esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
###### Abstract
The effective outcome of FPA gait modification is mainly adjusting the peak values of KAM in a short period of time [10; 11]. Because it is very difficult to directly measure TF contact forces, KAM has often been used for predicting total TF force and has been considered as a proxy for medial TF contact force [9; 16; 19]. From literature, it is not clear whether KAM should be used as a surrogate measure for total TF force or medial TF contact force. Higher KAM is related to radiographic structure changes at knee joints and cartilage degeneration [1; 20]. Some research work also has suggested that the knee flexion moment (KFM) also plays an important role in knee loading and that it should also be taken into account [14; 21]. [PERSON] et al. [14] suggested that the combination of KAM and KFM yields an improved surrogate measure for TF contact force than KAM alone; however, it improved only the first peak values not the second peak values of knee contact force during the stance phase. While FPA gait modification showed that the first peak KAM is reduced, it remains unclear whether these treatments subsequently increased the peak KFM and TF contact force. Previous studies showed that the reduced peak KFM also carries the risk of causing quadricep weakness, which might be associated with developing knee osteoarthritis (OA) [7; 21]. Increasing quadricep strength could improve functional outcomes and could alleviate pain in patients with OA [22]. However, it is still unclear whether an increase in KFM is related to quadriceps strength and whether it would increase the KAM and/or the TF contact force, which further affects the progression of OA [20]. On the other hand, modified gait patterns will involve changes at more than one body segment and one joint. Positive benefits at the TF joint might be linked with negative impact on other joints concurrently [11]. Furthermore, muscle coordination also plays a key role in contributing TF contact force [23; 24; 25; 26]. [PERSON] et al. [27] conducted only computer simulations to predict contact forces by gait modification; they reported that gait modification can reduce the peak KAM from normal walking. From the literature review, it was found that there was little information on the study of Chinese subjects through gait modifications, especially lacking analysis by using musculoskeletal modelling. To address the above issues, quantitatively comparing differences in lower-extremity kinematics, dynamics, and electromyography caused by FPA gait modification is important and will be conducted herein. Therefore, the first objective of this paper was to study the effects of toe-in and toe-out gaits through experimental measurement and computer modelling at normal walking speeds on lower-extremity joint angles, GRF, electromyography (EMG) of muscles crossing the knee joint, lower-extremity joint moment, and TF contact force during level walking. The second objective was to evaluate whether KAM can be used as a surrogate measure for medial or total TF contact force.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
###### Abstract  
Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model's coefficients of determination (R\({}^{2}\)s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 \(\mu\)g/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 \(\mu\)g/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake.  
dung beetle optimization algorithm; XGBoost; Sentinel-2; Wuliangsu Lake; Chl-a Concentration
## 5 Case Studies: Part II: Real-World Measured Data Analysis
### Engineering Effectiveness Analysis of the Proposed PQD Classification Algorithm
In summary, due to factors such as environmental noise, operational conditions, and measurement errors, real-world disturbance signals exhibit greater complexity and coupling than ideal simulated signals, posing a significant challenge for accurate classification. Nevertheless, the proposed algorithm achieves a high classification accuracy of 91.30% on complex real-world measured disturbance signals, thereby verifying its effectiveness and reliability in practical engineering applications.
# Blockchain-Based Quality Assurance System for Academic Programs
## 3 The Proposed Blockchain-Based QAS
### System Requirements for Accreditation Processes  
The process of accrediting a program is a crucial part of the quality assurance system (QAS). This section outlines the requirements for a blockchain-based system for program accreditation. Figure 6 depicts the sequence diagram for the processes and actions that are carried out by the entities involved in the accreditation process.  
The system uses IPFS technology to store quality files and smart contracts to perform transactions. The accreditation process involves program and quality deans from a particular HEI, the accreditation authority, and independent reviewers. All parties interact with each other during the program accreditation process.  
The first step involves the program representatives creating and storing accreditation requirements and reports using the IPFS. The hash code and program information are then input into the smart contract. In the next stage, the quality deanship requests an independent review. The review request form includes the smart contract ID and the hash code of the program files that are stored using the IPFS. The reviewer can access the program quality reports by searching using the hash code. The reviewer then creates and stores the review report, with the hash code and version number being input into the smart contract.  
Once the review report is complete, the quality deanship representative approves the accreditation request and signs the smart contract. Once the smart contract is signed, the formal request for accreditation, along with related quality reports, is automatically sent to the accreditation authority. All transactions and quality reports are registered on the blockchain to ensure protection from manipulation or falsification. The blockchain also enables easy tracing of all transactions and quality documents.  
