esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results for RQ1
### ANOVA-Based Hypothesis Testing
#### 5.1.1 Organization Location  
The contingency table for organization location (i.e., Table A1) shows unequal sample sizes among the organization locations, mainly due to the larger number of municipalities in the prefectures of Hokkaido, Nagano, Tokyo, and Saitam, and the fewer municipalities in the prefectures of Toyama and Tottori.  
Alternatively, the ANOVA Welch's F test confirmed that at least one prefecture differed from another for the green policymaking initiative scale (F = 4.637; df1 = 46; df2 = 436.491; \(p<0.001\)). In particular, the Games-Howell post-hoc test found differences between the prefectures, as presented in Table A2 (\(p<0.100\)).  
From the cross-tabulation and the Games-Howell post-hoc test, we learned that this difference was explained by a group of prefectures scoring relatively higher (i.e., Score 3 or higher) than a group of prefectures scoring relatively lower (i.e., Score 2 or lower).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline
**Enablers (See Section 3.6)** & **MOEJ Survey Statement: Please Select Two Statements about Government Support That You Think Are** \\ \hline Manuals & Provision of procedures and manuals for establishing a purchasing policy. \\ \hline Example forms & Provision of examples of specifications and bidding forms for green purchasing. \\ \hline Information establishment & Provision of information on the establishment of the purchasing policy of other local governments. \\ \hline Expert assistance & Provide support and dispatch of experts to assist in the process of establishing a purchasing policy. \\ \hline Consultation desk & Establishment of a consultation desk for the formulation of a procurement policy. \\ \hline Briefing session & Briefing sessions for local government officials regarding the establishment of a purchasing policy. \\ \hline Effectiveness demonstration & Presentation of the effects of reducing environmental impacts through green purchasing initiatives for each item. \\ \hline Others &... \\ \hline \end{tabular}
\end{table}
Table 7: Overview of GPP policy enablers, linking previous research (Section 3.6) and MOEJ survey statements.
## 7 Discussion
### Open Challenges and Potential Solutions
#### 7.1.2 Data Heterogeneity Problem--Distillation Methods  
Considering the data stored on each device are personalized and have non-uniform and non-independent distributions, the learning model has difficulty achieving stable performance. This property is called heterogeneity, and the most common issue is distribution imbalance among nodes. Numerous research efforts are being conducted to address this problem. Another heterogeneity issue arises when different types of devices acquire data. Location information is collected by a wide variety of devices, and the nature of the data differs due to various differences in usage environments, communication methods, and so on. This also affects the application of the learned parameters, and a universal model must be adapted to the characteristics of each device. Dataset distillation [46] can be a solution to this problem, and it is expected to be a technique that can resolve the heterogeneity of wireless localization data during dataset collection.
## 2 Literature Review and Hypotheses Formulation  
Employees' decision to practice good mobile security can be explained by the Rational Choice Theory (RTC). People are rational and pursue their self-interests based on their preferences [18] and choose to maximize their utility among the available options [19]. The theory states that an individual rationally decides based on cost-benefit considerations, and the chosen behavior will be the one with the best expected outcome [20]. Past studies have determined that cost-benefit considerations significantly influence employees' compliance with security policies [21], internet use policy [22], and employees' satisfaction with security practices [23]. It is, therefore, postulated that employees' decisions to implement mobile security measures depend on their cost-benefit consideration. Employees weigh between the cost of conforming to good practices and the expected benefits from the action as part of their decision process. Mobile security measures will be applied if the expected benefits of the action outweigh the perceived cost.  
