esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
###### Abstract  
Today's terrestrial cellular communications networks face difficulties in serving coexisting users and devices due to the enormous demands of mass connectivity. Further, natural disasters and unexpected events lead to an unpredictable amount of data traffic, thus causing congestion to the network. In such cases, the addition of on-demand network entities, such as fixed or aerial base stations, has been proposed as a viable solution for managing high data traffic and offloading the existing terrestrial infrastructure. This paper presents an unmanned aerial vehicles (UAVs) aided offloading strategy of the terrestrial network, utilizing an unsupervised machine learning method for the best placement of UAVs in sites with high data traffic. The proposed scheme forms clusters of users located in the affected area using the k-medoid algorithm. Followingly, based on the number of available UAVs, a cluster selection scheme is employed to select the available UAVs that will be deployed to achieve maximum offloading in the system. Comparisons with traditional offloading strategies integrating terrestrial picocells and other UAV-aided schemes show that significant offloading, throughput, spectral efficiency, and sum rate gains can be harvested through the proposed method under a varying number of UAVs.  
5G; machine learning; UAV placement; offloading Article
## 4 Cache-Supported ICN Router
### Implementation
#### 4.2.2 Forward Element  
The FE is mainly responsible for routing and forwarding the received IP and ICN packets. We deployed an open source POF software switch on the forward core to implement the _packet routing_ module. We did not need to add CCN-like data structures like Pending Interest Table (PIT) and (Forwarding Information Base) FIB [9] in the POF, and, as consequence of that, the NRS decouples content addressing from data forwarding. We could code the customized routing strategy according to our needs, and then the controller becomes able to issue the corresponding flow table to the router.  
We extended POF switch from following three aspects:  
1. In order to save precious memory resources, the router should not send ICN packets to the CE when the corresponding chunk has been cached. Therefore, we maintained an index table (IT) for name of cached chunks in POF. Considering the advantage of the CBF that the time of inserting and searching is independent of the number of entries in the table, we implemented the IT via the CBF.
2. We added the new POF instruction _outmulti_ (_port_, _queue_) in a data plane based on the instruction _output_ (_port_,) which forwards packets out from _port_. If the FE decides to cache the ICN packet, _outmulti_ (_port_, _queue_) puts a copy of the packet on the virtual _queue_ between processes while forwarding the packet to the _port_. This instruction also can be used for the realization of the multicast function of ICN.
3. POF cannot maintain line speed forwarding under a data-intensive environment because many CPU cycles are consumed in switching between the kernel and user modes of the Linux  
Figure 4: Implementation of the cache-supported ICN router.  
network protocol stack. We optimized the forwarding performance of OPF through Data Plane Development Kit (DPDK) technology [38], which accelerates network packet processing by exposing the handling memory area of packet to user-space for DMA with zero-copy.  
Moreover, to reduce memory copies and avoid the delay caused by reliable transmission, we implemented the _request response_ submodule in the TX core. The TX core polls I/O buffers. If any chunk is placed on the queue, the TX core sends it to the ICN transport protocol stack.
# Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference
## 2 Methodology
### Recurrence Plots with the RQA Method  
The definition of a recurrence plot is grounded in phase space reconstruction, reflecting the properties of the reconstructed attractor. Given the varying nature of vibration signals, the manifestation of their attractors differs, resulting in distinct structures in recurrence plots [16,17].  
The algorithm for constructing a recurrence plot is outlined as follows:  
\[R_{i,j}(\varepsilon)=\Theta\Big{(}\varepsilon-\|\overrightarrow{x}_{i}- \overrightarrow{x}_{j}\|\Big{)}\quad i,j=1,2,\cdots,N \tag{15}\]  
where the matrix \(R_{i,j}\) denotes an \(N\times N\) size matrix, with \(N\) corresponding to the number of elements in the state vector \(\overrightarrow{x}_{i}\). The symbol \(\varepsilon\) signifies a predetermined threshold, and \(\Theta(\cdot)\) represents the Heaviside step function (\(\Theta(y)=0\), if \(y\leq 0\); \(\Theta(y)=1\), if \(y>1\)). The norm \(\|\cdot\|\) is employed to quantify the magnitude of vectors. Selecting an appropriate norm is essential for accurately constructing a recurrence plot.  
