esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### PlanetiQ-COSMIC Profiles Comparison
#### 4.2.1 Single Profile Comparison
Upon subjecting the PlanetIQ and COSMIC atmospheric profiles to \(t\)-tests for individual variables, it becomes apparent that there are no statistically significant distinctions at the designated location and time. The \(t\)-test outcomes for each variable further support the conclusions drawn from the preceding f-tests. Figure 11: Regression between PlanetiQ and COSMIC profile observations for all variables indicating the mean diff., RMSE, and mean absolute diff. values. In the instance of pressure, the f-test indicates a lack of significant difference in variances between the two profiles, with an F-Stat of 0.00 and a \(p\)-value of 0.988. Consequently, the \(t\)-test for pressure was conducted under the assumption of equal variances, yielding a T-Stat of 0.015 alongside a \(p\)-value of 0.988. This result, surpassing the Tcritical value of 1.962 at a confidence level of 95%, solidifies the inference that the marginal pressure differentials observed between the two profiles lack statistical significance. Similarly, in the domain of WVP, the f-test shows no significant variance difference, with an F-Stat of 0.212 and a \(p\)-value of 0.645. Consequently, the \(t\)-test for WVP was performed under the assumption of equal variances, manifesting a T-Stat of \(-0.461\) and a \(p\)-value of 0.645. According to the Tcritical value of 1.962 at 95%, the non-significant \(p\)-value reinforces the absence of meaningful disparities in WVP between the PlanetIQ and Cosmic profiles. The \(t\)-test examination for Temperature, under the premise of equal variances based on the f-test results (F-Stat = 0.020, \(p\)-value = 0.887), yields a T-Stat of 0.142 and a \(p\)-value of 0.887. This outcome, consistent with a Tcritical value of 1.962, signifies that the minute temperature fluctuations between the two profiles are statistically insignificant. Finally, the f-test implies no significant variance difference (F-Stat = 0.001, \(p\)-value = 0.974). Consequently, the \(t\)-test produces a T-Stat of \(-0.032\) and a \(p\)-value of 0.974. With the Tcritical value at 1.962, the non-significant \(p\)-value reaffirms the absence of noteworthy distinctions in the reference values between the PlanetIQ and Cosmic profiles. In summary, the consistent lack of statistical significance in the \(t\)-test outcomes for each variable, coupled with the non-significant variance differences indicated by the f-tests, substantiates the overarching conclusion that there are no statistically significant differences between the PlanetIQ and COSMIC profiles at the designated location and time.
## 1 Introduction  
Several offshore wind farms are under construction on the Finnish west coast. The official environmental specifications define that bird species behaviour at the vicinity of wind turbines must be monitored. This concerns especially two species: the White-tailed Eagle (_Haliaetus albicilla_) and the Lesser Black-backed Gull (_Larus fuscus fuscus_), which are explicitly mentioned in the environment license. The only way to fulfil this demand cost efficiently is to automate monitoring, and that requires automatic bird species identification at such a level that the aforementioned bird species are separable from all other species in the study area. The problem is how to identify bird species in flight automatically in real-time? The prototype system for automated bird identification is developed and placed at a test location on Finnish west coast. This system is still under construction.  
The ultimate objective of bird monitoring in wind farms is to find suitable methods for collision detection [1; 2], and especially to find possible deterrent methods [3]. The WT-Bird of the Energy Research Centre of the Netherlands is the first (i.e., known to us) published research of this subject. The principle of the WT-Bird system is that a bird collision could be detected by the sound of the impact and that the bird species can be recognised by non-real time method from video footage [4; 5].  
However, it has known problems with false alarms in high wind circumstances concerning larger bird species and it has no automated species identification algorithm [6].  
Radar is a feasible choice for the detection of birds since the identification need is restricted to the flying birds only. If merely a radar is used, the identification capability is limited to a few size classes according to radar suppliers. Obviously, external information is required and a conceivable method is to exploit visual camera images, thus a digital single-lens reflex (DSLR) camera with a telephoto lens is applied. This paper shows that convolutional neural network (CNN) with deep learning algorithm trained on real-world images is capable to achieve sufficient state-of-the-art performance as an image classifier. At present, all the images are manually taken at the test location. The images will be acquired automatically by the final system.
