esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Treatment and Analysis  
Data of the BI location from the chosen articles were divided into categories by experience, gender, and service types using the following rules: (i) Data of the participants younger than 16 were put into the junior group, whereas data of the participants older than 16 were put into the professional group [24]. (ii) Participants and their data were put into groups (M) and (W) according to their gender. (iii) Participants and their data were put into two groups according to the type of service (FS and KS). Since the anthropometric parameters of the participants could bias the comparison ball impact location among participants [25], the means and standard deviations were normalized by the participants' height (Supplementary Materials, Table S2).  
The statistical analysis of the results was conducted using Statistica 14 (Tibco Software Inc., Palo Alto, CA, USA), R version 4.1.0 (R Core Team, 2021), and Microsoft Excel (Microsoft Corporation, Redmond, Washington, DC, USA). The mean and standard deviation (the distance BI location from the origin on the \(x\), \(y\), and \(z\) axes) within FS and KS were normalized by the participants' height and weighted by the number of participants. The aggregate forest plots presented the normalized mean and 95% confidence interval of the BI location of the individual chosen studies and the normalized weighted mean and 95% confidence interval of the BI location computed from the studies included in individual groups (Professional men, Junior men, Professional women, and Junior women). The one-way ANOVA with [PERSON]'s post hoc test was used to analyze differences among groups within the FS and KS; a significance level was 5%. A partial eta square (\(\eta^{2}\)) and [PERSON]'s \(d\) were used to estimate the effect sizes. [PERSON]'s test performed the assumption of data equality of variance. The normalized weighted mean BI location was used to compare ball position among individual groups (serve type FS and KS x professional and junior tennis players) separately for the \(x\), \(y\), and \(z\) axes. The BI location's stability analysis was computed separately for the \(x\), \(y\), and \(z\) axes comparing the normalized weighted standard deviation among the individual groups within the FS and KS.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 5 Experiment and Analysis  
### Experimental Data  
AIS data from January to February 2017 are used in the study. The data volume of this part is 10.9 G and contains daily AIS vessel trajectory information during this period. The information is stored in the format of CSV files. Each CSV file contains millions of data records, and each record shows the static and dynamic information of the vessel in detail.  
Among them, the static information includes MMSI number, IMO number, vessel name, type, length and width, position, etc. The dynamic information includes vessel position, time, ground heading, ground speed, bow direction, turning speed, sailing state, etc. The detailed information is shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Name** & **Attribute Meaning** & **Attribute Type** \\ \hline MMSI & Vessel Unique identification code & Int \\ Name & Vessel Name & Char \\ Vessel Type & Vessel Type & Char \\ Length & Vessel Length & Int \\ Width & Vessel Width & Int \\ \hline \hline \end{tabular}
\end{table}
Table 1: Static information of the vessel.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Name** & **Attribute Meaning** & **Attribute Type** \\ \hline Lon & Longitude & Float \\ Lat & Latitude & Float \\ Sog & Ground speed & Float \\ Cog & Course over ground & Float \\ Rot & Rate of turn & Float \\ DT\_Pos\_Utc & AIS Dynamic Position Update Time & Time \\ Drawht & Vessel Draught & Float \\ Nav\_status & Vessel Status & Char \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic information of the vessel.
## 4 FPGA Realization of the Chaotic Dynamical System with a Butterfly-Like Curve of Balance Points
As expected, the sub-system X uses less resources as it is less complex than the others. The sub-system Y uses more resources in addition to the Digital Signal Processors (DSPs) as it has more calculations. The sub-system Z is the most complex one. It requires 20 DSPs to perform seven multiplications and other operations. The complete implementation of discrete-time chaotic system (8) requires 390 Flip-Flops, 1295 Look-up Tables, 99 Input/outputs, and 28 DSPs. The use of the complete system does not exceed 0.37% of the total Flip-Flops, 2.43% of the total Look-up Tables, 12.73% of the DSPs available on the FPGA. The operation frequency of our chaotic oscillator system on the FPGA is 85.39 MHz. Figure 19: Realization of the sub-system Z on FPGA. Figure 18: Realization of the sub-system Y on FPGA.
