esa-sceva/satcom-chunk-collection huggingface dataset:
# Allocating Small Transporters to Large Jobs
## 5 The Saturated Transporter Allocation Problem  
In this section, we consider a simplification of \((\mathrm{TAP})\), which we call the _saturated transporter allocation problem_\((\mathrm{sTAP})\). The objective of \((\mathrm{sTAP})\) is to compute an optimal saturating assignment, that is with a minimum number of transporters and where jobs are never idle. If the transporter fleet is too small to allow such an assignment, then (STAP) is infeasible.  
Note that this simplification removes the bi-criteria nature of the problem, as the only objective is to assign as few transporters as possible, under the constraint of saturating every job. In addition, note that the solution to problem \((\mathrm{STAP})\) is not necessarily a point on the Pareto frontier of \((\mathrm{TAP})\). Indeed, from a solution of \((\mathrm{sTAP})\), it may be possible to remove some transporters from shorter jobs without increasing the makespan of the solution (but leaving shorter jobs unsaturated as a consequence). Finally, note that \((\mathrm{sTAP})\) is NP-hard in the strong sense, by Theorem 1, where we showed that computing a point on the Pareto frontier is strongly NP-hard, even if it reduces to finding a saturating assignment.  
We first provide two mixed integer programs to solve \((\mathrm{sTAP})\): one for the general case and one optimized for the case were the transporter fleet consists of several types of similar transporters, which is more prevalent in practical settings. Then, we introduce several greedy heuristics, which we will later extend also to the solution of general \((\mathrm{TAP})\)
## 4 Results and Discussion
### Performance with Feature Subsets
The FTTransformer displayed moderate performance degradation across subset configurations, maintaining relatively consistent behavior but at lower overall accuracy levels. This indicates that while its feature tokenization approach provides stable performance characteristics, it may not extract sufficiently discriminative representations from the available water quality parameters compared to other architectures.
## 3 Results and Validation
### Correlation between Landslides and Influencing Factors Based on SWARA
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Influencing** & **Classes** & **No. of Pixels** & **No. of Landslide** & **SWARA Weight** \\ \hline \multirow{8}{*}{**Lithology**} & A & 26836 & 0 & 0.01 \\  & B & 27889 & 4 & 0.12 \\  & C & 2422 & 1 & 0.36 \\  & D & 1159282 & 126 & 0.09 \\  & E & 710946 & 32 & 0.04 \\  & F & 76373 & 6 & 0.06 \\  & G & 508520 & 41 & 0.07 \\  & H & 6902 & 0 & 0.01 \\  & I & 142618 & 21 & 0.13 \\  & J & 719372 & 61 & 0.07 \\  & K & 410272 & 23 & 0.05 \\ \hline \multirow{8}{*}{**Distance to faults**} & 0–500 & 516832 & 60 & 0.21 \\  & 500–1000 & 466826 & 37 & 0.15 \\  & 1000–1500 & 436370 & 44 & 0.19 \\
**(m)** & 1500–2000 & 381470 & 43 & 0.20 \\  & 2000–2500 & 329017 & 26 & 0.15 \\  & \(>\)2500 & 1660917 & 105 & 0.11 \\ \hline \multirow{8}{*}{**Distance to rivers**} & 0–150 & 1001961 & 47 & 0.07 \\  & 150–300 & 799934 & 72 & 0.15 \\  & 300–450 & 703245 & 64 & 0.15 \\  & 450–600 & 543309 & 59 & 0.18 \\  & 600–750 & 388246 & 48 & 0.21 \\  & 750–900 & 220837 & 16 & 0.12 \\  & \(>\)900 & 220837 & 16 & 0.11 \\ \hline \multirow{8}{*}{**Land use**} & Bare & 656 & 0 & 0.00 \\  & Forest & 1839583 & 243 & 0.53 \\  & Grass & 748647 & 45 & 0.24 \\  & Residential & 189281 & 5 & 0.11 \\  & Farmland & 994815 & 22 & 0.09 \\  & Water & 18450 & 0 & 0.00 \\ \hline \multirow{8}{*}{**NDVI**} & \(<\)(\(-\)0.05) & 110766 & 2 & 0.04 \\  & (\(-\)0.05)–0.05 & 195838 & 7 & 0.07 \\  & 0.05–0.15 & 503739 & 18 & 0.07 \\  & 0.15–0.25 & 1185193 & 58 & 0.10 \\  & 0.25–0.35 & 1322962 & 141 & 0.20 \\  & 0.35–0.45 & 455283 & 88 & 0.40 \\  & \(>\)0.45 & 17651 & 1 & 0.12 \\ \hline \multirow{8}{*}{**Soil**} & ATc & 319882 & 2 & 0.02 \\  & ACu & 1035811 & 153 & 0.54 \\  & ALh & 117324 & 6 & 0.19 \\  & ACh & 2290342 & 154 & 0.25 \\  & RGc & 28073 & 0 & 0.00 \\ \hline \multirow{8}{*}{**Rainfall (mm)**} & \(<\)1100 & 85055 & 0 & 0.01 \\  & 1100–1200 & 427735 & 16 & 0.05 \\  & 1200–1300 & 1631694 & 74 & 0.06 \\ \cline{1-1}  & 1300–1400 & 1040983 & 134 & 0.17 \\ \cline{1-1}  & 1400–1500 & 433154 & 67 & 0.20 \\ \cline{1-1}  & 1500–1600 & 78140 & 13 & 0.21 \\ \cline{1-1}  & 1600–1700 & 38958 & 5 & 0.17 \\ \cline{1-1}  & \(>\)1700 & 55713 & 6 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 3 Method  
In this section, we will describe our model in detail. The multi-path semantic segmentation network with convolutional attention guidance (MCAG) also adopts an encoder-decoder structure with a three-path layout. The main path captures the multi-scale features, which are then fused through the spatial and channel reconstruction modules. The other two paths not only learn advanced semantic information but also delineate the object boundaries. Meanwhile, the model realizes the fusion of global information at larger scales. Section 3.1 will outline the overall architecture of the encoder of MCAG. Section 3.2 focuses on the convolutional attention mechanism. Section 3.3 describes the multi-path and attention-guided fusion module. Finally, Section 3.4 describes the functionality of the decoder.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 2 MIMO-FH-OFDM-DCSK System  
In this section, we will introduce how to apply the FH-OFDM-DCSK to the MIMO communication system.  
Figure 1 illustrates the transmitter and receiver structure of the MIMO-FH-OFDM-DCSK system with two transmitting antennas and one receiving antenna. To enhance the security performance of the MIMO communication system, we propose to apply the FH-OFDM-DCSK scheme in the MIMO communication system.
## 3 Renewal and Revocation Protocols
### Key Revocation Protocol  
The sink collects the IDS results and determines the nodes that have to be revoked. Then, it sends a revocation request to node \(I\) using the protocol (KR) described in Figure 2. In this protocol, the sink sends to node \(I\) the list \(M_{1},\ldots,M_{k}\) of all revoked nodes in the neighborhood of \(I\) and a nonce \(n_{S}\) encrypted with \(K_{DH}(S,I)\). Then, node \(I\) deletes all shared session keys with all nodes included in the list and does not accept any further communications with these nodes. In order to confirm the reception of the list, node \(I\) sends back the nonce \(n_{S}\) encrypted with \(K_{DH}(S,I)\). Nonce \(n_{S}\) acknowledges the reception of the list by node \(I\), and it also ensures the authentication.
## Materials and Methods
### Statistical Analyses  
The coefficient of variation (CV) was calculated by standard deviation (SD)/mean value (mean) to represent the spatial variation of Rs and environmental factors. CV \(<\) 10% was considered weak variability, 10% \(<\) CV \(<\) 100% was considered moderate variability, and CV \(>\) 100% was considered strong variability [42]. Differences in Rs, soil properties, and plant traits between the two sampling scales were analyzed using independent-samples \(t\)-tests. Two-factor analysis of variance (ANOVA) was used to analyze the effects of plot type and sampling scale on Rs. Rs was converted into a natural logarithm before analysis to achieve normal distribution. GS+ 9.0 (Gamma Design Software, Plainwell, MI, USA) geostatistics software was used for semi-variance function model fitting and parameter calculation. The ratio C/(C\({}_{0}\) + C) of partial sill and sill was used to reflect the spatial autocorrelation of Rs. C/(C\({}_{0}\) + C) \(>\) 75%, 25%-75%, and \(<\)25% indicated strong, moderate, and weak spatial autocorrelation of Rs, respectively [65]. The ggpmisc package was used to conduct fitting regression analysis of Rs and the environmental factors (soil microclimate, soil nutrients, and plant traits) of the three plots. Due to the collinearity among environmental factors, the Hmisc package varclus function was used to cluster and evaluate the redundancy among environmental factors before the multiple regression analysis, and the factors with high correlation were removed [66].  