## 1 Introduction
Space System Life Cycle Assessment guidelines aims to establish the methodological rules on how to correctly perform space-specific life cycle assessment [10]. The life cycle inventory database used by ESA is available on the subsystem level and also on the component level. A subsystem/component-level mass budget cannot be obtained for any satellite since it is commercially confidential. As of now, space-specific LCA can be performed only by respective satellite manufacturers who have readily available data on the subsystem/component-level mass budget. There is a need for a method to calculate the environmental footprint during the production process by any user for any low Earth orbit (LEO) satellite, even though a mass budget is not available to provide insights on environmental effects. This paper presents a method to perform a life cycle assessment for low Earth orbit satellites, with the Starlink constellation as a case study, and based on the mass budget, midpoint indicators are calculated using the Sinapro Life Cycle Assessment tool. Through this, we aim to address the following research question:  
How can the environmental impacts be quantified during the production phase of low Earth orbit constellation satellites by applying the systems engineering and life cycle assessment principles? This article is structured in following way. Section one provides a context for the sustainability paradox of the space industry and the need for focusing on the environmental impacts of the production phase of space missions. Section two, the Materials and Methods Section, outlines the application of the mass budget techniques used in space systems engineering and LCA to assess the life cycle environmental impact, emphasizing the delineation of the system boundary considered for this analysis. The case study Results Section presents a comprehensive examination of Starlink, including the breakdown of satellite subsystems and their mass budget and LCA results. The Discussion Section interprets these findings, exploring their implications for the environmental sustainability of satellite mega-constellations production, proposing future work in potential framework develop ment that can give insights into all aspects of environmental effects that are discussed in the context of the sustainability paradox.
# Human-Robot Collaboration: An Augmented Reality Toolkit for Bi-Directional Interaction
## 3 Development Tools and Experimental Setup  
The architecture of the developed platform is shown in Figure 1 and includes:  
1. The HMD Microsoft HoloLens 2, produced by Microsoft and running the Windows Holographic operating system [7]. It is equipped with a Qualcomm Snapdragon 850 ARM64 CPU with 64 GB of internal storage and 4 GB of RAM. Moreover, there is a dedicated processor for holograms visualization (HPU 2.0).
2. The collaborative robot Franka Emika Panda [9], characterized by seven rotational joints equipped with torque sensors and encoders to provide the joint positions and velocities. The cobot is controlled by using an external workstation PC connected via Ethernet to the controller. An open source C++ library, called libfranka, is used which allows for real-time commands to be sent and provides the current status of the robot at the frequency of 1 kHz.
3. A ROS node, allowing for commands to be sent to the cobot through the integration of libfranka with the entire ROS ecosystem. In particular, ROS Kinetic has been used in the experiments.
4. A Unity platform, version 2021.1.22 (64 bit) [10], with the Mixed Reality Toolkit (MRTK) library in version 2.7.2; this Microsoft library provides several additional components and features, which allows for the development of the cross-platform AR application in Unity. The code is written in C# through Visual Studio 2019 and perfectly integrated into the Unity development environment.
5. ROSSharp library [12], a Siemens Open-Source library designed for Unity, performing the data exchange between cobot and HMD or, in general, for communication of.NET applications with ROS. In this project, however, the version v1.2c of the ROSSharp library, designed exclusively for Universal Windows Platform (UWP) applications, is adopted [26].
6. ROSBridge, i.e., the protocol which enables messages exchange from C# to C++, by using the JSON format.  
By the GitHub repository in [27], the Unified Robot Description Format (URDF) of the cobot is inherited and then imported in Unity [28]. In AR, the cobot hologram overlaps the real one by means of a calibration procedure exploiting the SampleQR code application [29], provided by Microsoft, which requires NuGet [30], a package manager designed for Unity. In Table 1, the used hardware and all the development tools are reported.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 4 Results and Discussion in Weighing on Modular WIM System  
### Test Site Modular System Comprehensive Test  
To verify the weighing performance of modular systems and determine the appropriate number of platforms, practical tests were conducted with multiple trucks at the proving ground site (Figure 7). The test vehicles included a two-axle truck, a four-axle truck, and a six-axle truck, with static weights of 6831 kg, 12,370 kg, and 16,890 kg, respectively, and speeds ranging from 0 to 30 km/h. The test included a five-platform modular system, a three-platform modular system, and a one-platform independent system. In order to compare the accuracy, consistency, and robustness, the test scheme was designed as follows:  
* Accuracy Verification: In 30 trials, the three systems were used to weigh the vehicles, with the speed increasing during each trial, and the weighing results were compared.
* Consistency Verification: The three weighing systems were used to weigh the test vehicles under the same conditions repeatedly, and the results of repeated weighing were compared.