For a given signal, the phase space can be reconstructed using a time-lag approach through the following steps. Subsequently, a recurrence plot for this signal can be constructed.  
Figure 1: Comparison between the filtered signals and the original signals (the blue line is the original signal, and the red line is the filtered signal). (**a**) Filter contrast 1; (**b**) filter contrast 2.  
1. Assuming the initial signal is \(\overrightarrow{x}_{t}\), it is reconstructed as \(x_{i}\). \[x_{i}=(u_{i},u_{i+\tau},\cdots,u_{i+(m-1)\tau})\quad i=1,2,\cdots,N-(m-1)\tau\] (16) An appropriate embedding dimension \(m\) and delay time \(\tau\) are chosen based on the characteristics of the signal. 2. A suitable recurrence threshold and critical distance are determined to facilitate the subsequent matrix calculations. 3. The recurrence matrix is computed according to the specified equations. 4. Based on the principles of recurrence plot construction, each point in the recurrence matrix is plotted along two axes to obtain the recurrence plot of the studied signal. Recurrence plots for the sine and Lorenz signals were generated using programming methods [16], as depicted in Figures 2 and 3.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 5 Discussion
On the other hand, for _New Space_ missions, controllable risk acceptance is one of the fields to look for savings. Lower system availability, if justified, might be acceptable. Even slightly reduced system reliability might be considered if the system is understood as a single satellite in the constellation, where the mission (constellation) success is possible with partial loss of satellites. This opens the doors for use of limited TID/SEE qualification scenarios for the final qualification in _New Space_ applications.
## Materials and Methods
### Study Selection and Data Extraction  
The screening process was performed using the digital tool, Raysan [25] and reported in Figure 1. The study's selection process consisted of three phases: eliminating duplicates, screening the titles and abstracts, and reading full texts.  
After the removal of duplicates, all identified records were blinded and screened for potential relevance based on title and abstract by three independent researchers ([PERSON], E.T., [PERSON]). The author independently reviewed studies for eligibility as per the inclusion/exclusion criteria and extracted the data according to the criteria defined in advance. Disagreements were discussed with the team and resolved. After these initial screening steps, full texts were independently reviewed by the first three authors, considering the specific reference to the eligibility criteria. To reach a joint decision, the emerging discrepancies were discussed within the team. An inter-rater reliability analysis was performed using the Fleiss Kappa (\(\kappa\)) test, which measures the agreement between more than two evaluators.  
For articles suitable for inclusion in this systematic review, the first three authors extracted data related to study characteristics (i.e., target group, intervention, control group, etc.), assessment of outcome, intervention characteristics (i.e., play frequency and intensity, cognitive skills trained, type of element of gamification), and efficacy. See Tables 1 and 2 for a description of the studies included and their type of training.  
To describe the characteristics of each SG included in this systematic review, we used the Quality Criteria for Serious Games developed by [26]. They divide the evaluation into two sections: the serious section, which indicates the specific objectives that differentiate SGs from other types of gamifications; and the game section, which describes the contents necessary to highlight a playful component within these learning tools. We also analyzed the integration between the characterizing learning objective (serious section) within the gameplay (game section) according to the indications provided by [26]. Tables 3 and 4 present a detailed description of each section. The qualitative evaluation for each SG included in this systematic review was performed independently by the second and third researchers, and the percentage of agreement and [PERSON]'s K were computed.  
Figure 1: PRISMA flowchart of the literature search and screening process.
## 4 Results and Discussion
### Vision-Based Detection Experiment  
The UAV employs different detection modes based on its operational states. During terrestrial locomotion, visible-light-based visual recognition and ranging are utilized for the precise detection of target position and distance. In the aerial flight mode, the system activates infrared vision-based detection for target presence verification. This dual-mode sensing strategy arises from operational requirements: terrestrial locomotion necessitates a precise proximity detection of target position and range, while aerial operations require only target presence identification. Such adaptive sensing architecture significantly enhances the efficiency of search and rescue operations.  