The Vegan package varpart function was used to perform variance partitioning analysis (VPA) based on redundancy analysis (RDA) to assess the explanatory degree of different types of environmental factors (e.g., soil microclimate, soil nutrients, and plant traits) on the spatial change of Rs [67]. Before RDA analysis, to improve the normality and homogeneity of the variance of data, all environmental factors were log transformed. Then, the ordistep function was used for backward selection to delete the variables that were not significant in each explanatory set. Finally, 999 Monte Carlo permutation tests were performed on the RDA analysis results to quantitatively evaluate the contribution rates of different types of environmental factors to the spatial variation of Rs (i.e., the independent explanatory variables). To prevent overfitting, the adjusted R\({}^{2}\) was selected as the recognition criterion to evaluate the interpretation ability of the model.
# An Experimental Evaluation of Respiration by Monitoring Ribcage Motion
## 2 Materials and Methods
### Respiration Characteristics  
Respiration is a complex movement that involves the skeletal structure, the muscular complex, and various organs in addition to the pulmonary system, which is its main beneficiary. The resulting movement of the chest is indicative of the functionality and efficiency of the respiratory act, which takes the form of an adequate flow of air in the pulmonary system. Thus, respiration can be monitored and objectively evaluated by analyzing such ribcage motion in terms of both muscle actions and ribcage bone motions. The movement of the ribcage involves both the bone structure and the muscular structure in a synergistic way, determining not only characteristic movements but the consequent expansion and contraction of the lung tissues in precisely allowing for the flow of air during respiration.  
The ribcage motion during respiration can be characterized by four basic movements, as summarized in Figure 1a. The so-called pump-handle movement is indicated by the red arrow in Figure 1a, referring to the sternum swinging upward and outward, as produced by the rib system motion. The so-called bucket-handle movement is indicated by the light blue arrow in Figure 1a, and it involves the lower ribs that move laterally and upward like the handle of a bucket. The so-called caliper movement, indicated by the red arrow in Figure 1a, is due to the lowest ribs swinging laterally. A very limited torsion movement is experienced by ribs when twisting around costovertebral joints.  
The monitoring and numerical analysis of the movements in Figure 1a can be worked out by looking at the motion characteristics of a representative rib of the ribcage. Main motion data can refer to the movement frequency, angular excursions of the rib, and linear accelerations of a reference rib point as significant kinematic parameters for the quality and quantity of respiration motion. Figure 1b shows a scheme for a measuring system of those motion parameters of a respiratory act by using an IMU (Inertial Measuring Unit) as a detecting sensor in a properly located position on a rib by considering the sensing reference axes. The roll angle is the rotation angle about the X-axis of the sensor reference, whereas the pitch angle is a rotation around the Y-axis, as shown in Figure 1b. Correspondingly, the acceleration components of the rib reference point are those of the origin of the sensor reference frame. Thus, the accelerometer and gyroscope of the IMU can acquire those data with a proper sampling frequency.  
Such an acquisition of the time data can be evaluated as a characteristic of respiration with values of the respiratory frequency; the angular excursions can be indicative of the respiration efficiency; and the acceleration ranges can be useful in evaluating the quality of the respiratory act, as summarized in Figure 2.
# Blockchain Technology Application in Security: A Systematic Review
## 3 Findings  
All the selected papers have been fully read and the related data about the field of the blockchain usage application, the category type, consensus algorithms, usage of smart contracts, and the integration with other technologies are fully investigated and given in Table 2.  
Figure 1: PRISMA flowchart for selecting included papers. Figure 2: Publication year. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Data** & **Application Field** & **Category Type** & **Consensus Algorithm** & **Smart Contract** & **Integration with Other Tech** \\ \hline
[19] & Blockchain ensures data privacy and confidentiality and manages resources against untrusted users. & IoT (healthcare) & Private & Time-dependent Consensus Algorithm & Yes & SDN/Fog computing \\
[20] & A lightweight blockchain is designed to enable the IoT domain to deploy its blockchain network on its IoT devices on the Hyperledger Fabric blockchain platform. & IoT & Not specified & Not specified & No & Edge/Cloud computing/SDN \\
[21] & A security framework as a distributed blockchain-based Platform-as-a-Service (PaaS) model is implemented to ensure data confidentiality, detect a security attack, and authorize its identification. & IoT (Wireless RAN) & Public & PoW & No & AI \\
[22] & Blockchains secure WSN transmissions, IoT communications, and mobile databases. & IoT & Private or & Not specified & Yes & - \\
[23] & Ethereum platform is used to authenticate and authorize IoT devices with smart contracts deployed as business logic. & IoT & Private or & Not specified & Yes & - \\
[24] & In smart city use cases, a secure, low-latency blockchain framework is used for drone authentication. & IoT & Public and & Customized & Yes & - \\
[25] & Providing P2P communication ensures the security of access control for IoT devices and guarantees data privacy. & IoT & Public & Delegated PoS & No & Drone/UAV \\
[26] & A fuzzy logic with blockchain technology is designed to reach Authentication, Authorization, and Audit Logs (AAA) in IoT systems. & IoT & Public and & Customized & Yes & SDN \\
[27] & Secure orchestration is developed in 5G-IoT using blockchain technology is designed to reach Authentication, Authorization, and Audit Logs (AAA) in IoT systems. & IoT & Private & Consensus & \\
[28] & Blockchain is combined with a re-encryption proxy to ensure security criteria, such as confidentiality, integrity, privacy, and access control using smart contracts Ethereum to accelerate data storage. & IoT (healthcare) & Private or & Proof of & Yes & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The main findings of the study.