* Robustness Verification: The five-platform modular system and the one-platform independent system were selected to weigh vehicles traveling at variable speeds, and the weighing results were compared.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 7 Open Challenges
### Privacy and Security in AMI-Enabled IoMT Environments  
IoMT devices and sensors continuously collect sensitive healthcare data, emphasizing the need for robust security measures to protect patient privacy and prevent unauthorized access. One of the primary challenges is the susceptibility of IoT devices to cyberattacks, which stems from inadequate security protocols and firmware vulnerabilities. Exploiting these vulnerabilities can grant malicious actors unauthorized access to medical records, allowing them to manipulate patient data or disrupt healthcare services. Moreover, the vast volume of data generated by IoMT devices presents challenges for secure transmission, storage, and processing, necessitating the implementation of encryption techniques and robust access controls. Security concerns are also present in the integration of edge or cloud computing due to diverse locations and multi-hopping [160]. Therefore, developing lightweight and efficient encryption mechanisms that can ensure data security in IoMT environments is crucial.
## 3 Methodology  
The methodology employed in this investigation consisted of multiple sequential procedures (Figure 2).  
In this work, digital elevation models (DEMs) for different time periods (namely, 2009, 2013, and 2020) were first acquired for the studied area. Subsequently, control sites were selected, deriving their heights from the DEMs. Later, a genetically changed algorithmic model was trained using only 1% (17) of the total 1700 randomly picked elevation data points previously collected. The smallest number of randomly selected points was chosen, yielding the same result with 99% accuracy [30], taking into account variations in land usage. Then, the model, designed to provide a mathematical equation for forecasting the water mass volume, was assigned the responsibility of calculating the expected results at many randomly chosen locations evenly distributed around the research region. At this point, the result was a calculated approximation of the volume. After considering the climate variables, the rainfall amounts, and the randomly selected sites, the model was tasked with conducting both past and future sediment deposition simulations. Subsequently, the model utilized 1700 randomly obtained data points to forecast the alterations in both the surface and volumetric properties across the study period. Ultimately, the model predicted the mean clogging process in the marshland through the use of a ground shape factor (slope angle-dependent), based on both the shadow cast per pixel obtained in the period with no flooding and the elevation of the control points iterated across all the images in the evaluated time series.  
To check the obtained results' coherence, a comparison was carried out with what had been inferred for the years 2008/2009, 2009/2010, 2010/2011, 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018, 2018/2019 and 2019/2020 through the use of 792 satellite images with a 30 m resolution. Among these, 330 came from Landsat 5 TM (Thematic Mapper), 342 from Landsat 7 ETM+ (Enhance Thematic Mapper Plus), and 120 from Landsat 8 OLI (Operational Land Imager). All the images had less than 20%  
Figure 2: Methodology followed in this research.  
cloud cover. It is relevant to note that the flood cycle begins in October/November of the previous year and usually ends in February/March of the following year.
# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
###### Abstract  
Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a sleep disorder characterized by the simultaneous occurrence of comorbidities. Survival analysis provides a potential solution for assessing and categorizing the severity of obstructive sleep apnea, aiding personalized treatment strategies. Given the critical role of time in such scenarios and considering limitations in model interpretability, time-dependent explainable artificial intelligence algorithms have been developed in recent years for direct application to basic Machine Learning models, such as Cox regression and survival random forest. Our work aims to enhance model selection in OSA survival analysis using time-dependent XAI for Machine Learning and Deep Learning models. We developed an end-to-end pipeline, training several survival models and selecting the best performers. Our top models--Cox regression, Cox time, and logistic hazard--achieved good performance, with C-index scores of 0.81, 0.78, and 0.77, and Brier scores of 0.10, 0.12, and 0.11 on the test set. We applied SurvSHAP methods to Cox regression and logistic hazard to investigate their behavior. Although the models showed similar performance, our analysis established that the results of the log hazard model were more reliable and useful in clinical practice compared to those of Cox regression in OSA scenarios.  