Figure 5: The sequence diagram for internal QAPs.  
Using blockchain technology ensures the confidentiality of users involved in the accreditation processes. For instance, the personal information of independent reviewers is not exposed to program or college representatives because they will be using the blockchain platform through their wallet addresses or public keys.  
After presenting a basic prototype implementation, the following section discusses the security features and efficiency improvements that can be achieved by the proposed blockchain-based platform for QAPs.
## 2 Materials and Methods
### Theoretical Calculations Model and Equations
#### 2.6.3 Economic Analysis  
Even if the system is a prototype, it will be valuable to perform a primary evaluation of the economic viability of its operation. The method that will be used is the payback period analysis, which has also been used in similar applications [22; 23]. There will be two payback period scenarios: one for the examined prototype system and one for the case that it was installed on a larger commercial scale. The payback period is given by Equation (5) [23].  
\[\mathrm{P_{b}}=\frac{\mathrm{C_{i}}}{\mathrm{P_{net}}} \tag{5}\]  
\(\mathrm{P_{b}}\): payback period (years)  
\(\mathrm{C_{i}}\): capital investment cost (\(\mathrm{\SIUnitSymbolCelsius}\))  
\(\mathrm{P_{net}}\): annual net profit value (\(\mathrm{\SIUnitSymbolCelsius}\))  
The \(\mathrm{P_{net}}\) value is defined by the difference in income minus the cost of the system's operation. In the examined case, the cost values that will be included are  
* The cost of fresh fruits or vegetables (\(\mathrm{C_{f}}\));
* The energy cost (\(\mathrm{C_{e}}\)).  
While the income values will be taken are:  
* The income by selling drying products (I.d). All values will be assumed from current data of the Greek market. The income parameters that will be excluded are potential subsidies, or income by parallel activities in the greenhouse structure. On the other hand, tax, insurance, or costs that relate to parallel activities in the greenhouse will be excluded.
## 1 Introduction  
In this paper we consider the problem of clustering similar sequences of 3D points. Two such sequences of points are considered the same if they are equivalent under rotation and translation. The scenario which we consider is as follows. Suppose there is an original sequence of points that gave rise to a few variations of itself, through slight changes in some or all of its points. Now given these variations of the sequence, we are to reconstruct the original sequence. A likely candidate for such an original sequence would be a sequence which is \"nearest\" in terms of some distance measure, to the variations.  
A more complicated scenario involves \(k\) original sequences of the same length. Formally, we formulate the problem as follows. Given \(n\) sequences of points \(f_{1},f_{2},\ldots,f_{n}\), we are to find a set of \(k\) sequences \(g_{1},\ldots,g_{k}\), such that the sum of distances  
\[\sum_{1\leq i\leq n}\min_{1\leq j\leq k}dist(f_{i},g_{j}) \tag{1}\]  
is minimized. In this paper we consider the case where \(dist\) is the _minimum_ sum of squared Euclidean distances between each of the points in the two sequences \(f_{i}\) and \(g_{k}\), _under all possible rigid transformations on the sequences of points_. A cost function in the form of the squared Euclidean distance is used in many techniques for clustering 3D points [1]. Since our clustering problem is quite different from those previously studied, it calls for a new technique. (The \"square\" in the distance measure is to fulfill a condition needed by the method in this paper. The method does not work, for example, in the case of the root mean squared Euclidean distance. On the other hand, the method easily adapts to other distance measures that fulfill the required condition.)  
Such a problem has potential use in clustering protein structures. A protein structure is typically given as a sequence of points in 3D space, and for various reasons, there are typically minor variations in their measured structures. The problem can be considered a model of the situation where we have a set of measurements of a few protein structures, and are to reconstruct the original structures.  
In this paper, we show that there is a polynomial-time approximation scheme (PTAS) for the problem, through a sampling strategy. More precisely, we show that an optimal solution obtained by sampling smaller subsets of the input suffices to give us an approximate solution, and the approximation ratio improves as we increase the size of the subsets we sample.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Network Security
#### 6.5.3 Cryptographic Mechanisms  
There are also specific cryptographic protocols and mechanisms that can be used with both network emulation platforms, at the host and VM/container level, including:  
* **Secure Shell (SSH)**: _Host Systems_: Secure Shell (SSH) should be used to secure remote access to the host systems where GNS3 and the SEED Internet Emulator are installed. Ensure that the host systems have been configured to allow SSH connections only from trusted sources and strong authentication methods are enforced. _VMs/containers_: Within GNS3 and the SEED Internet Emulator, SSH can be used to securely access and manage _VMs and containers_. By connecting to the VMs/containers through SSH commands and using IP address filtering, you can control access and protect sensitive configurations.