A ranging accuracy validation experiment was conducted under the visible-light recognition mode, with test subjects positioned at 300 discrete locations relative to the depth camera for distance measurement. Representative data are summarized in Table 5, where \(\alpha\) denotes the target distance measured by the proposed stereo vision-based ranging method, \(\beta\) represents the ground truth distance obtained via a laser rangefinder, and the relative error (\(e\)) is calculated as  
\[e=\left|\frac{\alpha-\beta}{\beta}\right|\times 100\% \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP/\%** &
\begin{tabular}{c} **Average Inference** \\ **Time/ms** \\ \end{tabular} & **Parameters/M** & **FLOPs/G** \\ \hline YOLOv5s-Ghost & 77.8 & 0.7 & 5.4 & 11.5 \\ YOLOv5s & 78.7 & 0.9 & 7.2 & 16.5 \\ YOLOv5n & 74.4 & 1.3 & 1.9 & 4.5 \\ YOLOv5m & 80.8 & 6.7 & 20.8 & 48.2 \\ YOLOv5l & 81.4 & 11.1 & 46.1 & 107.9 \\ YOLOv7-tiny & 80.0 & 4.0 & 6.0 & 13.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different models in detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**a/m** & \(\beta\)**/m** & \(e\)**/\%** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Distance measurement results.
## 4 Structural Control Mechanisms on Coal and Gas Occurrence in Mines
### Stepwise Structural Control of Gas Occurrence  
#### 4.2.1 North China Craton's Control on Regional Gas Generation, Migration, and Preservation  
The evolution of the North China Plate and its interactions with surrounding plates influence the formation and deformation of coal-bearing basins. This, in turn, controls the formation and distribution of tectonic coal, thereby regulating the generation, migration, and preservation of coal mine gas [27; 28; 29; 30; 31]. The marginal zones of the coal-rich areas in North China experienced prolonged and intense plate collisions and compressions, leading to significant structural deformation in the coal-bearing strata. During this process, as compressive stress weakened from the plate margins toward the interior, the intensity of coal-bearing strata deformation also decreased. This created a stark contrast in structural deformation, with intense compressional deformation at the margins and more differential deformation toward the interior [30]. Previous research has shown that near the northern margin of the North China Basin, thrusting from the northern Hebei orogen created a high-gas-pressure zone (gas outburst zone), and the study area is situated within this unique geological belt. In this region, the mechanisms of gas generation, migration, and storage have been profoundly influenced by the tectonic activity of the North China Plate. The compressional structures of the Yanshan orogen and the northern margin of the North China Plate jointly played a decisive role in the deformation of coal seams in the study area,thereby influencing the spatial distribution pattern of tectonic coal. The differential spatial distribution of tectonic coal and the associated tectonic stress field have led to a distinct belt-like pattern of regional gas distribution [31] (Figure 8).
## 4 Materials and Methods  
### Bacterial Strains and Growth Conditions  
The eight endophytic strains were isolated following the protocol reported in [PERSON] et al. (2020). Bacteria were obtained from different compartments of _O. vulgare_ ssp. _vulgare_ plants (2 isolates from flower, 2 from leaf, and 4 from stem compartments). Isolates are referred to as OV followed by F, L, or S for flower, leaf, and stem districts, respectively. The selected isolates demonstrated the highest growth inhibitory activity against ten Bcc strains [27]. Six of them belong to the _Bacillus_ genus (OVF22, OVL9, OVL24, OVS6, OVS21, and OVS26), while OVS8 and OVF10 belong to the _Arthobacter_ and _Paenibacillus_ genera, respectively. Each strain was cultured on tryptic soy agar (TSA) medium (BioLife) for 48 h at 30 \({}^{\circ}\)C [27].  
Ten strains of the _Burkholderia cepacia_ complex (Bcc) collection, MDR bacteria able to resist different antibiotic classes, were selected based on their multiple drug resistance profile. The bacterial strains were isolated either from cystic fibrosis patients or the environment [20; 44]. Each strain was cultured on LB agar medium (NaCl 10 g/L, yeast extract 5 g/L, tryptone 10 g/L, agar 15 g/L) for 48 h at 37 \({}^{\circ}\)C.