## 3 DOA Estimation Based on Cyclostationary Characteristics
### DOA Estimation Based on the Cyclic-MUSIC Algorithm  
In this paper, the Cyclic-MUSIC algorithm is used to process data received by the antenna array, based on the distinct base cyclic frequencies of OFDM and DME signals and the fact that noise does not exhibit cyclostationary characteristics.  
Based on the definition of cyclic cross-correlation and the received data of the array elements, the cyclic cross-correlation function between element \(p\) and element \(q\) can be obtained as follows  
\[\mathrm{R}_{x_{p}x_{q}}^{a}(\tau)=\sum\limits_{m=1}^{L+1}\mathrm{R}_{x_{m}s_{ m}}^{a}(\tau)e^{-j\pi[(2f_{0}+\alpha)(p-1)-(2f_{0}-\alpha)(q-1)]\tau_{m}}, \tag{10}\]  
where \(\mathrm{R}_{x_{m}s_{m}}^{a}(\tau)\) represents the cyclic autocorrelation function of the signal.  
By combining different values of \(p\) and \(q\), an \(M\times M\)-dimensional data matrix \(\mathrm{R}_{1}^{a}(\tau)\) is constructed  
\[\mathrm{R}_{1}^{a}(\tau)=\left[\begin{array}{l}\mathrm{R}_{x_{1}x_{1}}^{a}( \tau)\mathrm{R}_{x_{1}x_{2}}^{a}(\tau)\cdots\mathrm{R}_{x_{1}x_{M}}^{a}(\tau) \\ \mathrm{R}_{x_{2}x_{1}}^{a}(\tau)\mathrm{R}_{x_{2}x_{2}}^{a}(\tau)\cdots\mathrm{ R}_{x_{2}x_{M}}^{a}(\tau)\\ \vdots\end{array}\right]. \tag{11}\]  
After obtaining \(\mathrm{R}_{1}^{a}(\tau)\), the pseudo-data matrix \(\mathrm{R}(\alpha)\) is constructed by combining the different \(\tau\) for the next correction:  
\[\mathrm{R}(\alpha)=\frac{1}{2D-1}\sum\limits_{\tau=-D+1}^{D-1}\mathrm{R}_{1}^{ a}(\tau). \tag{12}\]  
For the \(M\times M\) dimensional matrix \(\mathrm{R}(\alpha)\) formed by the above equation, the signal subspace and noise subspace can be obtained using singular value decomposition or eigenvalue decomposition on \(\mathrm{R}(\alpha)\mathrm{R}(\alpha)^{H}\) by the MUSIC algorithm. Subsequently, by employing spectral peak searching, the DOA of the OFDM signal can be estimated.
###### Abstract  
This study designs a differential dual-drive micro-feed mechanism, superposing the two \"macro feed motions\" (\"motor drive screw\" and \"motor drive nut\") using the same transmission of \"the nut rotary ball screw pair\" structure. These two motions are almost equal in terms of speed and turning direction, thus the \"micro feed\" can be obtained. (1) Background: Thermal deformation is the primary factor that can restrict the high-precision micro-feed mechanism and the distribution of heat sources differs from that of the conventional screw single-drive system owing to the structure and motion features of the transmission components. (2) Discussion: This study explores the thermal field distribution and thermal deformation of the differentially driven micro-feed mechanism when two driving motors are combined at different speeds. (3) Methods: Based on the theory of heat transfer, the differential dual-drive system can be used as the research object. The thermal equilibrium equations of the micro-feed transmission system are established using the thermal resistance network method, and a thermal field distribution model is obtained. (4) Results: Combined with the mechanism of thermal deformation theory, the established thermal field model is used to predict the axial thermal deformation of the differential dual-drive ball screw. (5) Conclusions: Under the dual-drive condition, the steady-state thermal error of the nut-rotating ball screw transmission mechanism increases with the increase in nut speed and composite speed and is greater than the steady-state thermal error under the single screw drive condition. After reaching the thermal steady state, the measured thermal elongation at the end of the screw in the experiment is approximately 10.5 \(\upmu\)m and the simulation result is 11.98 \(\upmu\)m. The experimental measurement result demonstrates the accuracy of the theoretical analysis model for thermal error at the end of the screw.  
