esa-sceva/satcom-chunk-collection huggingface dataset:
# Mining Sequential Patterns with VC-Dimension and Rademacher Complexity
## 2 Preliminaries
### Sequential Pattern Mining
#### 2.1.2 True Frequent Sequential Pattern Mining  
In several applications, the dataset \(\mathcal{D}\) is a sample of transactions independently drawn from an unknown probability distribution \(\pi\) on \(\mathbb{U}\). In such a scenario, the dataset \(\mathcal{D}\) is a finite bag of \(|\mathcal{D}|\)_independent identically distributed (i.i.d.)_ samples from \(\pi\). For any sequence \(p\in\mathbb{U}\), the _real support set_\(T(p)\) of \(p\) is the set of sequences in \(\mathbb{U}\) to which \(p\) belongs: \(T(p)=\{\tau\in\mathbb{U}:p\sqsubseteq\tau\}\). We define the _true frequency_\(t_{\pi}(p)\) of \(p\) w.r.t. \(\pi\) as the probability that a transaction sampled from \(\pi\) contains \(p\):  
\[t_{\pi}(p)=\sum_{\tau\in T(p)}\pi(\tau). \tag{6}\]  
In this scenario, the final goal of the data mining process on \(\mathcal{D}\) is to gain a better understanding of the process generating the data, that is, of the distribution \(\pi\), through the true frequencies \(t_{\pi}\), which are unknown and only approximately reflected in the dataset \(\mathcal{D}\). Therefore, we are interested in finding the sequential patterns with true frequency \(t_{\pi}\) at least \(\theta\) for some \(\theta\in(0,1]\). We call these sequential patterns the _true frequent sequential patterns_ (TFSPs) and denote their set as:  
\[TFSP(\pi,\theta)=\{(p,t_{\pi}(p)):p\in\mathbb{U},t_{\pi}(p)\geq\theta\}. \tag{7}\]  
Note that, given a finite number of random samples from \(\pi\) (e.g., the dataset \(\mathcal{D}\)), it is not possible to find the exact set \(TFSP(\pi,\theta)\), and one has to resort to approximations of \(TFSP(\pi,\theta)\). Analogously to the two approximations defined for the FSPs, now we define two approximations of the TFSPs, depending on the application we are interested in: the first one that does not have false negatives, while the second one that does not contain false positives.  
**Definition 3**.: _Given \(\mu\in(0,1)\), a \(\mu\)-approximation \(\mathcal{E}\) of \(TFSP(\pi,\theta)\) is defined as a set of pairs \((p,f_{p})\):_  
\[\mathcal{E}=\{(p,f_{p}):p\in\mathbb{U},f_{p}\in[0,1]\} \tag{8}\]  
_that has the following properties:_  
* \(\mathcal{E}\) _contains a pair_ \((p,f_{p})\) _for every_ \((p,t_{\pi}(p))\in TFSP(\pi,\theta)\)_;_
* \(\mathcal{E}\) _contains no pair_ \((p,f_{p})\) _such that_ \(t_{\pi}(p)<\theta-\mu\)_;_
* _for every_ \((p,f_{p})\in\mathcal{E}\)_, it holds_ \(|t_{\pi}(p)-f_{p}|\leq\mu/2\)_._  
**Definition 4**.: _Given \(\mu\in(0,1)\), a false positives free (FPF) \(\mu\)-approximation \(\mathcal{G}\) of \(TFSP(\pi,\theta)\) is defined as a set of pairs \((p,f_{p})\):_  
\[\mathcal{G}=\{(p,f_{p}):p\in\mathbb{U},f_{p}\in[0,1]\} \tag{9}\]  
_that has the following properties:_  
* \(\mathcal{G}\) _contains no pair_ \((p,f_{p})\) _such that_ \(t_{\pi}(p)<\theta\)_;_
* \(\mathcal{G}\) _contains all the pairs_ \((p,f_{p})\) _such that_ \(t_{\pi}(p)\geq\theta+\mu\)_;_
* _for every_ \((p,f_{p})\in\mathcal{G}\)_, it holds_ \(|t_{\pi}(p)-f_{p}|\leq\mu/2\)_._
## 2 Preliminaries
### Decentralized Identity (DID)  
Decentralized Identity (DID) constitutes an innovative modality of identity that resides wholly within the purview of the owner. This implies that all information pertaining to the identity is solely in the user's possession and no one can access this information without explicit permission.  
