esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybernetic Model Design for the Qualification of Pharmaceutical Facilities
## 3 Discussion and Model Verification, Validation, and Application
### Uts, DQ, TRM, and CCS  
Guidelines [4; 17] provide very precise and concise definitions for URS and DQ and represent one of the most important links for the success of the project. The difficulty of implementing this, especially without a project manager, can be seen from the results of the survey (Scheme 4) and the case study. From the first 88 projects, it was shown through the case study that QA was not involved from the beginning to the end of the project. In the subsequent 11 projects, this problem was solved through the application of the cybernetic model. This was carried out by organizing regular meetings initiated by the project manager twice a week. One meeting was with the contractors and the other was internal, with all involved sectors (Engineering, Production, QA, QC, Procurement, and Qualifications). This resulted in the successful completion of the project, with an average of 27% less time for the execution of the qualification activity, compared to previous projects. Also, the quality significantly increased and each phase was documented, which resulted in a positive report with no complaints or recommendations from the regulatory authorities that inspected the reconstructions in question.  
After the tests are completed, a traceability matrix (TRM) is created, which summarizes the qualification/validation activities and documents the fulfillment of the requirements from the URS and risk analysis throughout the qualification/validation cycle. A traceability matrix is a method used to establish the relationship between documents. Its purpose is to ensure that the requirements are not only followed by appropriate design elements but also that they are met through testing or other means of verification. The greatest amount of noncompliance during the performance of qualification activities occurs due to noncompliance with the URS or due to its nonexistence. If the DQ is not aligned with the URS, it is necessary to create a risk analysis that will justify or not justify the stated deviations.  
In the revised edition of Annex-1 [39], the greatest importance is given to contamination control strategy (CCS), although it does not belong to the qualification itself. PDA [40] defines CCS as a holistic approach to contamination control, which takes into account all possible risks and all contamination control measures that must be adapted to specific risks for each process individually. Based on experience, practice, and all the results presented, we believe that, before entering the production cycle, it is necessary to define a contamination strategy in order to reduce all risks to an acceptable level. Additionally, CCS can be defined as a comprehensive framework that brings together the basic components, and one of the most important components is certainly a perfectly qualified facility. The qualification of the plant using the model defined in this way makes the creation and management of CCS much easier.
## 1 Introduction
The bands chosen are the mmWave bands n257, n258, and n261 for 5G-advanced and beyond. The structure of this article is as follows: Section 2 provides a detailed analysis of the dispersion characteristics of the SIW and SSPP unit structures and their design methods. Section 3 presents the filters' operational principles, design procedure, and architecture: comb-shaped tapered SSPPs enable a wideband SIW-SSPP structure; Archimedean spirals enhance the single wideband filter's performance; dual-symmetry CSRRs achieve the multi-sub-band filter's multiband response. This section systematically examines the structural design and performance characteristics of the filters. Section 4 presents the experimental validation of the proposed filter prototypes and discusses the measurement results, as well as their comparison with reported works. Section 5 concludes with the methodological framework for SIW-SSPP multiband filter design and a comparative performance evaluation.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 4 System Model
### Assumptions  
In the proposed coexistence solutions, we consider coordinated eNBs which exchange coexistence decision made by a central coordinator eNB. In terms of simulation, the parameter parsing is done by functions and receiving the updated configurations from the central node takes negligible latency and overhead. However, for real-world deployments, this communication between nodes can be accomplished by combining the X2 interface and the X2 AP services [13]. The X2 is the designation of the interface that connects one eNB to another as defined by 3 GPP in [34]. Similarly, eNBs in different operators can exchange the information through corresponding Mobility Management Entity (MME) via S10 interface [35].  
In practical applications, the LTE and WiFi frames are classified in _Technology recognition model_ based on technology recognition solution as proposed in our work in [26]. In the context of this article, we use the ns-3 simulator to model the LTE and WiFi networks and the technology recognition step is emulated by generating log files that represent statistics of LTE and WiFi frames.  
To minimize the computational complexity, a coexistence scheme has to report the optimal ON-OFF duty-cycle ratio every prediction duration T seconds, where T is an optimally selected network traffic prediction period based on the traffic dynamics of the co-located networks. In practice, the traffic dynamics of the co-located networks have to be analyzed and the value of prediction duration T has to be set low enough to match the traffic dynamics of the WiFi network [36]. However, the selection of optimal time T is beyond the scope of this study.