Keywords:nut rotary ball screw pair; thermal field distribution; thermal deformation; differential dual-drive system; micro feed mechanism +
Footnote †: journal: Applied Sciences
## 5 Methodology
### Environment Setup Used for Evaluation  
For our evaluation environment, the network connectivity diagram is visible in Figure 3:  
Figure 3: Our network connectivity diagram, as referenced in the published Ansible code.  
We used a set of two HP ProLiant DL380 Gen10 hosts with a set of two Xeon Silver 4108 CPUs, 128 GB of memory, and the following set of network cards:  
* Built-in HP 4-port 331i Adapter, for 1 Gbit/s evaluations;
* Intel X550-T2, for copper 10 Gbit/s evaluations;
* Mellanox ConnectX EN MT26448, for optical 10 Gbit/s evaluations.  
Regarding the network switch, we used Arista's DCS-7050T-64 (copper-based evaluations) and Arista's 7050 SX-64-R (optical 10 Gbit/s evaluations).  
The main script that starts the complete evaluation suite is run_all_tests.sh [52], with a defined stack of variables. These variables can be changed to suit any environment--eliminate a set of tuned profiles or network configurations. The result of such modularity is that evaluation does not have to take as long--we can, for example, run only 10 Gbit/s performance evaluations on a couple of profiles and start evaluating. Variables that are important for that are:  
* TEST_OPTIMIZATIONS--this variable allows us to select a list of tuned profiles we want to evaluate;
* TEST_CONFIGURATIONS--this variable allows us to select a list of network configurations we want to evaluate;
* TEST_ENVIRONMENT--this variable allows us to select if evaluations will be done by using physical nodes or a Kubernetes-based environment with containers;
* BANDWIDTH_SERVER--this variable allows us to select the IP address of a server used to point bandwidth evaluations;
* LATENCY_SERVER--this variable allows us to select the IP address of a server used to point latency evaluations.  
These parameters are easy to modify, so they can work in any environment and don't require much additional background knowledge.
## 2 High-Precision Confidence Interval Weight Assignment Method for Acoustic Ranging
### Preprocessing Methods for Sound Signals  
To achieve noise reduction and mitigate the effects of multipath propagation, acoustic signals generally require modulation. The receiving end can then demodulate and obtain the required transmission time information through methods such as the cross-correlation (CC) method.  
However, in actual acoustic fields, factors such as noise and reverberation can cause ambiguity in the cross-correlation function, weakening the maximum peak and introducing spurious peaks, which complicates peak detection. To sharpen the peak, a certain weight can be added to the cross-power spectrum in the frequency domain, and then an inverse transformation can be applied to obtain the generalized cross-correlation function in the time domain. The transmission time is calculated based on the generalized cross-correlation function, which is known as the generalized cross-correlation (GCC) method.  
By preprocessing acoustic signals with the generalized cross-correlation (GCC) method, it is acknowledged that ultrasonic waves attenuate continuously as they propagate through the air. Therefore, when the distance is too great, the acoustic signal may not be correctly received or recognized; conversely, when the distance is too close, due to the beam radiation characteristics of the sound waves, receivers outside the half-diffraction angle will also fail to correctly receive the acoustic signals. Consequently, to maintain high-precision ranging results, acoustic ranging methods must operate within an appropriate ranging confidence interval to ensure that the ranging information is of high precision.
## 2 Background
### ABR Algorithm  
ABR algorithms continuously monitor changes in the user environment, such as network bandwidth and CPU usage, and transmit video content at an appropriate quality based on these factors [3]. Initially, when downloading a chunk encoded at the selected video quality, the network bandwidth is measured based on the download time and size of the video chunk. After downloading, the video chunk is stored in the playback buffer, and the videos stored in the buffer are played sequentially. The next chunk is downloaded when the size or playback time of the remaining video in the playback buffer falls below a certain threshold. The video quality of the next chunk is then selected based on the network bandwidth measured from the previous download. However, if the network environment changes suddenly, causing a significant drop in bandwidth, rebuffering can occur even if the previous network bandwidth was high, leading to all video chunks in the buffer being played.