, & \\  & process optimization & UWB, RFID, BLE, Wi-Fi \\ \hline \multirow{2}{*}{Military} & Personnel tracking, mission & \\  & planning, asset management & UWB, ZigBee, Wi-Fi, Bluetooth \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breakdown of indoor localization systems in the different sectors discussed in this paper.
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### MQ2. How Has the Frequency of Approaches Related to RSA Changed over Time?  
For this mapping question, it is interesting to consider both the set of 1131 candidate papers after applying EC1, and the final set of 106 papers after applying EC2. Figure 3 presents the number of  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Journal Name** & **Total** \\ \hline Remote Sensing & 20 \\ Remote Sensing of Environment & 15 \\ Journal of Experimental Botany & 6 \\ International Journal of Applied Earth Observation and Geoinformation & 6 \\ Computers and Electronics in Agriculture & 5 \\ Agricultural Water Management & 4 \\ Agricultural and Forest Meteorology & 3 \\ ISPRS Journal of Photogrammetry and Remote Sensing & 3 \\ Journal of Stored Products Research & 2 \\ Ecological Modelling & 2 \\ International Journal of Digital Earth & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Publication sources with more than one selected paper.  
Figure 2: Publication channels of the selected studies.  
articles published per year until 2019. This figure shows that there has been an important increase in the number of publications in RSA field in the last decade. Since 2000, this growth has followed a linear trend. Although the first papers date back to the 1970s, no paper meets the strict EC2 criterion until 1997; from 2002 onwards, there are always selected papers.  
There is an evident decrease in the number of publications in 2019. However, this is a consequence that the study was carried out in the first months of 2020. It is possible that many publications at the end of 2019, particularly proceedings, are yet to be indexed in the database used. The same reason applies to the small number of selected papers, and also because they have not had time to receive a sufficient number of citations.
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 1 Introduction
Cryospheric hydrological models and the climate model ensemble are utilized to quantify the hydrological conditions of the upstream regions of rivers like the Indus, Ganges, Brahmaputra, Salween, and Mekong rivers, and assess the impacts of climate change on future water availability in these basins [38]. Water-carbon-coupled biophysical models are also employed in studying hydrological changes in the Tibetan Plateau. These models can simulate changes in glacier mass balance and glacier runoff under different climate scenarios, providing decision support for water resource management under the influence of climate change [39]. The Tailan River originates from the southern slope of Tumur Peak and flows south into the Tarim basin. The upstream basin area of the Tarim River, above the Tailan hydrological station, is approximately 1324 km\({}^{2}\), and the river has a length of about 80 km. The annual discharge of this river is around 7.5 \(\times\) 10\({}^{8}\) m\({}^{3}\), with over 60% of it coming from glacial meltwater. This runoff provides vital freshwater resources to the Zhamutai oasis in the eastern part of Wensu County, Aksu District [40]. By utilizing statistical mechanic methods and the principle of maximum entropy, it is possible to describe the distribution of basin precipitation, average depth, and runoff coefficient using exponential functions [41]. [PERSON] et al. [42] employed this method to calculate the mass balance of the Tarim River basin glaciers from 1957 to 2000. Another method for calculating GMB is by analyzing glacier changes using remote-sensing data and estimating the changes in glacier volume, as well as assessing their contribution to river runoff [43]. Through the analysis of Sentinel-1A/1B data and the SRTM DEM in the Manas River basin of the Tianshan Mountains, it has been observed that the glaciers in this basin are also experiencing mass loss [44]. By studyingthe contribution of glacier meltwater to the runoff in the Manas River basin, it has been found that the replenishment rate of glacier meltwater to runoff is similar to that of the Tailan River basin and other Tianshan Mountain basins. This serves as evidence to verify the reliability of the estimation of GMB [45]. In this paper, InSAR DEM was obtained using Sentinel-1A image data with interferometry and its accuracy is verified by ICESat-2 combined with the ALOS DEM and NASA DEM. The paper firstly eliminates the offset values between different types of DEMs based on the ridgeline co-registration method, to obtain the GSE change. Secondly, the glacier thickness data are used to remove the abnormal values of the GSE change, and the relationship between topographic factors and the results of the GSE change is analyzed. Then, the GMB state in the Tailan River basin from 2000 to 2022 is discussed. Thirdly, this paper analyzes the influence of climate change on the GMB in the Tailan River basin based on meteorological data from 2000 to 2022. Finally, the GMB is converted into glacial meltwater, and the influence of GMB on river runoff are explored.
## 6 Recommendations for Cybersecurity Maintenance in the Vehicle
### Cybersecurity Integration Throughout the Entire Vehicle Lifecycle
#### 6.1.1 Risk Management in Automotive Cybersecurity  
Risk management is a fundamental process in ensuring the cybersecurity of automotive systems. This process involves identifying potential threats, evaluating the vulnerabilities in the system, and assessing the likelihood and potential impact of cyberattacks. In the context of automotive cybersecurity, risk management must account for both internal vehicle components and external factors, such as communication networks and supply chain dependencies.  