## 3 Materials and Methods
### Data Description  
The timing and methods used for acquiring material damage information have varying degrees of impact on the results of material damage analysis. To minimize the analysis errors caused by these factors, we selected damage data from acoustic emission and image-based damage for two materials which are two-dimensional woven laminate composites with opposing morphological characteristics as follows: a concave shape on the central side (Figure 3a) and a perforated center (Figure 3b). The damage data for both materials were collected using the same testing machine (Figure 3c), employing a unidirectional tensile loading behavior. The maximum loading capacity of the testing machine used was 10 kN, and the loading rate remained constant at 2 mm/min throughout the entire stretching process.  
The data used in this study were collected based on the machine and indicator specifications mentioned above. The visual analytics system can be applied to the force damage data analysis of any composite material as long as it has AE and DIC data associated with force loading in non-destructive testing.
## 1 Introduction
Therefore, a random forest (RF) machine learning algorithm for GPP (GPP\({}_{\text{RF}}\)) was developed for rice-wheat double-cropping fields by integrating multi-source satellite remote sensing images as well as ground measurements. Based on the above data, the main objectives were to: (1) assess the performance of the MODIS GPP product (GPP\({}_{\text{MOD}}\)) through comparison with EC-estimated GPP (GPP\({}_{\text{EC}}\)) and determine the driving factors of GPP; (2) extrapolate the GPP from the single-site scale to multi-site scales; and (3) calibrate the GPP\({}_{\text{MOD}}\) over the rice-wheat-rotation cropland in the NYRD.
## 6 Evaluation Experiment of the Proposed Approach
### Research Limitations  
There are some issues that could probably affect the findings achieved when conducting crowdsourcing RE for a mobile app using our social-network-based crowd selection approach. One of the limitations is in the expertise retrieval component, which is that the process of merging the categories of Google Play Store and Apple App Store to create a consistent list of categories was performed in a semi-automated way. There was some human intervention, and we suggest fully automating it as a future work. Another limitation is that since we could not find a dataset benchmark specifically for measuring similarity between mobile app descriptions, we have utilized only human judgments to evaluate similarity scores generated by the pre-trained models to measure similarity between mobile app descriptions.  
In the evaluation stage, one of the major concerns is the crowd motivation to participate in a crowdsourcing initiative. During the crowdsourcing experiment, we noticed that many of the selected crowd did not seem interested in responding to the invitation or might have considered the invitation as spam. We have relied on the intrinsic motivation of the crowd and no external incentives were provided. We believe that incentivizing the crowd could have resulted in a larger number of crowdsourced requirements and, hence, a larger number of new and desirable requirements. Such an issue could be investigated as a separate research gap that is beyond the scope of this study. In addition, a considerable segment of the selected crowd was no longer active on Twitter, which in turn reduced the size of the selected crowd. Furthermore, another concern that might arise is when a specific mobile app type has a small audience, as this might lead to smaller number of received contributions.  
Another factor that could have increased clarity and fully standardized the structure of the written mobile app requirements is delivering a training session for the selected crowd prior to participation. However, this would require a long period of time to prepare and conduct training sessions or could even lead to resistance by the crowd. Therefore, during our experiment, to make the crowdsourcing task appear simpler and avoid training the crowd, they were given examples of the requirements to follow. Nevertheless, in general, the elicited requirements had no major shortcomings in terms of format, but for optimal outcomes providing some level of guidance in the form of training or automated assistance could improve clarity.  
Regarding the experts' assessment stage within the evaluation, relativism and subjectivity accompanied by factors such as creativity are another aspect that might impact the produced results. How creativity is perceived from one expert to another is different. Uncommon and favorable features might be considered as creative from an expert's point of view. Creativity, meaning extreme novelty, can be very difficult to achieve, therefore, having a reasonable conception of creativity is necessary.