DIDs can be directly registered on the blockchain or a distributed network, thereby providing effective privacy and security protection for users. A typical DID system encompasses four entities: issuer, inspector-verifier, holder, and identifier registry.  
Issuers are entities tasked with managing and releasing user identity information. They substantiate a user's identity details by generating verifiable statements, and employ technologies like digital signatures to uphold the statement's authenticity and integrity. Inspector-verifiers represent an entity that accepts and verifies verifiable claims. They provide some type of service, such as verifying claims when requested by users. Holders are entities that request, receive, and hold verifiable claims from issuers. They can store issued claims in a verifiable claims wallet for future use. Identifier registries manage databases of decentralized identifiers (DIDs), enabling verifiers to authenticate users and claims.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
###### Abstract  
Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex and variable nature of tea cultivation landscapes. This study presents a high-precision approach to mapping tea plantations in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data and advanced deep learning models. We employed a combination of Sentinel-2 optical imagery, Sentinel-1 synthetic aperture radar imagery, and digital elevation models to capture the rich spatial, spectral, and temporal characteristics of tea plantations. Three deep learning models, namely U-Net, SE-UNet, and Swin-UNet, were constructed and trained for the semantic segmentation of tea plantations. Cross-validation and point-based accuracy assessment methods were used to evaluate the performance of the models. The results demonstrated that the Swin-UNet model, a transformer-based approach capturing long-range dependencies and global context for superior feature extraction, outperformed the others, achieving an overall accuracy of 0.993 and an F1-score of 0.977 when using multi-temporal Sentinel-2 data. The integration of Sentinel-1 data with optical data slightly improved the classification accuracy, particularly in areas affected by cloud cover, highlighting the complementary nature of Sentinel-1 imagery for all-weather monitoring. The study also analyzed the influence of terrain factors, such as elevation, slope, and aspect, on the accuracy of tea plantation mapping. It was found that tea plantations at higher altitudes or on north-facing slopes exhibited higher classification accuracy, and that accuracy improves with increasing slope, likely due to simpler land cover types and tea's preference for shade. The findings of this research not only provide valuable insights into the precision mapping of tea plantations but also contribute to the broader application of deep learning in remote sensing for agricultural monitoring.  
tea plantation mapping; multi-source remote sensing; deep learning; semantic segmentation; agricultural monitoring; DEM +
Footnote †: journal:  
Article
## 3 Preliminaries
### Data Description
Then, based on the EARR of the communication services with a QCI of 1 exported by the OMC, the drive test data are labeled as abnormal or normal, and a dataset is constructed for the detection of LTE-R cells with degraded communication performance. Figure 2 shows the construction process for this dataset. As can be seen from Figure 2, the drive test data for the selected performance indicators are sequence data distributed along the railway. In addition, due to the high reliability of the LTE-R network, the constructed dataset for the detection of LTE-R cells with degraded communication performance is a typical imbalanced sequence classification dataset.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 2 Network Slicing: Concept, System Architecture and Deployment
### Network Slice Concept
The definition of multiple slice subnets on a single domain allows this segment to provide differentiated behaviors, in terms of functionality and/or performance. The stitching of slice subnets across the RAN, CN and TN results in the definition of network slices. The rules for the definition of network slice subnets and their composition into network slices are detailed in the 5G Network Resource Model (NRM), specifically in the Network Slice NRM fragment [15]. This fragment captures the information model of 5G network slicing. As seen in Figure 1, this model specifies the relationships across the manageable entities, each represented as a separate Information Object Class (IOC). An IOC captures the semantics and attributes of a manageable entity; in other words, it defines the class based on which instances (objects) from this entity can be created. In the model, we have four different IOCs: (i) _NetworkSlice IOC_, representing a network slice; (ii) _NetworkSliceSubnet IOC_, associated with a network slice subnet; (iii) _ManagedFunction IOC_, which represents a 5G network function; and (iv) _EP_Transport IOC_, which represents an interface associated with transport level information, e.g., transport address, reachability information, and QoS profiles. Note that for NetworkSlice and NetworkSliceSubnet IOCs, two additional constructions are defined:  
* ServiceProfile: represents the requirements that the slice needs to support for a particular service. The 1:N relationship of this construction with the NetworkSlice IOC is because one network slice can host multiple services, as long as they do not impose conflicting requirements. These services can be from the same customer (the slice is dedicated for this customer) or different customers (the slice is used for serving multiple customers). Figure 1: 3 GPP Information Model of a network slice: the Network Slice NRM fragment. * SliceProfile: similar to the ServiceProfile, but applied to the slice subnet level. Though multiple associations can be found across these IOCs, the most typical case consists in having one slice consisting of two slice subnets: one including NG-RAN functions (RAN slice subnet) and the other 5 GC functions (CN slice subnet). Each network slice subnet can be deployed as an ETSI network service (via the _NetworkService_ class), provided that one of the network functions is realized as a Virtualized Network Function (VNF) [16]. Finally, the EP_Transport IOC features the TN slicing behavior across the RAN and CN slice subnets, by mapping the QoS requirements associated to the different interfaces (e.g., F1, N3, N6, etc.) into appropriate WAN resources.