Nevertheless, individuals' preferences among the available choices can be selfish, altruistic, or influenced by social norms [19] and the organization they are in [20; 22]. These elements form the personal and organizational context factors that influence employees' cost-benefit considerations. Personal factors include information security awareness and mobile usage behavior, while organizational factors are training, job characteristics, and monitoring [24; 25; 26; 27]. The hypothesized relationships among these constructs are depicted in Figure 1. The next subsections discuss the cost-benefit consideration, personal aspects, and organizational context factors believed to affect mobile security practices among employees, as well as the available supports.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 5 Experimental Results
### Evaluation Metrics  
The OTB2013 and OTB2015 datasets include 11 common challenges in object tracking: illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutters (BC), and low resolution (LR). Each video sequence also has one or more attributes. The proposed tracker is evaluated by one-pass evaluation (OPE), which initializes the tracker with the ground truth bounding box in the first frame, and subsequent frames are processed by the tracker. We evaluate the performance of the proposed tracker using the following four widely used metrics in Visual Tracking Benchmark dataset:  
**Overlap success (OS) rate:** the percentage of frames in which the Intersection over Union (IOU) between the estimated target bounding box \(b_{1}\) and the ground truth bounding box \(b_{0}\) is larger than a threshold \(t\), i.e., \(\frac{b_{1}\cap b_{0}}{b_{1}\cup b_{0}}>t\).  
**Distance precision (DP) rate:** the percentage of frames in which the distance between the estimated center location of the target and the ground truth center location is smaller than the threshold.  
**Average center location error:** the average distance between the estimated center location and the ground truth center location in each frame.  
**Average FPS:** the average number of frames processed by the trackers in one second.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 2 Time-Sensitive Networking
### Ultra-Reliability  
TSN extends the 802.1Q Bridging standard and adds Path Control and Reservation (PCR) as well as Per-Stream Filtering and Policing (PSFP) standards to guarantee reliable frame delivery between source and destination. PCR extends Shortest Path Forwarding (SPF), which is based on Intermediate System-Intermediate System (IS-IS) protocol to Explicit Paths Determination using the Path Computation Element. This allows the CNC to have granular control of paths and to reserve paths for streams with high reliability requirements. Reserved paths could be point to point, point to multipoint, or multipoint to multipoint connections.  
Frame Replication and Elimination enables TSN bridges to duplicate the frames and transmit over alternative paths to ensure reliability. The TSN bridge at the receiving end then eliminates the duplicate frames and forwards the remaining frames to the destination.  
PSFP adds ingress traffic policing to the bridges via filtering based on predefined rules which can be streamID matching, priority-level matching, service class selection, or even frame counting.
## 5 Case Study: Experimental Results
### Thematic and Temporal Analysis
#### 5.1.1 Temporal Topic Flow  
The basic method of demonstrating the characteristics of topics is to depict how active they are over time. We define topic flow \(\varphi_{k}(d;w)\) of a topic \(\tau_{k}\) starting at time \(d\) (in days), with time window size \(w\), as the number of occurrences of \(\tau_{k}\) in the topic assignment within the given time interval; e.g., \(\varphi_{2}(5;w=10)\) indicates the number of occurrences of topic \(\tau_{2}\) within 10 days from the 5 th day in the dataset.  
\[\varphi_{k}(t;w)=\sum_{a_{i}\in\mathcal{A}_{d}^{d+w}}|\{j:z_{j}^{(i)}=k\}| \tag{5}\]  
where \(\mathcal{A}_{d}^{d+w}\) is the set of articles that are uploaded within the interval \([d,d+w]\):  
\[\mathcal{A}_{d}^{d+w}=\{a_{i}\in\mathcal{A}:d\leq t_{\mathtt{day}}(a_{i})<d+w\} \tag{6}\]  
Figure 2 shows the topic flow for the SD dataset with the number of topics \(K=4\) and \(w=20\). We observe that Topic 1 occupies a large portion of the first half of the dataset, and the portion of Topic 3 gradually increases with time. Topics 2 and 4 are more evenly distributed over time than the other topics.  
Figure 2: Temporal topic flow of Slashdot dataset with \(K=4\).  
From the variation of a topic over time, we can distinguish temporal topics from thematic topics. Temporal topics are formed by users who actively participate in community activity over a certain time frame, therefore the temporal factor can be determined from the difference between the maximum and minimum degree of topic activation. We define the temporality of a topic \(\tau_{k}\) as follows:  
\[\texttt{temporality}(k)=\lg\frac{\max_{d}\varphi_{k}(d)}{\min_{d}\varphi_{k}(d)} \tag{7}\]  
Note that temporal and thematic topic factors are not mutually exclusive, which means a topic can have both temporal and thematic factors. For example, if many users are involved in discussion of a political election, this user behavior can be detected as a topic that has political issues as a thematic factor, and the election period as a temporal factor.  