## 1 Introduction
However, one of the biggest challenges facing this technology is scaling, as it is vital to have the ability to implement the technology on an industrial scale such as a WWTP [28]. For example, in order to achieve the EU's energy and climate targets, it is necessary to increase the production of biogas and, in particular, methane, which is why numerous methanisation plants [29] are being built. Starting from a biogas richer in methane will also help to improve yields and facilitate these objectives. This study aims to compare the impact of the incorporation of conductive fillers and the integration of BES in the anaerobic digestion of sewage sludge on a semi-pilot scale that can approach a real application. Initially, a biodegradability test is conducted to assess the effects of different types of conductive materials on biomethane production. Subsequently, a long-term semi-pilot-scale experiment is performed to evaluate the influence of one selected conductive material and the addition of electrodes on the conventional anaerobic digestion process. Finally, the study emphasizes the potential of these technologies to enhance biogas production, offering promising advancements for anaerobic digestion efficiency.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
To overcome these limitations, we focused on GCS advantages, since GCS is an essential component of drone networks, containing drone position and movement schedules [11]. Taking our preliminary work [12] further, we propose GCS-routing, a new routing protocol for drone networks that maximizes GCS utilization. This provides many advantages for drone networks, with the two most significant advantages as follows. Figure 1: Multi-drone system providing network services to users and IoT devices. 1. GCS can estimate link costs using drone position and movement information. Thus, drones are only required to detect a link failure, which can be achieved by counting beacon messages sent from neighboring drone access point (AP) interfaces. 2. GCS knows the drone mobility schedules, hence it can predict topology changes and notify new routes to each drone in advance. Thus, network disconnected time, which is extremely crucial for controlling drones due to their mobility, can be minimized. We implemented GCS-routing on real drones and GCS, and evaluated GCS-routing performance in various experiments, e.g., video [13]. In addition to evaluations using real drones, we conducted many simulations to support experimental results and analyze GCS-routing from more diverse perspectives. In all the experiments and simulations, GCS-routing outperformed existing protocols in terms of average throughput, throughput stability, and disconnection time. The major contributions of this paper can be summarized as follows. * Proposing a routing protocol and defining a link cost metric for drone networks to control multiple drones as a team
* Implementing the proposed routing protocol on actual drones and evaluating the performance of the protocol with a fleet of drones
* Devising a new GCS onto which the proposed routing control scheme was installed to collect topology changes and disseminate routing tables (current commercial drones and GCS products do not have this type of network routing function)  
The remainder of this paper is organized as follows. Section 2 briefly introduces related work. Section 3 provides a detailed explanation of the proposed GCS-routing design, and Section 4 presents evaluation results. Finally, Section 5 summarizes and concludes the paper.