## 3 Results and Discussion
### Experimental Results and Analysis
#### 3.2.2 Model Analysis of the Experimental Results  
As mentioned previously, according to the experimental results reported by [PERSON] et al. [7], the \(\beta\) values of non-plastic sandy soils were 2 and 0.45. Figure 14 presents the PWP data of Sand-01, Sand-02, and Sand-03. The results in Figure 14 were compared with those obtained using the model developed by [PERSON] et al. [42]. The PWP data of the Liugui specimens were consistent with the results produced by the model. Almost all the PWP values were within the model range. Accordingly, when the PWPs of nonplastic sandy soil specimens are examined, the \(\beta\) value in the model developed by [PERSON] et al. [42] should be 0.45-2, which corresponds to the results in Figure 7.  
In Figure 15a, the PWPs of specimens with plastic FCs of 15% are compared with those obtained using the model developed by [PERSON] et al. [42]. The PWP excitation speed of the specimens with plastic FCs of 15%, particularly those containing fines with low PI values, was slower in the initial stage. Most of the PWP values in the initial stages were not within the designated range of the model; only those in the later stage fell within the designated range. A possible reason for this is that the plastic fines only accounted for 15% of the specimens, and the fines had lower PI values. Therefore, the model might be less applicable to soils with low PI values or FCs. A comparison between the PWPs of the specimens with FCs of 30% and the range of the model are presented in Figure 15b. The normalized results of the nine sets of data indicate that different types of fines exert different effects on the WP excitation trends of plastic soils. The curves of the specimens with FCs of 30% increased faster than those of the specimens with FCs of 15%. Most of the PWP curves increased sharply in the initial stage; only the WP values of a few specimens were not within the range of the model. The PWP curves were generally similar to the curve produced by the  
Figure 13: PWP ratios and normalized N/N\({}_{\mathrm{L}}\) values are compared for different fine contents. (a) Clean sands. (b) FC = 15%. (c) FC = 30%. (d) Effect of PI on the curve.  
model. Therefore, the results of the present study suggest that when plastic soils with high FCs are inspected, the \(\beta\) parameter in the model developed by [PERSON] et al. [42] should be set at 20 to 250, which is consistent with the results in Figure 8.
## 3 Black-Box Penetration Testing Framework for ICVs
### Penetration Testing Case Set in ICVTest
\(\quad\)  
System Security Test Case Set  
A secure ICV operating system necessitates stringent control over external entities' access to resources within the system. Ensuring the security of the operating system entails not only safeguarding data through mechanisms like authority access control, encryption, and integrity verification during design but also mitigating cybersecurity risks arising from design and implementation defects via a series of configurations. 5. \(\quad\)  
Radio Security Test Case Set  
ICVs are equipped with various wireless communication modules that operate on different radio frequencies to cater to diverse communication needs in scenarios such as Wi-Fi, Bluetooth, near field communication (NFC), RFID, cellular networks, and dedicated short-range communications (DSRC). However, the inherent openness of the wireless communication channel introduces vulnerabilities for ICVs. If the traffic transmitted over the radio channel is not encrypted, malicious attackers can intercept and manipulate user secrets by collecting and analyzing radio signals. 6. \(\quad\)  
Network Security Test Case Set  
In contrast to radio security, network security primarily focuses on ensuring the security of the TCP/IP protocol stack at the network level, while the former concentrates on physical layer and link layer security. Malicious attackers have the potential to intercept sensitive data transmitted over a network, and they can also manipulate legitimate data obtained through replay attacks in order to deceive recipients. Furthermore, attackers may even exploit communication between entities to carry out man-in-the-middle attacks. Once successfully hijacked, these attackers can eavesdrop and tamper with communication information exchanged among authorized users. Web Security Test Case Set  
The interaction between ICVs and the cloud platform may introduce cybersecurity risks to the ICVs. In the event of a compromise of the cloud platform by attackers, not only can private data such as user information be leaked, but also the ICVs can be invaded through remote wireless networks. Web applications, being crucial components of the cloud platform, face threats from both clients and servers. Client-side threats primarily encompass browser vulnerabilities, cross-site scripting (XSS) attacks, cross-site request forgery (CSRF) attacks, clickjacking exploits, and hypertext markup language (HTML) vulnerabilities. On the other hand, server-side threats mainly involve injection attacks, file upload vulnerabilities, authentication and session management weaknesses, access control issues, web framework susceptibilities, distributed denial-of-service (DDoS) attacks, and improper server configurations.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Artificial Intelligence-Based Fault Diagnosis Methods
#### 5.2.1 Statistical Analysis Methods  
The statistical analysis method primarily involves a sensor's randomly distributed data collection. The sensor is deemed defective when the probability of the data instance generated by the model is extremely low. The primary components of this method are time series analysis and multivariate statistical analysis.  