Table 4 shows the temporality of each topic depicted in Figure 2. We also list the topic subjects, and their classification as temporal or thematic topics according to their temporalities. It is obvious that Topic 1 is a temporal topic, because it has gradually decreased over time, and its share has almost disappeared by the end of the dataset period. Topics 2 and 4 are classified as thematic topics due to low temporality measures. Their contents are climate change and OS development, respectively. Topic 3 is determined to be a combination of the two classes. Several articles regarding technology and business tend to have topics with this classification. Its portion gradually increases over time, but the change is not as great as that of Topic 1, which has shrunk almost to zero by the end of the period.
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 1 Introduction
The total size of the double-layer printed circuit board (PCB) containing the magnetic sensing probe and the main processing module is 140 mm \(\times\) 109 mm \(\times\) 17 mm. We develop a software interface to adjust the sensitivity of unpackaged Hall elements to change the array's magnetic field measurement range. The X-Y axis motorized rail is capable of moving the magnetic sensing probe to detect and image a 15 cm \(\times\) 15 cm magnetic field area. The spatial resolution of this magnetic imaging system reaches 99.61 ppi. In addition, the special movement of this rail enables a further resolution of 199.22 ppi.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Extra Parameters  
In the same context of the WSN data integration, the researchers in [15] propose the Randomized Watermarking Filtering (RWF) algorithm along with a clustering approach in order to achieve the data filtering on its involved traffic instead of an end-to-end filtering. This process eliminates all data implanted at an early step of communication and reduces the encountered overhead. Moreover, the watermark generation depends on four factors, which include: the WSN-ID, pre-shared keys, an encrypted payload and data capture time. These factors are added to the watermark during the embedding process. Moreover, the embedding positions use the Pseudo Random Number Generator (PRNG) algorithm in order to generate them randomly. Finally, the integrity of the watermarked object is verified by the WSN master node in the perception layer and after that is sent to the upper layer. The researchers in [111] suggest a zero-watermarking algorithm in order to preserve the data integrity between the source WSN and edge router (an end-to-end protection). In this algorithm, the watermark generation depends on three factors: the frequency of data occurrence, data length and data capture time. After generating a secret watermark, the WSN adds it to their original data and forwards it to the edge router through the successor of multiple WSNs. The edge router that possesses the authority can verify and extract the data. The results of the algorithm demonstrate a better performance and lower energy consumption.
## 7 Classification of the Surveyed AI Techniques
### Swarm Intelligence Algorithms
#### Hybrid Metaheuristics  
These are metaheuristics that combine strength and minimize weaknesses of various algorithms to form an improved hybrid algorithm that outperforms the individual algorithms either in terms of speed, accuracy, computational complexity or general performance. Authors in ref. [64] propose a hybrid (k-means and search economics) metaheuristic algorithm for addressing the IoT resource allocation problem. The algorithm uses k-means clustering to create an initial solution for the SE algorithm. In ref. [85], authors use PSO, ACO, ABC to select the most relevant feature set for identifying network attacks while KNN and SVM are used to classify the performance of the feature selection algorithms. The work uses the NSL-KDD dataset for training and testing and used different metrics to determine the best algorithms that provide better overall performance when used for feature selection in intrusion detection. Results of the evaluation show that PSO, ACO and ABC algorithms perform better than other approaches in feature selection with a 98.9% accuracy rate and 0.78% false alarm with the KNN algorithm as the classifier. Other hybrid techniques used to address various cognitive sensing tasks include GA and deep belief networks (intrusion detection) [86], Search Economics, K-means and SVM (Intrusion detection system) [172], GA and K-Medoids (sensor allocation) [173], GA and Fuzzy logic (node selection and placement) [174]. Table 7 presents some of the reviewed metaheuristics and selected works detailing their strength and weaknesses for cognitive sensing tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Algorithm** & **Usage for IoT Cognitive Sensing Activities** & **Strengths** & **Weakeness** \\ \hline \multirow{6}{*}{Single-Solution Search} & \multirow{3}{*}{Table Search} & \(\bullet\) VNF placement optimization at the IoT gap and cloud [161] & * The possibility of a direct search through the selection space without a \(\bullet\) VGP (Organal load balancing between fog and cloud nodes [161] & * Not practical in problem with a large gradient formulation & * Not practical in problem with a large gradient formulation & * Not cost by too large for multi-objective tasks \\  & & \(\bullet\) Complex event processing [175] & * Flexible memory for a thorough search, & * Not cost by too large for multi-objective tasks \\ \cline{2-5}  & \multirow{2}{*}{Simulated Annealing} & \(\bullet\) Scheduling for IoT applications on clouds [162] & * Uses a probability function to select a \(\bullet\) solution which prevents working & * Increased computational cost
## 3 Survey of Techniques for Updating
### Traffic Reduction  
Traffic reduction has a direct impact on the wireless energy used, and therefore it both extends sensor network lifetime, and enables updates on networks which are near the end of their life (from an energy viewpoint).  
