esa-sceva/satcom-chunk-collection huggingface dataset:
# Plugging Small Models in Large Language Models for POI Recommendation in Smart Tourism
## 3 Preliminaries  
### Problem Definition  
This section introduces the definitions and preliminary concepts pertinent to the time-specific next POI recommendation problem. Specifically, we define \(\mathcal{U}=\{u_{1},u_{2},\ldots,u_{|\mathcal{U}|}\}\) as the set of users, \(L=\{l_{1},l_{2},\ldots,l_{|L|}\}\) as the set of locations, i.e., POIs, \(\mathcal{T}=\{t_{1},t_{2},\ldots,t_{|\mathcal{T}|}\}\) as the set of types of temporal relationships, \(\mathcal{C}=\{c_{1},c_{2},\ldots,c_{|\mathcal{C}|}\}\) as the set of categories, and \(\mathcal{A}=\{a_{1},a_{2},\ldots,a_{|\mathcal{A}|}\}\) as the set of regions obtained by clustering coordinates using the k-means method.  
**Definition 1** (Check-in Record).: _A check-in record is denoted by \(c=(u,l,c,a,t,d)\), where user \(u\) visits POI 1 during the time slot \(t\) on the date \(d\), and POI 1 belongs to the category \(c\), located in the region \(a\)._  
**Definition 2** (Trajectory).: _A trajectory is an ordered sequence of check-in records sorted chronologically by timestamp, represented as \(T_{u_{i}}=\{c_{1},c_{2},\ldots,c_{m}\}\). Here, \(c_{k}\) represents the k-th check-in on the trajectory, and \(c_{m}\) is the most recent check-in record for the user \(u_{i}\)._  
**Definition 3** (Temporal knowledge graph, TKG).: _A TKG, symbolized by \(\mathcal{G}=\{\mathcal{G}_{1},\mathcal{G}_{2},\ldots,\mathcal{G}_{d}\}\), is constructed from a set of factual quadruples arranged in ascending order according to their timestamps. A quad \((s,r,o,t)\in\mathcal{G}_{t}\) represents a fact with \(s\) (subject), \(r\) (relation), \(o\) (object), and \(t\) (timestamp). Each \(\mathcal{G}_{t}\) represents a TKG snapshot, and all events within a snapshot occur at the same time._  
**Definition 4** (Time-Specific Next POI Recommendation).: _Consider the historical trajectory \(T_{u_{i}}\) of the user \(u_{i}\), as well as a new query \(q=(u_{i},t,\gamma,d)\), with \(u_{i}\) belonging to the set of users \(\mathcal{U}\). The objective of the time-specific next POI recommendation task is to identify and recommend the top \(k\) POIs that are most likely to pique the interest of the user \(u_{i}\) at the specific time \(t\) on the date \(d\)._
# Future Wireless Networking Experiments Escaping Simulations
## 2 Theoretical Comparison of the Testbeds
### IoT Capabilities and Data to Be Collected  
While POWDER has only a few nodes providing wireless capabilities, IoT capabilities and data collection are enabled by dozens of static and mobile sensor units [19].  
Similarly, in the COSMOS testbed, IoT capabilities and data collection are enabled via different types of sensor units, varying from in-vehicle to infrastructure sensors such as street-level and bird's eye cameras. The data collected by the sensors can afterwards be aggregated at the edge cloud for processing and algorithm running.  
Beyond different experiments, testers can select in W-iLab many sensor nodes to collect parameters such as the temperature, humidity, and light ([[https://doc.ilabt.imec.be/ilabt/wilab/overview.html](https://doc.ilabt.imec.be/ilabt/wilab/overview.html)]([https://doc.ilabt.imec.be/ilabt/wilab/overview.html](https://doc.ilabt.imec.be/ilabt/wilab/overview.html))). LTE dongles and LTE Android smartphones complete available W-iLab2 hardware as LTE user equipment. Mobility is ensured by utilising robots, where dongles and smartphones can be deployed for mobility testing purposes. Using the nodes mentioned above, it is possible to collect LTE key performance data (e.g., data rate) since the testbed provides two (2) different fully operational LTE networks. The W-iLab2 has dedicated spectrum sensing devices for energy detection schemes and spectrum scanning. Finally, W-iLab.t testbeds offer the opportunity to configure different fixed and mobile experimental Wi-Fi scenarios and measure the network performance metrics such as throughput and Round-Trip Time (RTT).  
