esa-sceva/satcom-chunk-collection huggingface dataset:
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 1 Introduction
In the host-to-host communication among distributed data centers, to provide QoS, it is essential to consider four network parameters such as available bandwidth, end-to-end delay, packet loss rate, and jitter according to application requirements. Software-defined networks (SDN) provide a mechanism to allow scientific applications dynamically request network services or parameters such as available bandwidth, QoS policies, security, among others [8]. These scientific applications can be classified as intensive communication. SDN can address the programability problem allowing for scientific applications to program local or inter-domain networks at run-time to meet their requirements [9]. This process is considered an SDN advantage compared to traditional network services. SDNs are gradually spreading to large-scale (such as data centers) and complex networks (multi-agency collaborative networks) [10]. The millions of data generated from IoT devices will suppose, in the short term, the most massive flow of information and will be the largest provider for BDA. Given the amount of information that needs to be processed to make quick decisions, BDA needs to use computing and storage resources available in distributed data centers connected with state-of-the-art communication networks. As a result, IoT demands more advanced computing, storage, and communication network solutions to handle BDA distributed workloads. We designed QoSComm, and it is defined as an SDN strategy to allocate QoS-based data flows for BDA running jobs across distributed data centers to minimize their completion time. QoSComm implements a QoS-based data flow allocation algorithm for data flow forwarding using a multi-objective optimization method, considering the four most essential networks parameters: available bandwidth, delay, jitter, and packet loss. In addition, QoSComm includes a communication process to dynamically configure feasible paths through the SDN controller for each data center. BDA classified as intensive communication is the target domain of QoSComm. Figure 1 shows the BDA and QoSComm general view. The rest of the article is organized as follows: related work is presented in Section 2. Section 3 introduces our strategy called QoSComm, its design, and implementation. Section 4 describes the simulation model and the experimental setup. Section 5 presents the performance tests and the discussion of the results. Section 6 presents the conclusions and future directions.
# Design, Implementation, and Field Testing
## 4 System Implementation
### System Overview
The Kinect server has a number of components:  
* Server and ServerTask to handle HTTP communications. The Server component serves as the HTTP listener for the KinectServer. Upon receiving an HTTP request, the Server creates a new ServerTask to handle the message. Both the HTTP request and the response contain a JSON object. * KinectDriver to handle motion sensing data from the Kinect sensor. In addition, It also handles the user registration request in conjunction with the Activity Recognizer. If the user is properly registered, the user is added to the User object. For each registered user, the KinectDriver tracks her/him for possible non-compliance activities. * Kinect Coordinator to dispatch the registration and activity requests to a particular KinectDriver and the corresponding User. Figure 3: The message flow between different components of the system. Figure 2: Internal structure of the KinectServer. During KinectServer initialization, the KinectDriver registers a delegate with the Kinect Sensor so that it can receive the skeleton stream from the Kinect runtime. For each new skeleton frame (i.e., Kinect input), the KinectDriver first makes a copy. If the frame contains a registered user, the KinectDriver invokes the Activity Recognizer to conduct the single-frame level non-compliant activity detection based on what is defined in the Configuration file. The single-frame level activity detection result is passed on to the corresponding User object, which is responsible to carry out the activity-level activity detection. When a request arrives at the KinectServer from the network (i.e., network input), a ServerTask is created by the HTTP Server object to handle the message. The ServerTask passes the request to the Kinect Coordinator to find the appropriate KinectDriver or User object to handle the request. Then, the corresponding response will be composed and returned to the client.
## 2 Related Work
### Monocular Metric 3D Reconstruction  
Monocular 3D reconstruction techniques can be broadly categorized into two groups: stereo-like methods that rely on sequences of images, such as **S**tructure from **M**otion (SfM) or **S**imultaneous **I**ocalization **A**nd **M**apping (SLAM), and learning-based methods that estimate depth directly from single images using deep neural networks.  
SfM and SLAM approaches, such as COLMAP [14] and the method proposed in [15], achieve sparse or semi-dense 3D reconstructions by assuming known or reliably estimated camera poses. However, without external sensors or additional constraints, these methods are unable to resolve the scale ambiguity inherent to monocular vision, rendering them unsuitable for applications where metric accuracy is essential. While SLAM-based approaches such as [16] incorporate simultaneous pose estimation, they too suffer from scale indeterminacy.  