The three main approaches (see Figure 4) for reducing traffic are: (a) Modularity: by using modular software, so that just the necessary changed or new modules need to be transmitted; (b) Size Reduction: by using differential updates, so that just the changes from the previous version need to be transmitted; and (c) Network Coding: using network coding to maximize the benefits of overhearing in a broadcast environment with loss, and thus reduce the traffic. These approaches have been addressed in other domains, for example the rsync algorithm [41], mobile agents [42], and network coding [43].  
Figure 4: Update Traffic Reduction Approaches.  
In the following subsections, the two direct size-reduction techniques are reviewed (differential updates and network coding). Modular updates which require a supporting execution environment are discussed in Section 3.3.
## 2 Circuit Model and Theoretical Analysis
### Determination of Compensation Capacitor Value  
Based on the above analysis, to realize the CCO and CVO characteristics and ZPA operation meeting battery charging requirements, the compensation capacitors \(C_{1}\) and \(C_{2}\) must simultaneously satisfy Equations (9) and (17). Therefore, the expressions of compensation capacitors \(C_{1}\) and \(C_{2}\) can be obtained by associating Equations (3), (9) and (17).  
\[\left\{\begin{array}{l}C_{1}=\frac{1}{(\omega_{C_{1}}^{2}-\omega_{C_{2}}^{2} )L_{1}}\\ C_{2}=\frac{(\omega_{C_{1}}^{2}-\omega_{C_{2}}^{2})^{3}L_{1}}{2\omega_{C_{2}}^{ 2}\omega_{C_{2}}^{2}M^{2}}\end{array}\right. \tag{20}\]
## 2 Materials and Methods  
### Software Description  
The software is intended for supporting the process of carrying out satellite measurements of linear objects. It allows a GNSS measurement campaign to be planned at a time when the satellite positions around the inspection vehicle are optimal while taking into account the specificity of linear objects, including the fact that the surveys are carried out in motion and during a long time interval. The calculations take into account the effect of field obstacles on the satellite visibility. As a result of its operation, the program determines the schedule for the optimum navigation of a linear object. The inspection vehicle operator is informed about the locations at which the vehicle should stop in order to wait for the end of the unfavourable satellite constellation status and about the time when it should start again.  
The software operation is not complicated. The user enters the time frame during which the campaign can be implemented, the parameters of the inspection vehicle's movement, the geometry of the test linear object, the current ephemeris data, and the elevation model of field obstacles. Once the calculations are made, the user obtains a schedule of the inspection vehicle's movement (the locations and times of stops and starts) that guarantees the globally optimal navigation constellation status during the GNSS measurement campaign. In addition, visualisations of the calculations made and the results obtained are also presented.
## 3 Methodology
### Transmission
#### 3.1.1 Device Initialization and Authentication  
The quantum device \(D\) is first initialized and assigned a unique identity through a secure MAC address generated using the CHMAC-EPQH algorithm. This approach combines HMAC with post-quantum hash functions to ensure robust authentication and message integrity, offering resistance against quantum computing-based threats.  