The time series analysis method examines the characteristics of sensor data from a time series perspective, estimates the predicted value of the sensor data, and determines whether the sensor is faulty by comparing the difference between the predicted value and the actual value. [PERSON] et al. [92] designed a module based on time series theory to detect sensor faults in the navigation system of autonomous mobile agricultural robots. Simple and effective, the time series method was able to explain the relevant results intuitively. However, agricultural sensors are typically deployed outside, where the environmentconstantly changes, causing the sensor data to fluctuate greatly and be prone to error. Due to the difficulty of a single sensor explaining the complexity of agricultural environments, multivariate statistical analysis methods that combine data from multiple sensors for fault diagnosis are crucial. Nonnegative Matrix Factorization (NMF) [14], Partial Least Squares Regression (PLS) [93], and PCA [94] are the three multivariate statistical analysis methods.  
After establishing the probability statistics model, the model can effectively detect sensor system faults. The model is highly interpretable; however, when using time correlation to detect faulty data, a sudden change in the data distribution will reduce the time correlation and make sensor fault diagnosis more difficult. The statistical analysis method has the following drawbacks: the nonparametric statistical model struggles to meet real-time requirements; the parametric statistical model is ineffective in practical applications due to a lack of data distribution knowledge in the sensor system; the histogram does not take into account the relationship between multivariate data and only applies to univariate data.
# Learning Models for Bone Marrow Edema Detection in Magnetic Resonance Imaging
## 2 Materials and Methods
### Dataset and Pre-Processing  
The dataset collected at the University Hospital Center of Sao Joao (UHCSJ) contains T1 and fluid-sensitive sequences of 36 non-edema and 28 edema patients. This dataset contains MRI examinations of the coronal, axial, and sagittal planes of the knee. The MRI was analyzed in the different planes, and annotations were performed in the coronal plane. Clinical annotations of the MRI scans were conducted by one radiologist and one radiology resident, and all of the annotations were double-checked with a concordance review. All sequences were annotated, namely with the shape of the bones (femur and tibia), and, in patients with BME, the areas that corresponded to this finding were also annotated. One slice of each sequence was also annotated with a circle in the quadriceps muscle for a possible normalization process. Examples of MRI slices from edema and non-edema patients and the correspondent annotations are present in Figure 1.  
Figure 1: Examples of fluid-sensitive sequences for patients with and without BME. The first and second rows represent examples of MRI of BME patients with the bone segmentations in the first row and the segmentation of the BME regions in the second row; the third row contains the bone annotations from three different non-BME patients, as well as a rounded annotation in the quadriceps muscle.  
In total, the dataset of edema patients with bone segmented contains 313 slices (approximately 11 slices per patient with bone segmented), 228 slices positive with BME regions, and 85 slices without BME regions. For the non-edema patients, the radiologists annotated the bone region in only one slice, which originated only 36 non-edema slices. Since there are only 36 bone segmentations from non-edema patients in the dataset, the segmented slices from edema patients with BME regions were labeled as edema, while the ones without BME regions were labeled as non-edema. This process was inspired by the work of [PERSON] et al. (2014), in which slices of edema patients that did not contain BME regions appeared to show no differences when compared to slices of non-edema patients. Table 1 summarizes the number of slices and patients on the dataset.  
The mean age of all patients is 14.35 \(\pm\) 2.81 years old in a range between 4 and 17 years old. Among the studied classes, the mean age of edema patients is very similar to non-edema ones (14.68 \(\pm\) 1.93 and 14.14 \(\pm\) 3.36, respectively).  
The pre-processing of the data consisted of two steps: segmentation and normalization. The annotations from the clinicians, such as the ones represented in Figure 1, were used to segment the bone region. Each image was then normalized, dividing each pixel intensity by the corresponding maximum intensity of its image.
## 2 Methodology  
### Problem Definition  
In this subsection, the traffic flow forecasting problem will be introduced briefly.  
The underlying traffic network can be represented by a directed graph \(G(V,E,A)\), where \(V\) is the vertex set composed of traffic monitors with \(|V|=N\); \(E\) is the edge set composed of road segments connected by traffic monitors; \(A\in\mathbb{R}^{|E|\times|E|}\) is the adjacency matrix of traffic network, where \(|E|\) denotes the cardinality of edge set of \(E\). In detail, \(A_{ij}=1/d_{ij}\) represents the weight between nodes \(v_{i}\) to \(v_{j}\) along the driving direction with the road section length \(d_{ij}\).  