CityLab can be used to capture the noise and Received Signal Strength Indication (RSSI) measurements [21]. The noise levels are measured via sensors that additionally can collect measurements of air quality and movement. Additionally, the nodes in CityLab utilise specific sensors to obtain the most accurate noise, temperature, and humidity measurements.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 5 Simulation Results and Discussion
### Energy Consumption of Each Joint  
From the above discussion, we have observed that the total energy consumption per unit distance of TPM is less than that of TKV. However, we do not obtain any information on the energy consumption of each joint actuator. To this end, we illustrate energy consumption of each joint for two gait patterns with \(T_{c}\) = 1.0 s and \(L\) = 0.3 m, as shown in Figure 14. Energy consumptions of support ankle joint and support hip joint of TPM are basically the same as these of TKV. Energy consumptions of the support knee joint and swing hip joint of TPM are a little more than these of TKV. However, the energy consumption of the swing knee joint of TPM is much less than that of TKV. The energy consumption of the swing knee joint is mainly in DSP (see Figure 6). In DSP, the trailing leg (swing leg) pushes the ground to compensate for the energy loss of the previous swing foot landing. As a result, torso pitch motion can reduce the push force of the trailing leg during DSP. This view can also be seen in Figure 15, which shows the ground reaction on the trailing leg for TKV and TPM during DSP. It is observed that the vertical force of TPM is obviously less than that of TKV.
## 3 Key Concepts and Technologies  
### Centralised Identity Management Terminologies  
* **Single Sign-On (SSO):** SSO is a feature that allows users to log in once to a system and gain access to multiple trusted services, without the need to authenticate again. It enhances user experience by reducing the number of times the user needs to log in and validate their identity. The central authority manages the authentication of the user identity and issues a token that grants them access to different services [12].
* **Identity Provider (IdP):** The IdP is a central authority in a system that manages user identities, stores their credentials, and handles authentication processes. Its main function is to validate the user's identity and issue a token or assertion through which the user can access the application [12].  
* **Service Provider (SP):** An SP is an application or service that relies on an IdP to authenticate users. Once the identity is validated by the IdP, the SP grants access to its resources based on the user's permissions and roles [12].
* **Access Control:** This is a process to define and enforce rules and policies on what and how much access to specific resources in a system a user can obtain after authentication. It involves verifying the user's identity and determining what permissions or roles they have, allowing access to services and data based on those rules [12].
* **Authentication Server:** The Authentication Server is responsible for validating user credentials during the login process. It works with the IdP to verify credentials entered by the user and ensures that the user is who they claim to be before granting access to requested resources [13; 14].
* **Single Point of Failure:** It is a critical component in a system whose failure disrupts the whole system. It could be a centrally located server or database on which multiple users are dependent. This disruption could lead to complete unavailability of the system or service [14].
* **Role-Based Access Control (RBAC):** This method manages and regulates access for users based on their roles. Through this, user access can be limited by specifying what and how they can utilize the service. This enhances security and restricts unauthorised access [13; 15].
* **Trust Management:** In organizational setups where multiple domains or entities are involved, the process of trust management helps establish relationships among them. It involves exchanging security tokens, metadata, and public keys to ensure that users and SPs can verify each other's authenticity [12; 13; 14].
* **Multi-Factor Authentication (MFA):** MFA enhances security by requiring users to authenticate using more than one method, such as a combination of something they know (password), something they have (security token), or something they are (bio-metrics). It adds an additional layer of protection to centralised identity systems, helping prevent unauthorised access [12; 13; 15].
## 2 GNSS Measurement Residuals  
There are three measurement types used to quantify the line-of-sight (LOS) distance and velocity between the satellite and the user: code phase, carrier phase, and doppler measurements. Code measurements provide information about the absolute distance between a satellite and a user as described in (1). The carrier phase measurements, which are shown in (2), provide higher resolution distance information, but it is difficult to resolve the integer ambiguities that are inherent in the measurements, so they are often used as a relative metric unless the ambiguities are resolved [17]. Doppler measurements provide the LOS doppler frequency between the satellite and the user. They are similar to the rate of carrier phase measurements, but doppler is estimated by looking at the frequency of the signal with respect to the base frequency instead of computing the difference in the number of cycles between measurement epochs.  
\[\rho_{A}^{P}=R_{A}^{P}+c(\delta t_{A}-\delta t^{P})+T+I+M+\epsilon_{\rho_{A}}^{P} \tag{1}\]  
\[\phi_{A}^{P}=R_{A}^{P}+c(\delta t_{A}-\delta t^{P})+T-I+M+N_{A}^{P}\lambda+ \epsilon_{\phi_{A}}^{P} \tag{2}\]  
where subscript \(A\) denotes a receiver, and superscript \(P\) stands for a visible satellite. The pseudorange, carrier phase, geometric range, integer ambiguity in cycles, wavelength of the carrier, and speed of light are \(\rho\), \(\phi\), \(R\), \(N\), \(\lambda\), and \(c\), respectively. The common errors to be removed by the SBDD such as clock error and tropospheric and ionospheric errors are denoted as \(\delta t\), \(T\), and \(I\), respectively. The multipath and noise, which are residual errors to be monitored, are represented as \(M\) and \(\epsilon\), respectively.  