Learning-based methods aim to predict dense depth or disparity maps from single monocular images [17], but often result in non-metric, relative depth estimates [18,19] that are independent of camera intrinsics or scene-specific geometry. This makes them unsuitable for use cases where absolute metric depth is required. Even recent state-of-the-art models specifically designed to predict metric depth, such as Depth Anything [20], Depth Pro [21], or the most recent UniDepth [2], which bypasses intermediate depth maps and instead predicts 3D point clouds directly, still do not achieve the legally required reconstruction accuracy for rail-specific applications such as vegetation monitoring.  
A major contributing factor is the domain shift inherent in these models, as they are predominantly trained on datasets such as KITTI [22], NYU Depth V2 [23], or synthetic datasets like ScanNet [24]. These datasets primarily represent urban roadways, indoor environments, or artificial scenes, and therefore do not adequately capture the geometric and visual characteristics of railway environments.
## 3 Dynamic Weight Modification Procedure
### Weight Modification Scenarios  
In this subsection, we present four different triggering procedures for link weight modification. If traffic on at least one link crosses one of utilization thresholds, MC dynamically adjusts the link weights in the vLSDB for all links in the whole network. The proposed mechanisms for determining link weights encompass four distinct scenarios, each tailored to address specific considerations:  
1. Discrete scenario: In this scenario, the weight of the link remains constant within a specified range of link utilization. This approach ensures a straightforward and stable weighting mechanism that responds predictably to variations in link traffic.
2. Linear scenario: The weight assigned to a link follows a linear function of the current utilization of the link. This scenario introduces a level of proportionality in which the weight of the link is incrementally adjusted in response to changes in transmission throughput. It provides a flexible approach that correlates weight adjustments with the dynamic nature of traffic.
3. M/M/1 scenario: This scenario involves the recalculation of weights based on the M/M/1 queuing model, a modification proposed in [1].
4. M/G/1 scenario: Similarly to the M/M/1 scenario, this approach recalculates link weights according to the M/G/1 queuing model. The use of general service-time distributions (G) in this scenario provides a more realistic representation of link performance, extending the simple M/M/1 model.  
It is worth mentioning that the values used refer to average delays, which have been somehow 'translated' into weight values, i.e., the value of the delay calculated according to the given model corresponds to the value of the assigned weight (the larger the delay, the larger the weight) [10]. The vLSDB database is modified with each reading, but the paths are only recalculated when certain load thresholds are exceeded. We conducted studies assuming that LAC initiates path recalculation only when the link utilization threshold (WarnTh = 50% and CongTh = 80%) is exceeded (up or down) on any link. The thresholds were selected in such a way as to limit the number of recalculations of the path [8].  
In the Evaluation section, we will assess and compare the performance of these scenarios, considering their effectiveness in adapting to changes in network conditions. This comparative analysis will show the strengths and weaknesses of each scenario, helping to select an appropriate mechanism based on the specific requirements and characteristics of the network.