## 3 Materials and Methods
### Methods
#### 3.2.2 Survival Analysis  
Survival analysis is a statistical methodology used in biostatistics to study the duration of the life of an entity [27]. The approach is based on measurements of events that can occur at any time during a study. For example, survival analysis can be used to model the time until death after a treatment intervention, tumor recurrence, or presence of disease in patients. Survival analysis has already been applied to analyze open-source software development. [PERSON] et al. used commits by new developers as their event to analyze the effects of the introduction of buggy code to a software repository [28]. [PERSON] et al. used survival analysis to study the effect of different code constructs on the overall resolution time of bugs in open-source software repositories [29]. The event of interest in our analysis is the resolution of a simple stupid bug in the repositories of top Java open source projects.  
The Kaplan-Meier (K-M) survival estimator is a widely used method for estimating the survival function [30], where the survival function \(S(t)=P(T>t)\) gives the probability that a subject survives longer than some time t [31]. The K-M estimator produces a curve that approaches the true survival function for the data. This method allows us to compare the survival probabilities of SSTUBs, with different attributes. While the K-M curves give us a visual representation of the bug resolution over time, the Cox Proportional-Hazards (Cox PH) model allows us to fit a regression model to investigate the association between the bug resolution time and key commit attributes. The Cox model uses a hazard function, defined as \(h(t)=h_{0}(t)\cdot e^{\beta X}\), under the assumption that the baseline hazard function \(h_{0}(t)\) is left unspecified, and the hazards for two subjects, with different covariates (X), are proportional [29]. The \(\beta\) parameters estimate the multiplicative effect of a covariate \(x_{i}\) on the risk of the event occurring. The quantity \(e^{\beta}\) is the hazard ratio (HR), which can be defined as the hazard of one subject divided by the hazard of a different subject, distinguished by different covariates. \(e^{\beta_{i}}\) is the HR between the subject \(X^{i}\), with the \(i\)th covariate set to 1 and all others set to 0, and the reference subject \(X^{0}\), with all covariates set to 0:  
\[\frac{h_{X^{i}}(t)}{h_{X^{0}}(t)}=\frac{h_{0}(t)\cdot e^{\beta_{i}}}{h_{0}(t) e^{\beta_{1}\cdot 0+\beta_{1}\cdot 0+\cdots+\beta_{n}\cdot 0}}=e^{\beta_{i}} \tag{3}\]  
\(HR>1\) indicates a covariate that is positively associated with the event probability and negatively associated with the duration of the survival. For our analysis, \(HR>1\) for a commit feature shows that the presence of the feature reduces the resolution time for a bug. \(HR<1\) indicates the opposite. Alongside the statistical tests carried out earlier, we apply both the K-M estimator and the Cox PH model on the data to estimate the effects of attributes on the resolution time of SSTUBs in open source repositories.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 3 Methodology
### Discussion of the Proposed PFPS-LWC Method  
PFPS-LWC introduces several key innovations that enhance the personalization and robustness of federated learning. One of the most significant contributions is the Progressive Local Training Strategy combined with the local knowledge recall stage, which ensures that each client retains and utilizes its local knowledge across multiple rounds of global training. Unlike traditional methods, where local models are initialized using global feature extractors without consideration for previous local training, PFPS-LWC allows clients to recall previous local knowledge before integrating new global information. This effectively reduces catastrophic forgetting and enables a smoother transition between local and global training phases, significantly improving the model's ability to adapt to the unique characteristics of each client's data.  
Additionally, PFPS-LWC improves model generalization through the use of lightweight classifiers. By limiting the classifier complexity, the method reduces the risk of overfitting to local data, which is a common challenge in federated learning systems. This lightweight classifier not only enhances personalization but also mitigates the model complexity, helping to maintain a balance between personalization and generalization.