The spatio-temporal variables matrix \([G_{t-n},\ldots,G_{t-1},G_{t}]\) is employed to denotes the traffic flow sequences distributed over the traffic network, where \(n\) refers to the look-back time windows; \(G_{t}=\left[v_{t}^{1},\ldots,v_{t}^{|E|-1},v_{t}^{|E|}\right]\). Thus, the traffic flow forecasting problem is formulated as a multivariate time series problem to estimate the traffic flow at a future forecasting interval \(T\) based on the historical traffic data collected over previous forecasting intervals from the overall traffic network with \(N\) traffic monitors.  
While defining the traffic network topology structure \(G(V,E)\) and relevant previously monitor data until \(t\), the traffic flow forecasting problem is tackled to to forecast the traffic  
Figure 1: Spatial-temporal correlation of traffic flow.  
flow \([Y_{t+1},\ldots,Y_{t+T}]\) under forecasting interval \(T\). Its definition is shown in Figure 2, and its formula is as follows:  
\[[Y_{t+1},\ldots,Y_{t+T}]=f(X_{t-n},\ldots,X_{t-1},X_{t}). \tag{1}\]  
The direct forecasting method [29; 30] used in this paper is shown in Figure 3. The historical value \((X_{t-n},X_{t-n+1},\cdots,X_{t-1},X_{t})\) is input into the forecasting model to forecast future traffic at once of T-fold resolution, i.e., \((Y_{t+1},\cdots,Y_{t+T-1},Y_{t+T})\).  
**Remark 1**.: _The arbitrary forecasting interval \(T\) is predefined before training the forecasting model. Meanwhile, the sampling period in datasets could be the same as the predefined forecasting interval. It is assumed that the original sampling period could be small enough to obtain the required datasets._
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 4 Main Design of RICC
### Preliminary Knowledge  
Some relevant technologies and models for the proposed scheme should be introduced.
## 5 Case Study: Applying CVMan to Analyze Clones in Ubuntu
### Answering PQ1  
Taking Ubuntu 18.04 as an example, in order to answer PQ1, the interface feeds the programs in Ubuntu 18.04 to the clone management component, which detects clones and constructs clone landscape, represented by a SCG. The clone management component would report back the metrics that are measured from the SCG, including the clone surface of each program and the clone prevalence of each cloned fragment in Ubuntu 18.04. The SCG of Ubuntu 18.04 has 337,837 nodes (i.e., fragments) and 4,761,650 edges.
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
## 5 Conclusions  
A novel chipless RFID sensor for monitoring the angular position of a rotating object has been presented in this paper. The rotation state is encoded in the cross polar response of a tag, based on a truncated periodic surface, composed of a set of dipoles. The adopted cross polarization response allows the achievement of a reliable response even if it the chipless RFID sensor is applied on metallic objects or in an environment with strong multipath. The proposed passive sensor needs only a small set of reading frequencies that depend on the number of employed dipoles. A manufactured prototype has been experimentally characterized and a good agreement between simulations and measurements has been found.  
**Author Contributions:** Conceptualization: S.G., F.C. and M.B.; Methodology, S.G., F.C., M.B., F.A.D. and G.M.; Software, S.G., F.C. and M.B.; Validation, S.G., F.C., M.B., F.A.D.; Writing-Original Draft Preparation, S.G.; Writing-Review & Editing, S.G., F.C., M.B., F.A.D. and G.M.; Funding Acquisition, G.M., S.G. and F.C.  
**Funding:** This research was partially funded by H2020 Programme, Call MSCA-RISE-2014, Project EMERGENT, GA n. 645771.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] at the Eng. Electronica, Electrica i Automatica Universitat Rovira i Virgili, Spain as well as [PERSON] at the Laboratoire de Conception et d'Integration des Systemes (LCIS), Grenoble-INF, Universite Grenoble-Alpes, France for their useful technical discussions and comments. The authors are also grateful to the Additive Manufacturing CrossLab ([[https://crosslab.dii.unipi.it/additive-manufacturing-lab](https://crosslab.dii.unipi.it/additive-manufacturing-lab)]([https://crosslab.dii.unipi.it/additive-manufacturing-lab](https://crosslab.dii.unipi.it/additive-manufacturing-lab))) for partially supporting this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Chart mapping the distance for the four-dipole structure obtained by measured data.