Key steps in the risk management process include:  
1. Risk Identification: This involves detecting and cataloging potential threats that could affect various parts of the vehicle system, including hardware components such as Electronic Control Units (ECUs) and communication interfaces such as V2X.
2. Risk Assessment: The identified risks are assessed in terms of likelihood and impact. A quantitative risk assessment may include metrics such as the probability of a cyberattack occurring and the financial or safety implications of such an attack.
3. Risk Mitigation: Strategies for reducing or eliminating identified risks are developed. This may include implementing hardware security modules (HSMs), secure communication protocols, and regular software updates.
4. Risk Monitoring and Review: Continuous monitoring of the cybersecurity landscape is crucial, as new vulnerabilities and threats emerge over time. Risk management plans must be revisited regularly to adapt to the evolving threat environment.  
By implementing robust risk management frameworks, OEMs and suppliers can anticipate, mitigate, and respond to cybersecurity risks more effectively. Industry standards such as ISO/SAE 21434 provide comprehensive guidelines for integrating risk management into the automotive cybersecurity lifecycle.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 6 Conclusions
\begin{tabular}{l l} \(S\) & Piston stroke, m \\ \(T\) & In-cylinder gas temperature, K \\ \(T_{EPC}\) & In-cylinder gas temperature at the EPC, K \\ \(T_{sc}\) & Scavenging temperature, K \\ \(T_{w}\) & Wall temperature, K \\ \(t_{al}\) & Combustion duration, s \\ \(t_{qp}\) & Compression duration, s \\ \(t_{exp}\) & Expansion duration, s \\ \(V\) & In-cylinder gas volume, m\({}^{3}\) \\ \(V_{EPC}\) & In-cylinder gas volume at the EPC, m\({}^{3}\) \\ \(V_{h}\) & Swept volume, m\({}^{3}\) \\ \(v_{max}\) & Max piston velocity, m/s \\ \(W_{i}\) & Indicated work, J \\ \(W_{f}\) & Friction loss, J \\ \(W_{gon}\) & Gross electric energy generation, J \\ \(W_{pump}\) & Scavenging loss, J \\ \(W_{camsm}\) & Motoring energy, J \\ \(W_{net}\) & Net electric energy, J \\ \(x\) & Piston displacement, m \\ \(x_{tilm}\) & Top limit position, m \\ \(x_{limm}\) & Bottom limit position, m \\ \(x_{ign}\) & Ignition position, m \\ \(x_{EPO}\) & Exhaust port opening, m \\ \(x_{Eh}\) & Exhaust port height, m \\ \(x_{SPO}\) & Scavenging port opening, m \\ \(x_{Sh}\) & Scavenging port height, m \\ \(a_{w}\) & Convective heat transfer coefficient, W/(m\({}^{2}\cdot\)K) \\ \(\gamma\) & Adiabatic exponent \\ \(\Delta\eta_{ea-b}\) & Effective thermal efficiency loss caused by leakage \\ \(\Delta\eta_{c,b-c}\) & Effective thermal efficiency loss caused by heat transfer \\ \(\Delta\eta_{ia-b}\) & Indicated thermal efficiency loss caused by leakage \\ \(\Delta\eta_{lib-c}\) & Indicated thermal efficiency loss caused by heat transfer \\ \(\epsilon_{c}\) & Geometry compression ratio \\ \(\epsilon_{ec}\) & Effective compression ratio \\ \(\eta_{c}\) & Effective thermal efficiency \\ \(\eta_{c,a}\), \(\eta_{c,b}\), \(\eta_{c,c}\) & Effective thermal efficiency of Hypothesis 1, Hypothesis 2, Hypothesis 3 \\ \(\eta_{i}\) & Indicated thermal efficiency; \\ \(\eta_{i,a}\), \(\eta_{i,b}\), \(\eta_{i,c}\) & Indicated thermal efficiency of Hypothesis 1, Hypothesis 2, Hypothesis 3 \\ \(\rho_{1}\), \(\rho_{\Pi}\) & Upstream and downstream densities of the throttling orifice, kg/m\({}^{3}\) \\ \(\varphi_{s}\) & Scavenging efficiency \\ \(\varphi_{trap}\) & Trapping efficiency of the in-cylinder gas \\ \(\mu_{all}\) & Discharge coefficient of the leakage passage \\ \(\Psi_{labu}\), \(\Psi_{bup}\) & Subsonic and supersonic flow functions of the in-cylinder gas leakage \\ BDC & Bottom dead center \\ CR & Compression ratio \\ EPC & Exhaust port closing \\ EPO & Exhaust port opening \\ EGR & Exhaust gas recirculation \\ Exp. & Experiment data \\ ICE & Internal combustion engine \\ MFPG & Miniature free-piston generator \\ SC-Batt ES & Super capacitor-battery energy storage \\ Sim. & Simulation data \\ SOC & Start of combustion \\ SPC & Scavenging port closing \\ SPO & Scavenging port opening \\ TDC & Top dead center \\ VCM & Voice coil motor \\ \end{tabular}
## 6 Protocol Stack Considerations  
As shown in the previous section, the performance gains of SCF relaying can be considerable when services can tolerate large delays at the application layer. In order to implement SCF relaying within the cell a number of architectural elements need to be introduced at both the mobile nodes and BSs in the network. A number of different solutions that have been previously proposed and can be utilized to provide such functionalities in cellular networks are outlined in the sequel. In [32] a detailed treatment of the issues arising from such long delays in the end-to-end path have been considered. As described  
Figure 5: Optimal operating points for the network model under investigation. (a) Energy efficiency gains for increasing number of active users within the cell; (b) Energy efficiency gains for increasing number of candidate forwarding nodes.  