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 3 Preliminaries
### Stakeholders  
There are three main stakeholders in the proposed framework: the cloud users, the cloud resource holders, and the cloud service providers. First, the cloud users are located in the architecture's front-end zone (sending chain), where a built-in Cosmos account authenticates each cloud client to the blockchain network. Each user account holds a pair of keys (public and private) generated by the supported secp256k1 scheme. These keys serve to sign and verify transactions sent to the architecture's back-end zone (receiving chain). In the sending chain, users are identified by a unique ID, and they may use a lightweight node (which does not store a copy of the ledger) to send their transaction or a full node that stores an identical copy of the entire BC with all the transactions communicated to the receiving chain (in case of inter-organizational communication).  
The cloud resources holder is an entity (organization) that is authenticated also to the BC network by a pair of keys using a built-in Cosmos network account, benefiting from the full BC access control node that stores the whole ongoing access request and transaction logs. This cloud service consumer (CSC) defines rules and policies outlining how the cloud front-end zone users can access the offered services and resources, dictating the conditions, actions, and privileges they have. The access policy rules are created and maintained by the CSC and enforced by the deployed ABAC mechanism within the AC node.  
Figure 1: Architecture of the proposed framework.  
The third stakeholder is the cloud service provider, a business that offers on-demand scalable cloud computing resources. A CSP deploying our framework initiates the access control chain (the receiving chain) and provides the CSCs with authorization node capabilities to verify the access rights of the cloud users.
## 2 Simultaneous Segmentation and Detection Network (SSADNet)  
### Input Data Preprocessing  
#### 2.1.1 Point Cloud Data Representation  
A point cloud is a set of points obtained using a 3D scanner such as LiDAR. As shown in Figure 1, each point includes information on the 3D location coordinates and the reflection intensity of the object surface. The coordinates of an object consist of the x-coordinate, y-coordinate, and z-coordinate on the 3D LiDAR coordinate system. The reflection intensity of the light (laser) is determined by the material color and the distance from a reflector.
## 2 Development of Analytical Model
### Normal Force of Scratching  
For the available analysis of a scratching force, some major assumptions and simplifications are made in this study: (1) As a rigid body, the indenter does not deform during the process of scratching, and (2) the Berkovich indenter is a combination of a sphere and a triangular pyramid. The researchers often use the scratch hardness formula,  
\[H=\frac{F_{N}}{A} \tag{7}\]  
to calculate the normal force in the scratching process. The traditional method, without considering the elastic recovery in the scratching process, is shown in Figure 4a. The projected area, \(A_{t}\), can be expressed as  
\[A_{t}=\sqrt{3}\tan\theta(d+d^{*})^{2} \tag{8}\]  
where \(\theta\) is the angle between the edge plane and the centerline, \(d\) is the scratching depth, and \(d^{*}\) is the distance from the nose vertex to the top of the ideal indenter; \(d^{*}=R(1/\text{sin}\beta-1)\), where \(R\) is the indenter nose radius and \(\gamma\) is the angle between the edge line and the centerline [27].  
The traditional normal force, \(F_{Nt}\), can be obtained by combining Equations (7) and (8).  
\[F_{Nt}=\sqrt{3}H\tan\gamma(d+\frac{R}{\sin\beta}-R)^{2} \tag{9}\]  
A modified method considering the elastic recovery in the scratching process is shown in Figure 4b. The projected area \(A_{a}\) can be expressed as  
\[A_{a}=\sqrt{3}[(d_{e}+d*)\tan\gamma+(d+d*)\tan\theta](d+d*)\tan\theta \tag{10}\]  
where \(d_{e}\) is the part elastic recovery depth. There is a linear relationship between \(d\) and \(d_{e}\). According to [PERSON]'s study, the linear coefficient, \(\psi\), is within the range of 35%-40% [50].  
Figure 4: Diagram of the scratching process: (**a**) traditional analysis of normal force and (**b**) modified analysis of normal force.  
The modified normal force, \(F_{Nt}\), can be obtained by combining Equations (7) and (10).  
\[F_{Na}=\sqrt{3}H[(\psi d+\frac{R}{\sin\beta}-R)\tan\beta+(d+\frac{R}{\sin\beta}-R )\tan\theta](d+\frac{R}{\sin\beta}-R)\tan\theta \tag{11}\]