# Thermal Conductivity Calculations for Nanoparticles Embedded in a Base Fluid
## 2 Theories  
[PERSON] [54] derived an equation based on the solution of the Boltzmann transport equation for calculating the thermal conductivity of nanocomposites made from microw and nanowires. The effective thermal conductivity of the base-fluid embedded nanowires, \(K_{ceff}\), is given by:  
\[K_{ceff}=(1-\varphi)K_{m}+\varphi K_{w}-(\Delta K1+\Delta K2), \tag{1}\]  
where; \(K_{m}\) and \(K_{w}\) are the thermal conductivities of the host medium and nanowire, respectively; \(\varphi\) is the volume fraction of a nanowire;  
\[\Delta K1=\left(\frac{6K_{m}}{\pi r_{2}^{2}}\right)\times\left[\begin{array}{ c}\left(\int_{r=0}^{r=r_{1}}\int_{\theta=0}^{\theta=\frac{\pi}{2}}\int_{0}^{ \infty_{max}}exp\left(\frac{-B3}{l_{w}}\right)rdr\,\cos^{2}\theta\,\sin\theta \,d\theta\,d\varnothing\right)\\ +\left(\int_{r=1}^{r=r_{2}}\int_{\theta=0}^{\theta=\frac{\pi}{2}}\int_{\pi- \partial_{max}}^{\infty_{max}}exp\left(\frac{B2}{l_{w}}\right)rdr\,\cos^{2} \theta\,\sin\theta\,d\theta\,d\varnothing\right)\end{array}\right]; \tag{2}\]  
\[\Delta K2=\left(\frac{6K_{w}}{\pi r_{2}^{2}}\right)\times\left[\int_{r=0}^{r=r _{1}}\int_{\theta=0}^{\theta=\frac{\pi}{2}}\int_{0}^{\pi}exp\left(\frac{-B3}{ l_{w}}\right)rdr\,\cos^{2}\theta\,\sin\theta\,d\varnothing\right]; \tag{3}\]  
\[B1=\frac{r\cos\varnothing-\sqrt{\left(\frac{r_{1}^{2}-r^{2}\sin^{2}\varnothing \right)}}}{\sin\theta};\,\,\,B2=\frac{\sqrt{\left(\frac{r_{1}^{2}-r^{2}\sin^{ 2}\varnothing}{\delta}\right)}-r\cos\varnothing-2\sqrt{\left(\frac{r_{2}^{2}-r ^{2}\sin^{2}\varnothing}{\delta}\right)}}{\sin\theta};\,\,\,B3=\frac{r\cos \varnothing+\sqrt{\left(\frac{r_{1}^{2}-r^{2}\sin^{2}\varnothing}{\delta} \right)}}{\sin\theta};\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\the effective thermal conductivity of NFs, which suggested that liquid layering is one of the parameters that are responsible for the enhanced thermal conductivity of NFs. [PERSON] and [PERSON] [56] assumed that an equivalent particle could be formed from the combination with its coated nanolayer with a radius of \(r_{1}+h\) (where \(r_{1}\) is the original particle radius and \(h\) is the nanolayer thickness). Unfortunately, there is no experimental data for the thermal conductivity of liquid layering. This is why the thermal conductivity of the nanoparticle, \(K_{p}\), is equal to that of nanolayer, as suggested by [PERSON] and [PERSON] [56]. The conventional Maxwell equation for homogeneous mixtures is [61]:  
\[\frac{K_{ceff}}{K_{m}}=\frac{K_{p}+2K_{m}-2\varphi\big{(}K_{m}-K_{p}\big{)}}{K_ {p}+2K_{m}+\varphi\big{(}K_{m}-K_{p}\big{)}}. \tag{5}\]  
This Maxwell equation can be modified to [56]:  
\[\frac{K_{ceff}}{K_{m}}=\frac{\Big{[}K_{p}+2K_{m}+2\varphi\big{(}K_{p}-K_{m} \big{)}(1+\varDelta)^{3}\Big{]}}{\Big{[}K_{p}+2K_{m}-\varphi\big{(}K_{p}-K_{m} \big{)}(1+\varDelta)^{3}\Big{]}}, \tag{6}\]  
where \(\varDelta\) is equal to \(h/r_{1}\).
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Constant Rate (ConstRate)  
The fundamental concept of _ConstRate_[32] is to divide the lifetime of the network into intervals. Packets are only sent or forwarded at these intervals whether they are real or fake to make them indistinguishable. If a node does not have a real packet to transmit during the interval, a fake packet is  
Figure 3: Types of attacks in WSNs.  
transmitted instead. However, the use of intervals concept increases the latency. In addition, this technique has a high power consumption due to the number of fake packets created to protect the real traffic.