survial analysis; Machine Learning; Deep Learning; explainable artificial intelligence; rehabilitation medicine; obstructive sleep apnea +
Footnote †: journal: Medical Imaging
## 2 Materials and Methods
### Leaf Area, Cluster Exposure, Yield Components and Berry Sampling  
Leaf area, cluster exposure, yield components and berry sampling were assessed as previously described [32]. In summary, eight representative shoots per replicate were collected one week before harvest and leaf area was determined with a LI-3000 leaf area meter (LI-COR Bioscience, Lincoln, NE, USA). Photosynthetically active radiation (PAR) in the cluster zone was determined one day after the application of severe shoot trimming between 11:00 and 12:00 h under cloudless conditions using a portable QSO-S PAR Photon Flux sensor (Decagon Devices, Pullman, WA, USA), which was placed vertically and upward near clusters on both sides of the canopy. The yield and number of clusters per vine were recorded at harvest and the average cluster weight was calculated. The number of clusters per shoot was determined as clusters to shoot ratio. Single berry weight wascalculated using a sample of 200 berries. In both years, berry sampling was performed at four stages of ripening in order to follow the accumulation of sugar content in berries. In both years, the first sampling also was performed on a day when severe shoot trimming was applied, while the fourth sampling was performed on a harvest day. The second and third samplings were performed in intervals of 11 to 13 days in 2017 and at intervals of 14 to 17 days in 2018. At harvest date, samples of 200 berries were used for total anthocyanins and total phenolic determinations. All sets of berries were weighed and immediately stored at \(-20\)\({}^{\circ}\)C.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision
### Stereo Vision Camera  
A stereo vision camera obtains depth information about a scene by mimicking the principle of human binocular disparity. It uses two or more cameras to capture the same scene from different angles and reconstructs the three-dimensional information of the target by calculating the differences between corresponding points in the images. Stereo vision cameras can output RGB and depth information, and their robustness in occluded scenes can be enhanced by increasing the number of viewpoints. However, they are highly dependent on object texture, and their performance is limited under low-texture conditions [29]. Currently, mainstream cameras on the market include the Intel RealSense D455, ZED 2 (StereoLabs, San Francisco, CA, USA), and Basler blaze cameras (Basler, Ahrensburg, Germany).  
In the agricultural field, stereo vision cameras play an important role. Ref. [30] uses the ZED2 camera to capture color images and depth point cloud information, enabling the development of a real-time detection and localization method for potted flowers, aimed at the automated management of greenhouse flowers. In [31], videos are captured around crops to extract multi-angle frame images. Real-time weed detection is performed using 3D reconstruction technology. Ref. [32] proposes a non-contact measurement method for vegetable size based on stereo vision cameras and keypoint detection, integrating the pixel coordinates and depth values of keypoints to enhance detection accuracy for small targets. Ref. [33] generates depth maps through stereo disparity calculation, thereby reconstructing a 3D model of the soil surface. This non-contact method based on stereo vision can quantify soil surface roughness and assess tillage quality. Additionally, study [34] is a plant detection method based on RGB-D data, aiming to address the identification of crops and weeds in dense scenes, and summarizes the limitations of current stereo vision depth data in plant detection.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 2 Framework Design
### Framework Implementation
#### 2.6.2 Network Architecture  
After the sequences are truncated to the unified length, they are ready to be fed to the network. As Figure 4 shows, the ELMo module followed by the input layer treats each source code function (untokenized) as a sentence. The ELMo module integrated in our network is provided by Tensorflow Hub ([[https://tfhub.dev/google/elmo/2](https://tfhub.dev/google/elmo/2)]([https://tfhub.dev/google/elmo/2](https://tfhub.dev/google/elmo/2)), accessed on 10 November 2021). It is a 5-layer neural network, which exposes three trainable parameters for the aggregation of the first three layers. We remove the mean-pooling layer so that the fourth layer, which is the ELMo word embedding layer, outputs the word  
Figure 4: The structure of the proposed deep neural network with the ELMo module built-in for extracting function-level embeddings. The ELMo module exposes 3 trainable weights—\(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\)—for the aggregation of the character-based word embedding layer, and the two Bi-LSTM layers, respectively. We remove the mean-pooling layer and connect the ELMo word embedding layer to another Bi-LSTM layer, followed by a global max pooling layer to form the embedding framework. During the training phase, three dense layers are added to the global max pooling layer to train the network. When training is completed, the source code functions can be fed to the input layer and the embeddings are the output of the third last layer (global max pooling layer).  
embeddings acceptable by the subsequent Bi-LSTM layer. The Bi-LSTM layer contains the 64 forward and 64 backward LSTM units, respectively, forming a bi-directional structure. Then, outputs of the forward and backward LSTM units are concatenated and passed to a pooling layer. To speed up the Bi-LSTM training process, we use the CudnnLSTM implementation, which is the LSTM based on the NVIDIA CUDA deep neural network library ([[https://developer.nvidia.com/cudnn](https://developer.nvidia.com/cudnn)]([https://developer.nvidia.com/cudnn](https://developer.nvidia.com/cudnn)), accessed on 10 November 2021).  
The pooling layer is the last layer of the proposed framework, which aims to reduce the output dimensionality to a dense vector of a fixed size, acceptable by mainstream ML algorithms. In this paper, we use the global max pooling layer to retain the most important information as represented by the maximal value of the activations from the Bi-LSTM layer. Since there is at most one vulnerability in each sample in our data sets, we surmise that the vulnerable elements in the input sequence will result in having large values after the processing of the preceding layers. Applying the global max pooling for selecting the maximum value can, thus, help to identify potentially vulnerable signals.