# Ecotoxicological Assessment of Soils Reclaimed with Waste
## 3 Results  
### Assessment of Selected Physicochemical Properties of Reclaimed Soil  
The control soil had a very acidic pH of 4.7. After applying remediation treatments (lime, sewage sludge, and/or wool), the pH increased to 6.1-6.7, indicating a transition to slightly acidic or neutral conditions. An increase in pH increases the number of negative surface charges (-COO\({}^{-}\), -OH\({}^{-}\)), which intensifies the sorption of metal cations, especially Pb and Zn. Sorption capacity (V) increased from 27.5% in the control soil to 62-93% in the remediated soils, indicating a significantly greater ability of the soil to bind cations (Pb\({}^{2+}\), Zn\({}^{2+}\)). A key role in this ability is played by both the higher proportion of organic matter and the effect of pH on the deprotonation of soil functional groups or humus. The increase in Corg. content from 1.8 to 4.8 g/kg is important for metal stabilization--functional groups (carboxyl, phenolic) complex metal cations, forming strong complexes unavailable to plants. The control variant contained 4.5 mg Pb/kg and 5.6 mg Zn/kg--low concentrations. After adding sewage sludge, the concentrations increased to 32 mg Pb/kg and 42 mg Zn/kg, indicating a significant increase in the metal load in the soil. The data indicate that remediation improved the soil properties for metal sorption--pH, CEC, and Corg. increased, contributing to the reduction in Pb and Zn availability. At the same time, the content of these metals increased in the soil with sewage sludge, which indicates that caution is required (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & & & \\ \cline{2-6}
**Rclamation Variants** & **pH in 1 mol** & **Sorption** & **Organic** & **Lead Content** & **Zinc Content** \\  & **KCl** & **Capacity V (\%)** & **Content Corg.** & **Pb (mg\(\cdot\)kg\({}^{-1}\))** & **Zn (mg\(\cdot\)kg\({}^{-1}\))** \\ \hline \hline \multicolumn{6
## 7 Future Research Directions
### Multi-Robot Collaboration  
Future research should investigate the use of multiple heterogeneous robots in performing inspection and monitoring of the built environment. UGV-UAV teams have been studied and found to provide better capabilities as compared to a single type of robot [33; 34; 131; 244]. Due to their complementary capabilities, UGV and UAV create synergistic relationships that can outperform either of them individually. However, due to their different motion capabilities and degrees of freedom, they require different types of input. For example, in addition to velocity and angle, UAVs also require a target altitude. Moreover, different manufacturers provide their own software development kits (SDKs) which might not be compatible with another manufacturer's SDK. This requires the development of a separate integration layer in the software architecture. Additionally, if one of the robots is replaced with another robot from a different manufacturer, the software backend needs to be re-developed. This creates the necessity for the development of exchangeable data formats similar to IFC. Future research may develop standard formats and protocols for multi-robot collaboration which are manufacturer-independent.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 2 Related Work  
### Image-Based Scene Graph  
The scene graph is a structured representation of a scene that represents the objects, attributes, and relationships between objects in the scene [10]. Classical 2D scene graph prediction methods follow a multi-stage process [10]. The first stage uses an object detection algorithm to initialize the nodes and obtain feature information, such as detection frames, visual features and categories; after that, the prediction of relational predicates that are based on object entities is performed, and this stage is a relational classification task, mainly using graph neural networks (GNNs) [16] and recurrent neural networks (RNNs) [17] to implement the inference of scene graphs. The final prediction results can be expressed as triads, such as _<subject_, _predicate_, _object_>. The inference ability of computers is gradually improved with the development of related technologies. Later, with the first release of the visual genome [18] dataset, a series of studies on image-based scene graph generation began to appear.  
However, due to the prevalence of the long-tail distribution effects in datasets, the classification results of scene graphs can be biased when predicting the head categories and ignoring the tail categories [19]. De-biasing methods can be used to deal with this problem, such as data augmentation, resampling and re-weighting [10]. Using the Group Collaborative Learning (GCL) strategy and particular loss functions can significantly improve the average metrics of the scene graph task [20; 21; 22]. Furthermore, using additional prior knowledge as an effective information source can enhance the recognition of objects and inter-object relations. The knowledge-embedded routing network (KERN) implicitly incorporates co-occurrence probabilities as weights in the message propagation to eliminate the long-tail phenomenon and, thus, improves the classification performance for fewer sample categories [8]. Subsequently, Schemata first used a multi-task learning approach to encode inductive bias into prototypical features at the category level, allowing previously obtained knowledge to appear and propagate as guidance information in the perceptual model [9].  
Research on the 2D scene graph is relatively mature. It has been successfully applied in various downstream scene understanding tasks, such as image understanding and inference, image retrieval, visual quizzing, caption and subtitle generation, image generation and interactive behavior detection and recognition [7; 10].