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 4 Evaluation
### I/O Throughput  
Scheduling is essential for efficient resource utilization, as it enables us to determine the amount of resources such as IOPS that are needed at specific times. This information enables effective resource management and planning. When the resource requirements of a job exceed the available capacity, the system must take actions such as redistributing the workload across other resources by employing auto-scaling mechanisms. While such strategies ensure that the job is completed, they can also lead to additional costs if extra resources are used beyond what was initially allocated. Therefore, maximizing the utilization of existing resources is essential to avoid unnecessary expenses and maintain cost efficiency. By optimizing scheduling and effectively managing available resources, it is possible to prevent overprovisioning and reduce the need for costly scaling, ultimately resulting in better performance and cost savings. This opportunity underscores the importance of evaluating the effectiveness of different scheduling methods in resource utilization. Figure 7 shows the I/O throughput for each scheduling method. The x-axis represents the max IOPS, while the y-axis indicates the I/O throughput, calculated by averaging the I/O throughput across each check time. As illustrated in the figure, our scheduling scheme consistently achieves a higher resource utilization ratio compared to the FCFS algorithm across all maximum IOPS settings. The I/O throughput varies depending on how well the IOPS requirements of the jobs align with the maximum IOPS capacity. Therefore, it does not simply increase or decrease with higher maximum IOPS; rather, it depends largely on the method used to allocate jobs at each check time. By prioritizing jobs with higher IOPS requirements, we can achieve better resource utilization compared to scheduling jobs in the order of their arrival, as demonstrated in Section 3.3.3.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 4 Proposed CKGAT Method
### Heterogeneous Propagation Layer
#### 4.2.1 Collaboration Propagation Module  
The goal of the collaboration propagation module in CKGAT is to obtain the user initial entity set and the item initial entity set as initial seeds in the process of knowledge propagation. An item that a user has interacted with can represent the user preference to a certain extent. Therefore, for a user \(u\in\mathcal{U}\), the items which the user has interacted with and are in the alignment set \(\mathcal{A}\) can be used as the initial entities of the user. The user initial entity set \(\mathcal{E}_{u}^{0}\) is composed of these initial entities, which is defined in Equation (1) [4].  
\[\mathcal{E}_{u}^{0}=\{e\mid(v,e)\in\mathcal{A}\text{ and }v\in\{v\mid y_{uv}=1\}\} \tag{1}\]  
Similarly, the users who interacted with the same item (essentially, all the items that these users interacted with) can also contribute to the item's feature representation as these users have similar preferences. Therefore, for an item \(v\in\mathcal{V}\), the collaboration propagation module first obtains all the users who interacted with item \(v\). Then, all the items that these users interacted with form the collaborative item set \(\mathcal{V}_{v}\) of \(v\), which is defined in Equation (2) [4].  
\[\mathcal{V}_{v}=\{v_{u}\mid u\in\{u\mid y_{uv}=1\}\text{ and }y_{uv_{u}}=1\} \tag{2}\]  
Furthermore, this module filters out the items in \(\mathcal{V}_{v}\) that cannot be aligned with the entities in the knowledge graph through the alignment set \(\mathcal{A}\) to obtain the item initial entity set \(\mathcal{E}_{v}^{0}\), which is defined in Equation (3) [4].  
\[\mathcal{E}_{v}^{0}=\{e\mid(v_{u},e)\in\mathcal{A}\text{ and }v_{u}\in\mathcal{V} _{v}\} \tag{3}\]  
The collaboration propagation module explicitly encodes the first-order interaction information into the user/item initial entity set, thereby enhancing the user representations and item representations.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 3 Efficient Soft Iterative Decoding Algorithms for HARQ with Rateless Codes in Satellite Systems
### HARQ with Cross-Layer Rateless Codes
#### 3.3.2 Soft Iterative Decoding Algorithms
In a word, the cross-layer joint decoding, shown in Figure 7, is performed as follows. The LDPC decoders in the PHY layer feed the soft information in Equation (23) to the LT decoder in MAC layer after performing Equations (20) to (22). Subsequently, the LT decoder in the MAC layer performs Equations (24) to (27) and terminates the joint iterative decoding if a stopping criterion is satisfied. Otherwise, the LT decoder will feed its soft information to LDPC decoders with Equation (28) for the next joint iterative decoding until the predetermined maximum number of iterations, \(\eta_{max}\) is reached.