* **Internet Protocol Security (IPSec)**: _Built-in capabilities_: Both host systems, GNS3, and the SEED Internet Emulator, support IPsec as a suite of protocols for securing communication at the IP layer. Ensure that IPsec is correctly configured on the emulated network devices and _VMs/containers_ to encrypt and authenticate network packets, maintaining confidentiality, and integrity.
* **Transport Layer Security (TLS)**: _Built-in capabilities_: GNS3 and the SEED Internet Emulator enable secure communication using TLS. In GNS3, TLS can be utilized for encrypted communication between compatible virtual appliances and machines within the environment. SEED Internet Emulator and TLS: Although the SEED Internet Emulator does not natively provide support for TLS, you can manually configure TLS on the operating system that the SEED Internet Emulator is installed on. This ensures secure communication within the emulator environment.
* **Virtual Private Network (VPN)**: Both platforms can be configured with either Open-VPN or IPsec VPN to establish secure network connections between physical and virtual locations. A VPN server can be set up on a virtual appliance to simulate secure communications between virtual clients and locations.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
### Our Contribution  
This paper focuses on simulation of the multi-UAV coverage path planning problem and the corresponding solution algorithms. The main research outcomes of this paper are the following:  
* We formulate the covering path planning problem in a more rigorous manner as an objective optimization problem with multiple constraints.
* We add a new constraint, \"energy\", to the previously proposed algorithm and optimize it.
* We compare the performance of the proposed heuristic algorithm for each of the six different route cases.  
The remainder of the paper is organized as follows: Section 2 describes the relevant theories and the research status of UAV path planning. First, UAV energy consumption is briefly analyzed. Then, the research status of UAV path planning is introduced from three points of view: a single UAV performing a single objective task, a single UAV performing a multi-objective task, and a multi-UAV performing a multi-objective task. Finally, the route planning is further analyzed with the help of the traveling salesman problem. Our methodology is presented in Sections 3 and 4. The methodology is divided into two parts: Section 3 concerns the planning of the UAV path, and Section 4 concerns the multi-UAV cooperative search algorithm. Section 3 focuses on the influence of UAV energy consumption and time on cost and describes the multi-UAV path planning scheme. In Section 4, according to the system scenario and assumed conditions, problem modeling is carried out to minimize the total UAV time. Multiple path planning is carried out, and the multi-UAV cooperative search algorithm is used to plan the flight path with minimum total energy consumption. In Section 5, the algorithm is simulated and compared with the existing algorithm to analyze its performance. Section 6 discusses the results and implications of the study, as well as the limitations of the study. Finally, Section 7 concludes the paper. The overall structure of this paper is shown in Figure 1.  
Figure 1: The overall structure of this paper.
## 5 Performance Evaluation  
In this section, we compare and analyze MPI communication performance in high-performance memory and Python environment through experiments. The server used in the experiment consists of a KNL processor with 68 cores, 16 GB of MCDRAM, and 96 GB of DDR4. For software (SW) environment, we used Linux (Kernel Version 5.2.8) and Intel MPI (Version 2017 Update 3). Due to the GIL feature of Python, experiments were conducted by exchanging data using MPI, which is a multi-processing method instead of multi-threading. We tested collective communication of Broadcast, Gather, and AllReduce methods, which are widely used as MPI communication methods in parallel and distributed deep learning. Python's MPI communication has two methods: Object and Buffer communication [43]. Object communication provides Pickle-based communication to conveniently communicate objects in Python. Object communication uses all lowercase methods, like bcast(), gather(), and allreduce(). Buffer communication is an efficient communication method for array data communication, such as NumPy. Buffer communication uses uppercase, like Bcast(), Gather(), and AllReduce(). The Pickle base of object communication converts object data into byte streams and byte streams back into object data [44]. On the other hand, buffer communication allows direct access to object data using the buffer interface [45]. The objects supported by the buffer interface are byte and array objects. In this study, buffer communication can be used because the Numpy array is used in the experiment. Object communication is simple to use but has an overhead compared to buffer communication, which does not require a conversion process.  