# An Assessment of Smart Factories in Korea: An Exploratory Empirical Investigation
### Status of Smart Factories in Korea  
A smart factory in Korea is defined as a factory that produces customized products with minimal cost and time by integrating the entire process of planning, design, production, distribution, and sales into IoT, AI, and big data [5]. In Korea, various strategies are being pursued in response to Industry 4.0. In particular, the Korean government began to distribute and expand smart factories from 2014 to make the manufacturing industry smart. Recently, the government has been making continuous efforts to establish 30,000 smart factories.  
The smartness of manufacturing companies following the establishment of their smart factories is defined in five levels: level 1 (identified), level 2 (measured), level 3 (analyzed), level 4 (optimized), and level 5 (customized) [5]. According to the smartness level, manufacturing companies establish a smart factory to meet their process and system levels. In Korea, by the end of 2018, about 8000 manufacturing companies had established smart factories, but about 80% of them were established at a basic level--that is, at either level 1 or 2 [6].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Search Term (Group)**} & **Number of Publication in Which** \\  & **Search Term (Group) Occurred** \\ \hline Cyber-Physical Systems, CPS & 46 \\ Internet of Things, IoT & 36 \\ Smart Factory & 24 \\ Internet of Services, IoS & 19 \\ Smart Product & 10 \\ Machine-to-Machine, M2M & 8 \\ Big Data & 7 \\ Cloud & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Core technologies of Industry 4.0.  
Although smart factory projects are currently underway in Korea, methods for accurately diagnosing the levels of companies that want to establish smart factories are lacking. There is also a lack of empirical information on existing smart factories that could help guide the establishment of new smart factories. First, operating a supplier-centered diagnostic system is problematic despite the need to provide a checklist suitable for a manufacturing company that uses an actual smart factory. In the existing smart factory diagnosis model, the smart factory level is diagnosed by combining five areas (shop floor automation, factory operation, enterprise resource management, product development, supply chain management) and five levels (advanced level, intermediate level 2, intermediate level 1, basic level, no application of ICT). To supplement for this, it is necessary to consider the manufacturing company's process, manpower, and performance aspects, including the level of supply technology to make a smart factory. Second, given the advent of Industry 4.0, various new technologies such as artificial intelligence, IoT, CPS, and collaborative robots are being applied in manufacturing; thus, it is necessary to develop a diagnosis and evaluation system for these technologies.
## 3 Results and Analysis
### Dimensional Accuracy  
The percentage of the dimensional errors including depth error and width error of the fabricated pocket were calculated by using Equations (1) and (2), respectively.  
\[\text{Depth error (DE) \%=}\left|\frac{AD-DD}{DD}\right|\times 100 \tag{1}\]  
\[\text{Width error (WE) \%=}\left|\frac{AW-DW}{DW}\right|\times 100 \tag{2}\]  
where AD is the actual depth after machining, DD is the desired depth, AW is the actual width after machining and DW is the desired width. Table 6 listed the results of the depth and width errors of all machined pockets. It can be found from Table 5 that the maximum the depth and the width errors of the machined pockets are less than +5% under all milling strategies considered in this study. This shows that the RUM can be used for fabricating accurate pockets. The positive error percentage could be due to (i) the rounded edge due to the tool wear (see Section 3.4), (ii) variation in the diamond abrasive sizes, which make the actual tool diameter/length greater than the nominal diameter/length. Moreover, it can be seen from Table 6 that the average width tolerance (0.2604 mm) is greater than the average depth tolerance (0.01191 mm) for all the fabricated pockets. It could be due to (i) the machine tolerance increases with increasing the size of the machined part/feature and (ii) more numbers of active diamond abrasives around the tool side, which will reversely affect the width size compared with less diamond abrasives at the end cutting edge because of the internal hole (see SEM image of Figure 2b).  