## 2 Materials and Methods
### Real-Time RT-PCR  
The mRNA expressions of mucus-producing genes such as MUC5B and MUC5 AC were quantified with a real-time RT-PCR machine, as outlined in earlier studies [34; 35]. RNA was isolated with a Trizol RNA isolation kit (Invitrogen, Carlsbad, CA, USA), and the quality and concentration of isolated RNA were measured with the Real-Time PCR System (CFX96 TM; Bio-Rad, Hercules, CA, USA). The RNA samples were processed with a recombinant DNase I DNA Removal Kit (Cat No. AM1906; Thermo Fisher Scientific Inc., Rockford, IL, USA) to remove any contaminating DNA. The RNA was reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (4368813; Thermo Fisher Scientific Inc., Rockford, IL, USA). PCR analyses were conducted using the ABI Step One Plus Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The PCR cycling conditions included an initial pre-denaturation step at 95 \({}^{\circ}\)C for 1 min, followed by 50 cycles of denaturation for 15 s, annealing at 55-65 \({}^{\circ}\)C for 20 s, and extension at 72 \({}^{\circ}\)C for 30 s. \(\beta\)-actin RNA was used as a standard to check tissue integrity in the tested samples. PCR primer sequences are provided in Table S2. For quantification, the intact control lung tissue served as the baseline, and the relative expressions of MUC5 AC and MUC5B were determined utilizing the \(2^{-\Delta\Delta\text{Cq}}\) method [36].
## 1 Introduction
Despite the progress in the development of multi-source precipitation merging models based on ML, most studies have primarily focused on describing the relationship be-tween the precipitation data and complex environment variables, while the spatiotemporal correlation patterns between satellite products and rain gauge observations have received relatively limited attention. The long short-term memory network (LSTM) is a type of recurrent neural network (RNN) specifically designed to handle long-term dependencies by utilizing a gating mechanism to regulate the flow of information. [PERSON] et al. [21] proposed an integrated framework to merge multi-satellite and gauge precipitation data, which integrates the geographically weighted regression to improve the spatial resolution of precipitation estimations and the LSTM to improve the precipitation estimation accuracy by exploiting the temporal correlation pattern between multi-satellite precipitation products and rain gauges. [PERSON] et al. [22] proposed a spatiotemporal deep fusion model by combining the convolutional neural networks (CNN) and the LSTM to merge the TRMM 3B42 V7 satellite data, rain gauge data, and thermal infrared images. The CNN was used to extract spatial features from the radar and satellite data, while the LSTM was used to capture the temporal features of the precipitation data. The superiority of LSTM in merging precipitation data has been verified. On the other hand, Convolutional LSTM (ConvLSTM) is an extension of LSTM that incorporates convolutional layers into its net work architecture, which allows it to perform spatiotemporal modeling of sequential data and capture spatiotemporal correlations more effectively due to its inherent convolutional structure. It has been successfully applied to precipitation nowcasting and verified to consistently outperform the fully connected LSTM [23]. To the best of our knowledge, there are no related studies merging satellite and gauge precipitation using a ConvLSTM network by formulating multi-source precipitation merging as a spatiotemporal sequence processing problem. The purpose of our study is to address the challenge of spatiotemporally merging precipitation data from satellite and rain gauges by introducing an integrated framework. This framework aims to effectively combine spatiotemporal information from different data sources to enhance the accuracy and reliability of precipitation estimation. It integrates F-SVD in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and ConvLSTM by exploiting the spatiotemporal correlation pattern between them. The framework (F-ConvLSTM) is applied to the Jianxi Basin of China to generate hourly precipitation estimates with a resolution of 0.1\({}^{\circ}\) from the data of both rain gauges and GPM (IMERG V06) from 2006 to 2018.