In Sections 5.1-5.3, to evaluate the proposed scheme, the Cluster mode of the KNL processor was tested in Quadrant mode. Memory mode was set in Flat mode and Cache mode for comparison. Different combinations of DDR4 and MCDRAM were compared by changing the number of processes in C and Python environments. In addition, to see the Python communication performance characteristic, we used the Buffer and Object communication methods. In order to consider the distributed environment, we conducted an experiment on distributed communication in Docker's virtual environment. In Section 5.4, we compare Quadrant mode and SNC-4 mode among Cluster modes of KNL processor.
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 2 Related Works
### Video-Based Person Re-ID  
Video-based person Re-ID can be seen as an extension of image-based person Re-ID efforts. Compared to static images, video can provide richer information for person Re-ID tasks because it contains both spatial and temporal information. Video-based person Re-ID is also closer to the real world for better application. So, in recent years, video-based person Re-ID has also attracted the attention of more researchers. Some early work [5; 24; 25] considered frame-level similarities to identify the person. Recently, deep learning methods have been applied to gain more discriminative video-level features. They first trained the CNN network to extract image features and then aggregated them into video features through average or maximum pooling. [PERSON] et al. [5] proposed a method for extracting time information using RNN and the temporal pooling layer. Following [5], [PERSON] et al. [7] proposed a spatial and temporal attention pooling network (STAPN), which extracts more robust  
Figure 1: Some examples of the application of non-local attention in our network. The starting point of the arrow represents one frame in a video, and the picture pointed by the arrow represents another frame of the video. For brevity, we only selected four frames in the figure to show how our model directly finds relevant clues in frames to support its prediction. The ‘blue’ arrow represents the similarity between the first frame and the remaining frames, and the ‘green’, ‘red’, and ‘orange’ respectively represent the similarity of the second, third, and fourth frames with the remaining frames.  
features by calculating attention in the spatial and temporal dimensions. [PERSON] et al. [13] proposed a new spatial-temporal attention model to distinguish different body parts automatically.
# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## 4 Experiments and Results  
In this section, we first describe the configuration parameters and the connections of the measurement equipment used in the experimental stage to acquire the energy consumption of each node of the three networks. The data collection experiments were repeated ten times, all under the same scenario, to characterize the statistical behavior of the energy consumption of each element of the networks. Next, we present the dynamics of the consumption signals that were analyzed with custom-developed software in Matlab\({}^{\text{TM}}\). Selected and representative graphs of time series, probability distributions, and SAF, were studied in each node and at different transmission rates. The scheme of the comparative study was the following. Initially, we defined some relevant characteristics of the different graphical representations of the DigiMesh network, as given by its trend, mean, standard deviation, probability distribution, and seasonality, at a fixed and low rate of 9600 bauds. Next, we compared these signals with other transmission speeds, and we identified the significant peculiarities and got the corresponding conclusions. The same procedure was applied to the ZigBee and WiFi networks. We end with a global analysis of the results of the three networks to discern the incidence of communication technology and the transmission rate with respect to the battery consumption of each kind of node.
###### Abstract  
Background: Transarterial radioembolization (TARE) relies on directly injected \({}^{90}\)Y- or \({}^{166}\)Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE \({}^{99m}\)Tc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., \(\mathrm{D_{T}}\) and \(\mathrm{D_{NTL}}\), respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on \({}^{90}\)Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and \(\mathrm{D_{T}}/\mathrm{D_{NTL}}/\mathrm{D_{L}}\) were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson's (PCC), concordance (CCCo) and Bland-Altman analyses. The Kaplan-Meier curves of overall survival (OS) were calculated according to \(\mathrm{D_{T}}\). Results: All computed CCS indicated very good (>0.92) agreement for volume comparison, while they suggested good (ICC \(\geq\) 0.869, PCC \(\geq\) 0.876 and CCCo \(\geq\) 0.790) and moderate agreement in the intra- and inter-modality \(\mathrm{D_{T}}/\mathrm{D_{NTL}}/\mathrm{D_{L}}\) comparisons, respectively. Bland-Altman analyses showed percentage differences between the manual and deformable approaches of up to about \(-31\%\), \(9\%\) and \(62\%\) for tumoral volumes, \(\mathrm{D_{T}}\) and \(\mathrm{D_{NTL}}\), respectively. The overall survival analysis showed statistically significant differences using \(\mathrm{D_{T}}\) cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusions: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic \(\mathrm{D_{T}}/\mathrm{D_{NTL}}\) constraints.  
Keywords:transarterial radioembolization; dosimetry; image registration; delineation +
Footnote †: journal: _Sciences_