Figure 14: (**a**) Typical example of surface roughness profile and (**b**) surface morphology of the pocket machined with Mix-2 strategy.
## 3 Results
### Repair Outcomes  
#### Structural Repair  
The quantity and quality of ChondroMimetic repair tissues was determined for 14 subjects (the MRI from the fifteenth subject was unusable due to movement; see Table 2). ChondroMimetic-treated osteochondral defects at 7.9 years post-treatment demonstrated a substantial degree of defect filling of 95.2 \(\pm\) 3.6%. Figure 1 shows the Total Defect Fill (%) of individual subjects and illustrates a low variability and consistently high degree of defect filling following ChondroMimetic treatment. In addition, subjects treated with ChondroMimetic had a mean T2 relaxation time of 52.5 \(\pm\) 4.8 ms for the cartilage repair tissue ROI, which was nearly equal to the T2 relaxation time of the ipsilateral native control cartilage regions (52.3 \(\pm\) 9.2 ms (\(p\) = 0.94)), and suggestive of a very similar tissue biochemical make-up and structure. The T2 standard deviations calculated for each repair tissue volume were comparable to native cartilage (17.3 \(\pm\) 3.3 vs. 13.1 \(\pm\) 5.3 ms), with a variance that was statistically equal to that of native cartilage (\(p\) = 0.11 by F-test).  
Using each scan series, full 3D reconstructions were generated of treated knees at the 10-day post-operative baseline, and at follow-up, to assist in visualizing the morphological impact of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Outcome** \\ \hline Total defect fill (\%) & 95.2 \(\pm\) 3.6 (89.8, 99.9) \\ T2 relaxation time (ms) & \\ Cartilage repair tissue & 52.5 \(\pm\) 4.8 (44.4, 58.5) \({}^{+}\) \\ Ipsilateral native cartilage & 52.3 \(\pm\) 9.2 (39.9, 78.5) \\ \hline \hline \end{tabular} Values are presented as means \(\pm\) standard deviation (min, max). T2 = transverse relaxation time; MRI = magnetic resonance imaging. \({}^{+}\)\(p\) = 0.94 by Student’s \(t\)-test compared to native cartilage.  
\end{table}
Table 2: Structural repair outcomes by 3 dimensional MRI at 7.9 years follow-up (n = 14).  
Figure 1: Total Defect Fill (%) by quantitative MRI for individual subjects at a mean followup of 7.9 years after ChondroMimetic treatment. Total Defect Fill (%) was determined using 3D quantitative magnetic resonance imaging by calculating the ratio of the new repair tissue (bone and cartilage) volume at followup, to original defect (bone and cartilage) volume acquired from 10 days post-operative scans (baseline). (n = 14).  
ChondroMimetic repair. Figure 2 illustrates a representative reconstruction conducted for a 29-year-old female subject who received 5 ChondroMimetic scaffolds in the trochlea, which are clearly visible filling the newly treated osteochondral defects in Figure 2A by 2D MRI, and are similarly visible in the 3D rendering (Figure 2B). At the follow-up after 8 years and 3 months, both the subchondral bone and cartilage aspects of the original defects appear as near-normal in both Figure 2C,D, compared with ipsilateral control regions in Figure 2E.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 2 Related Work  
Multiple studies have investigated the beam recovery problem in mmWave communications. The work in [4; 5] used hierarchical codebook-based procedures that restart the beam access search if a blockage effect is triggered. The authors in [6] presented a new beam aggregation method for fast beam recovery. The method utilized two beams to collectively add signal powers from the same direction. Moreover, an equal gain combining (EGC) scheme was presented in [7] that also combines multiple signals, which are received from the secondary and tertiary best directions, in order to overcome the signal losses caused by blockage at the main beam. Note that the aforementioned schemes are limited to low blockage parameters (obstacles of low density). Therefore, these schemes can yield unreliable links in dense scenarios.  