## 2 Results and Discussion
### Chemical Analysis

Table 1: _Cont._Volatile thiols are a group of aroma compounds with a significance to wine aroma, particularly Sauvignon Blanc and Cabernet Sauvignon wines, that has been widely studied [1]. Some of the most important of these are 4-Mercapto-4-methylpentan-2-one (4 MMP), 3-(Methylthio)-propionaldehyde, 4-Mercapto-4-methyl-2-pentanol (4 MMPol), 3-mercaptoethanol-1-ol (3 MH) and 3-Mercaptohexyl acetate (3 MHA), which have aromas described as blackcurrant, grapefruit, passionfruit and boxwood [21]. A total of 45 aroma compounds with high Osme values (\(\geq\)1.5) were further quantified. Five different methods were involved in aroma quantitation (Table 2). These aroma compounds had more influence on Cabernet Sauvignon wines from the Chinese Loess Plateau region. Currently, no one analytical method can quantify all of the volatile compounds accurately because of the different properties of aroma components. Therefore, five different methods were used for the quantification of these 45 compounds. Table 3 presents the concentration of the 45 aroma active compounds. Data in the table have been arranged into ten chemical families (esters, alcohols, aldehyde, ketones, aromatic compounds, terpenes, sulfide,furan, acids, and pyrazines). Esters and alcohols were the major group of volatile compounds in all the wines, followed by terpenes. ANOVA was used to determine differences between the regional volatile compounds' concentration analysis, using Duncan with a 95% confidence level (\(p<0.05\)). If the contents of two vineyards marked the same letter in the same row, it suggested that the contents of this compound had no significant difference in the two vineyards. On the other hand, if the contents of two vineyards marked the same letter, it suggested that the contents of this compound had a significant difference with a 95% confidence level (\(p<0.05\)). There were four aroma components of Cabernet Sauvignon wines that had no significant difference in the five vineyards. They were (e)-3-hexen-1-ol, 1-octen-3-one, (e)-nerolidol and 2-methoxy-3-isobutyl pyrazine.
## 2 Materials and Methods
### Acoustic Signal Parameters
#### 2.3.3 Harmonic to Noise Ratio--HNR  
The HNR is a parameter in which the relationship between harmonic and noise components provides an indication of the voiced components of the speech signal by quantifying the relationship between the periodic and aperiodic components, expressed in dB ([PERSON], 2018; [PERSON], 2018; [PERSON], 2018). This measurement relates to the energy conveyed by the voiced signal through the glottal impulses and the energy of the glottal noise fraction after being filtered through the vocal tract. This noise arises from the turbulence generated when the airflow passes through the glottis during phonation, occurring when, for example, the vocal folds close inappropriately ([PERSON], 2018; [PERSON], 2018). A signal's overall HNR value varies because different vocal  
Figure 2: Determination of NHR from the normalized Autocorrelation.  
tract configurations give different amplitudes for harmonics. Various approaches can be used to determine the HNR automatically. For instance, [41; 42] used the cepstrum to measure the harmonic and noise components, while [27; 43] used the Autocorrelation in the time domain.  
Considering that a signal \(x(t)\) has additive harmonic and noise components, in the frequency domain, it can be expressed as in Equation (6).  
\[X(w)=H(w)+N(w) \tag{6}\]  
where \(X(w)\) corresponds to the speech signal, \(H(w)\) to the harmonic component, and \(N(w)\) to the noise component in the frequency domain.  
The HNR is a logarithmic measure of the energy ratio associated with the harmonic and noise components. Through Equation (7), it is possible to integrate the spectral power over the audible range of frequencies [44].  
\[HNR=10\times\log_{10}\frac{\int_{w}|H(w)|^{2}}{\int_{w}|N(w)|^{2}} \tag{7}\]  
The spectral power of the harmonic components, expressed by the nominator in Equation (7), corresponds to the power of the harmonic components determined in the Autocorrelation function (\(H\)). The spectral power of the noise, expressed by the denominator in Equation (7), corresponds to the remaining power defined in the Autocorrelation function as \(N\).  
The proposed method for the \(HNR\) determination is based on the Autocorrelation, as described above, and previously used by [PERSON] in [27]. This method determines \(HNR\) according to Equation (8).  
\[HNR(db)=10\times\log_{10}\frac{H}{N} \tag{8}\]  
From the normalized Autocorrelation, and considering \(S=1\), and \(H=A\) (Autocorrelation), according to Figure 2,  
\[\begin{array}{c}HNR\ (dB)=10\times\log_{10}\frac{H}{S-H}=10\times\log_{10} \frac{H}{T-H}\\ HNR\ (dB)=10\times\log_{10}\frac{A}{T-A}\end{array} \tag{9}\]