by the authors, extended periods of inactivity can cause problems at the transport layer resulting to disconnections. In that work, the authors introduce a DTN architecture that is capable to cope with the unpredictable radio link characteristics. A short term solution has been considered in [33] where the Challenged Internet Access Network Technology Infrastructure (CHIANTI) has been proposed and evaluated. CHIANTI is designed to handle disruptions that could possible occur at different stages of the communication path and that could cause excessive delays. The protocol was evaluated in real network topologies and shown to successfully operate with disruptions of over 5 minutes in length [34]. However, the driving principle in the latter work was to ensure continuation after a disconnection in the end-to-end path. On the contrary, for the work we present here, the delay sensitivity of an information message is determined at the application layer and is a parameter passed and used by the network layer to devise routing paths that satisfy the service deadlines. Therefore, negotiations and guarantees on the delay deadlines should be made by the application layer protocol and the network layer routing protocol.  
Already many applications on smartphone devices include the option to adjust the frequency of checking for content updates. In [20] the authors showed that aggregating all update requests and transmitting information in a single shot reduces considerable the communication energy consumption. A plethora also of different applications over the Internet can be deemed as delay tolerant such as for example e-mails, social networking, file transfer and automatic background updates regarding the operating system and/or for different installed software packages. For the proposed relaying scheme, such knowledge on the delay tolerance of application services allows for informed routing decisions to be made at no expense on the perceivable user experience.
## 3 Results  
As explained in the previous section, the system is used for the transmission of high-resolution images from a UAV to a ground station. It was necessary to test the real data rates of the used data links for various distances between the transmitter and receiver.  
This system consists of a laptop computer, a Raspberry Pi 3 single board computer (RPi3), two pMDLL2450 modules (transceivers), a power supply and connection cables. Raspberry Pi 3 starts a server that waits for the client's connection. After connection with the client (laptop computer) is established, RPi3 starts sending data (image files) to the client. The test code was written in Python programming language. A code snippet for the server side and for the client side is presented in Figure 7. A wireless link is established using two pMDLL2450 modules, each connected to the computers (laptop and RPi3) with  
Figure 5: Ground station antenna and UAV used for tests.  
Figure 6: Horizontal (**left**) and vertical (**right**) antenna gain patterns for the directional panel antenna on the ground station (GS) side.  
an Ethernet cable. The modules were in Master-Slave mode (the module connected to RPi3 was the Slave; the module connected to the laptop was the Master). Several data rate tests for various distances were performed.
###### Abstract  
Deep learning-based object detection is one of the most popular research topics. However, in cases where large-scale datasets are unavailable, the training of detection models remains challenging due to the data-driven characteristics of deep learning. Small object detection in infrared images is such a case. To solve this problem, we propose a YOLOv5-based framework with a novel training strategy based on the domain adaptation method. First, an auxiliary domain classifier is combined with the YOLOv5 architecture to compose a detection framework that is trainable using datasets from multiple domains while maintaining calculation costs in the inference stage. Secondly, a new loss function based on Wasserstein distance is proposed to deal with small-sized objects by overcoming the problem of the intersection over union sensitivity problem in small-scale cases. Then, a model training strategy inspired from domain adaptation and knowledge distillation is presented. Using the domain confidence output of the domain classifier as a soft label, domain confusion loss is backpropagated to force the model to extract domain-invariant features while training the model with datasets with imbalanced distributions. Additionally, we generate a synthetic dataset in both the visible light and infrared spectrum to overcome the data shortage. The proposed framework is trained on the MS COCO, VEDAI, DOTA, ADAS Thermal datasets along with a constructed synthetic dataset for human detection and vehicle detection tasks. The experimental results show that the proposed framework achieved the best mean average precision (mAP) of 64.7 and 57.5 in human and vehicle detection tasks. Additionally, the ablation experiment shows that the proposed training strategy can improve the performance by training the model to extract domain-invariant features.  
computer vision; object detection; deep learning; YOLO; infrared small-target detection +
Footnote †: journal: Medical Imaging