Additionally, authors in [8] computed the signal level at the neighboring beam to the blocked direction affiliated with the main beam. Here the BS and MS are compelled to perform beam scanning, which features an increased number of measurements at the neighboring directions. In turn, this results in prolonged recovery times and vulnerability to communication sessions-drop. Moreover, a relay node method was presented in [9] that performs a handover decision when the direct link at the main beam is not recovered within a threshold time period. However, this scheme works only if at least triangulation geometry is available in the MS proximity. The work in [10] proposed a reactive beam recovery method, in which the MS exploits the microwave band to identify a back-up direction, in order to recover links from blockage without requirements for handover procedures. Note that the latter scheme is dependent on sub-6 GHz microwave frequencies, which impedes the realization of SA mmWave networks.
## 1 Introduction
### Heterogeneous Networks  
The Internet of Things is about small devices (which includes eHealth devices) limited in terms of computing and networking capabilities. Short-range communication technologies, such as radio frequency identification (RFID), Bluetooth, or IEEE 802.15.4 standard are much more common than long-range communication technologies (3G, LTE or WiMax). Therefore, an additional functional element, the gateway (GW), translates between both short- and long-range communication technologies and helps to expand the boundaries of the current Internet. From an addressing perspective, these gateways are called address translation gateways [15], due to their dual addressing function (IP and IEEE 802.15.4 in 6 LoWPAN, for example).  
With regards to this topic, this paper focuses on the integration scenario between vehicular networking (as an enabling platform) and eHealth technologies (as end user applications). The eHealth devices supported by this platform, namely electrocardiograph (ECG), spirometer, oximeter and blood glucose meters, send health-related measurements over Bluetooth to an IPv6-ready phone application. The cluster head is attached to an IPv6 mobile router (the second part of the testbed) connected to the cellular infrastructure. The cluster head sends these measurements after user review to an application server in the IPv6 Internet. The gathered data is viewed remotely by the user's physician.  
This paper describes the integration of a machine type device (M2M) from the IoT world into an IPv6 vehicular network setting. The remainder of this paper is organized as follows. Section II presents related work in the field of eHealth and vehicular networks. Section III presents auto-configuration in IPv6 networks to meet M2M requirements. Section IV describes the overall system architecture and the functional elements of the adopted solution. Section V shows the protocol messages in the system,and Section VI gives the details of the prototype implementation along with the functional element specifications. The current state of integration and future work are given in Section VII to conclude the paper. Note that this paper is an extended version of [16].
# Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
## 3 Feeding Antenna  
In this section, the analysis and design of the feeding antenna for the proposed reflectarray are discussed. The proposed feeding antenna is a Yagi-Uda antenna, as shown in Figure 2a. It consists of a fed dipole antenna inserted between two parasitic elements: a director and a reflector element. The reflector and the director elements enhance the radiation in the direction of the aperture of the reflectarray. Typically, the feed element length \(L_{2}\) is usually around 0.45-0.49\(\lambda\), while the director length \(L_{3}\) is approximately 0.4\(\lambda\) to 0.45\(\lambda\). In addition, the reflector length \(L_{1}\) is slightly greater than the fed element. The separation between the elements \(d_{s}\) is found to be around 0.1 \(\lambda\). The radius of these wire elements (\(a\)) is set to around approximately 0.025 \(\lambda\). The proposed Yagi-Uda antenna is simulated using HFSS. Parametric studies are performed through EM simulation for setting the optimum values of the dimensional parameters of an \(X\)-band Yagi-Uda antenna at the operating frequency of 8.5 GHz. The required performance of the feeding antenna of the reflectarray in the present case comprises input matching below \(-\)10 dB and forward to backward radiation of more than 10 dB. The optimum values of the proposed Yagi-Uda antenna are \(L_{1}=20.9\) mm, \(L_{2}=16.5\) mm, \(L_{3}=10.8\) mm, \(d_{s}=3.9\) mm, and \(a=0.5\) mm.  
Figure 2b shows the simulated \(|S_{11}|\) for the feed antenna. It can be noted that \(|S_{11}|\) at the required operating frequency 8.5 GHz is less than \(-15\) dB, which represents a good  
Figure 2: Proposed Yagi–Uda feeding antenna. (**a**) Geometry, (**b**) Simulated Reflection coefficient, (**c**) Simulated total gain pattern.  
matching. The simulated realized gain pattern is shown in Figure 2c. The peak gain is obtained in the -ve z direction towards the aperture of the reflectarray. The peak gain is greater than 6 dBi and the backward radiation is less than \(-12\) dBi; thus, the front to back ratio is around 18 dB. Thus, the proposed Yagi-Uda antenna is suitable for the proposed requirements for the feeding antenna of the reflectarray.