To reduce key management complexity and storage overhead, the secret key \(\tau\) generated for \(D\) undergoes compression and encoding. The compression is performed using a transformation function:  
\[\tau^{comp}=\Im(\tau) \tag{1}\]  
The compressed key \(\tau^{comp}\) is then encoded using a post-quantum encoding function \(e_{n}\):  
\[\tau^{\prime\prime}=e_{n}(\tau^{comp}) \tag{2}\]  
Authentication is achieved using the encoded key \(\tau^{\prime\prime}\) in conjunction with padding constants \(o^{\prime}\) and \(o^{\prime\prime}\) and a hash function \(\tilde{h}\):  
\[\tilde{J}=\tilde{h}((\tau^{\prime\prime}\otimes o^{\prime})\|\tilde{h}((\tau^ {\prime\prime}\otimes o^{\prime\prime})\|D)) \tag{3}\]  
Here, \(\tilde{J}\) represents the authenticated device, and \(\otimes\) denotes the XOR operation. Once the generated MAC and \(\tilde{J}\) have been verified, secure communication is established between the authenticated device and the network.  
Figure 1: Structural diagram for the proposed methodology.
## 2 Chang'E-5T Onboard GPS Data Analysis
### Onboard GPS Data  
As shown in Figure 1, during the return periods, the receiver was able to acquire and track at least 22 GPS satellites. In most of time, the signals from eight GPS satellites were tracked simultaneously. The length of continuous tracking periods of four GPS satellites are near two hs, for instance Pseudo Random Noise (PRN) G11, G21, G22 and G32.  
Figure 2 shows the measured signal levels of all the GPS satellites during the returning arc. It should be noted that the original C/N0 values were recorded in integers. The variations of C/N0 are very significant, approximately 25 dB-Hz peak-to-peak. For PRN G16, G19 and G27, the C/N0 values were basically larger than 35 dB-Hz because the Chang'E-5T probe was in the coverage of their main-beam signals; while the line-of-sight of the probe pierced the ionosphere and troposphere, i.e., GPS occultation, the C/N0 values dropped dramatically. Figure 3 is a statistic histogram of all the C/N0. The C/N0 values are mainly concentrated between 28 dB-Hz and 35 dB-Hz, much smaller than those of terrestrial and low earth orbit receivers. Even though the designed tracking sensitivity threshold of the GNSS receiver is 26 dB-Hz, there are also about four hundred observations weaker than the thresholds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Design Parameter** \\ \hline Compatible frequency & GPS L1 and GLONASS L1 \\ Original observation types & carrier phase, pseudo-range, Doppler and C/N0 \\ Number of channels & 16 for GPS, 8 for GLONASS \\ Sampling rate & 3 s for ascent arc, 1 s for return arc \\ Acquisition sensitivity threshold & 29 dB-Hz \\ Tracking sensitivity threshold & 26 dB-Hz \\ GNSS Antenna & 2, quadrifilar helix, earth-pointing and the opposite direction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary parameters of the Chang’E-5T space-borne GNSS receiver.  
Figure 1: GPS satellites tracked by Chang’E-5T onboard receiver on 31 October 2014.  
Figure 3: The number of observations with different C/N0 (unit: dB-Hz).  
Figure 2: The measured signal level of GPS Satellites: C/N0 (unit: dB-Hz).
# Survey on Power-Aware Optimization Solutions for MANETs
## 5 Challenges and Future Research Directions
### Challenges
The integration of the cross-layer design idea with fuzzy-logic support can enhance the performance of multicast routing protocols. In this direction, [PERSON] et al. [232] proposed the Cross-layer optimized Multicast Route finding Protocol (C-MRP), integrated with a light fuzzy-logic set of rules for selecting optimal multiple routes to a set of destination nodes based on available network bandwidth, route stability, and node-to-node delay. Experimental results demonstrated that C-MRP outperforms other multicast routing protocols in all characteristics.