Although code measurements have been mainly used for PVT estimates and navigation of vehicles, carrier phase measurements are also necessary to achieve reliable sub-meter level accuracy navigation [18]. In order to estimate the expected accuracy of the PVT solutions, an accurate uncertainty model of the measurements is required [19]. Additionally, to ensure the integrity of the solutions, anomaly monitoring of the measurements is necessary. Although there exist multiple code measurement modeling and monitoring studies, work on the carrier phase monitoring methodologies is not as abundant [20]. In this paper, we review the modeling and monitoring methodologies for carrier phase measurements, which are accompanied by integer ambiguity resolution that had not been considered when applying the doppler or time-differenced carrier phase [21].
# Modeling Trust in IoT Systems for Drinking-Water Management
## 4 Results and Discussion
### Discussion
#### 4.2.3 ISO-Objective Approach  
The integration of ISO standards in the choice of trust factors gives our approach an objective character. Using criteria in line with ISO standards, we ensure an impartial evaluation of trust relationships, enhancing the credibility of our model [29].  
We elaborated our criteria based on ISO 9001 [31], ISO 27001 [32], ISO 14001 [33], ISO 24511 [34], ISO 24512 [35], ISO 24521 [36], ISO 24516 [37], ISO 24517 [38], ISO 24523 [39], and ISO 24526 [40] standards, which are recognized references for quality, information security, environmental management systems, and water-treatment machines. These standards provide guidelines and specific requirements to ensure the quality, security, sustainability, and performance of systems and equipment.  
Building on these standards, we identified relevant criteria to assess the quality of sensors, IoT devices, and drinking-water treatment machines. These criteria encompass aspects such as accuracy, reliability, information security, environmental impact, component performance, corrosion resistance, output pressure, and other essential characteristics.  
The objectivity of our approach stems from these criteria being aligned with internationally recognized standards, establishing a transparent evaluation framework in line with universally accepted quality standards. This approach instills confidence in the evaluation process, providing users with assurance regarding the robustness and reliability of the results generated by our model.  
It is important to note that our approach remains adaptable to changes in ISO standards and advances in specific fields. We are committed to keeping our methodology up-to-date by incorporating the latest standards and best practices, ensuring the ongoing relevance of our ISO-objective approach.  
Tables 5-9 provide a succinct summary of criteria aligned with ISO standards across different domains. For Quality (ISO 9001), criteria cover measurement precision, reliability, and adherence to specifications for sensors and IoT devices. In Information Security (ISO 27001), the focus is on data protection and secure communications. Environmental considerations (ISO 14001) encompass the impact of materials, eco-friendly design, and regulatory compliance. Water-treatment machine criteria (ISO 24511-26) include factors like energy efficiency and maintenance frequency. The Personnel category (ISO 9001, ISO 45001, ISO 14001, ISO 50001) outlines leadership commitment, resource management, and process control criteria. This concise format facilitates a clear understanding of standards-compliant assessment criteria in each area.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Situ Methods for R&D Purposes
#### 3.2.4 High Temperature Laser Scanning Confocal Microscopy (HT-LSCM)  
This method combines the classical LSCM with an infrared heated high temperature chamber. A focused laser beam scans the surface of the sample selectively. The intrinsic radiation is blocked out by a He-Ne laser, allowing the investigation to be carried out up to the fluid state. The thermal etching of the laser enables the observation of grain growth during a defined time-temperature profile. Thus, the effects of precipitates on grain growth behavior can be investigated as well. The main disadvantage of this method remains the restriction of the analysis to surfaces only. In addition, the oxygen content in the high-temperature chamber must be kept meticulously low, since the slightest oxidation leads to reduced observability [89].
# Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
## 2 Problem Formulation  
### Overview of Uncertain Data  
In decision-making, data types could be divided into three categories, such as labeled data, unlabeled data and semi-labeled data, as shown in Figure 1. According to these data types, a general decision-making problem, a classification problem, could be divided into three types: supervised classification, unsupervised classification and semi-classification [24]. In Figure 1a, the labeled data is assumed as \(\{\alpha\),\(y\}\,|\,x\!\in\!\mathbb{R}^{n}\), where \(\mathbf{x}\) is the input data and \(y\) is the label variable. Its classification rules are constructed by commonly used supervised classifiers, e.g., support vector machine (SVM), decision trees (DT), neural networks (NN) and so on [25; 26]. Due to taking the label variable \(y\) as a reference in training, these classifiers can achieve good performance on most of the data. However, misclassification usually happens on the boundary of two classes, as seen in Figure 1a. In Figure 1b, the unlabeled data contains only inputs, expressed as \(\{\mathbf{x}\,|\,x\!\in\!\mathbb{R}^{n}\}\). Due to the lack of transcendental categories information, its classification rules are constructed by unsupervised classifiers, namely clustering algorithms for determining classes. FCM clustering is a typical unsupervised classification algorithm used in the literature [27]. It is seen from Figure 1b that data points in the overlap of two clusters are difficult to classify. In Figure 1c, the given dataset is divided into two parts, the labeled part \(\{(\mathbf{x}_{1}\),\(y_{1})\), \((\mathbf{x}_{2}\),\(y_{2})\), \(\ldots\), \((\mathbf{x}_{r}\),\(y_{r})\}\) and the unlabeled part \(\{\mathbf{x}_{r+1},\mathbf{x}_{r+2},\ldots,\mathbf{x}_{N}\}\), where \(N\) and \(r\) are the number of data points in the whole dataset and labeled part, respectively. Usually, its classification rules are constructed through a two-phase process. Firstly, a classifier is trained by the labeled data. Then, the unlabeled data points are labeled based on the constructed classifier. It is seen that there are two uncertain regions. One is the boundary of two classes in the labeled part, the other one is the overlap in the unlabeled part.  
Through the discussion of Figure 1, it is seen that, with respect to no matter which type of classification, there exist uncertain data points affecting the accuracy of classification rules, e.g., in misclassification regions or overlap regions. To improve the efficiency and effectiveness of classification, this is a good way to extract these data points as an uncertain region for manual recognition and \"clerical review\". Then, with the description of uncertain regions, we can construct better classification rules for high-performance classification.
## 3 Design Rationale and System Architecture  
Aiming at a self-adaptive and secure-by-design service architecture for assurance- and resilience-oriented 3D point cloud applications, SAUSA leverages SDN to achieve efficient resource coordination and network configuration in point cloud data processing and delivery. By combining Blockchain and distributed data storage (DDS) to build a decentralized authentication network, SAUSA is promising to guarantee the security and privacy of data access, usage, and storage in 3D point cloud applications.  
Figure 1 demonstrates the SAUSA architecture, which consists of two sub-frameworks: (i) a hierarchical SDN-enabled point cloud service network; (ii) a decentralized security fabric based on Blockchain and DDS.
# Evaluation of Chemical Compounds in Local Garlic Genotypes from Southwestern Romania
###### Abstract  
Garlic (_Allium sativum_ L) is one of the most esteemed plants due to its medicinal properties. Its health benefits for humans are attributed to its chemical compounds. Few studies characterize garlic genotypes cultivated in Romania concerning their chemical composition. In this context, this study aimed to determine the chemical compounds for 16 local garlic genotypes cultivated under the same climatic and technological conditions. The chemical characteristics studied were the total phenolic content (TPC), total flavonoid content (TFC), reducing sugar content, antioxidant activity (AO), vitamin C, and acidity level. Chemical composition varied significantly among genotypes: TPC ranged from 656.07 to 1317.32 ug GAE/g FW; TPC ranged from 427.08 to 1447.90 ug QE/g FW; vitamin C ranged from 3.24 to 5.37 mg/g FW; reducing sugar content ranged from 3.24 to 5.37 mg/g FW; and the acidity level for the control was 6 meq/100 g. Among the selected genotypes, differences were observed between 3.48 and 11.02 meq/100 g. Significant correlations were noted between different compounds, specifically between TPC and AO, as well as between TPC and acidity level. In conclusion, this study highlights significant variability in terms of chemical composition of local garlic genotypes, which indicates notable differences between them and suggests that the genotypes may have different potential in medicinal and nutritional uses due to their distinctive chemical compositions.  
Allium sativum; biochemical diversity; bulb compounds; local genotypes +
Footnote †: journal: Medical  
effects and its low toxicity, garlic represents a promising ingredient in the development of functional foods aimed at treating or preventing various conditions [13]. According to [PERSON] et al. and [PERSON] et al. [14; 15], identifying and highlighting the chemical properties of local garlic genotypes, compared to commercial varieties, could represent an advantage for consumer preferences. [PERSON] et al. [6] appreciate that these studies could enable breeders to develop stable varieties with higher or lower levels of biochemical compounds, depending on nutritional and/or technological needs. In this context, this study aimed to evaluate the chemical compounds in some local garlic genotypes from southwestern Romania. The evaluation of chemical compounds in local garlic genotypes is a relatively new field of research, with multiple innovative and relevant considerations for the scientific community and for practice, such as the identification of genotypes that have not been previously analyzed or documented, or the identification of the specific effects of the growing conditions in the environment of southwestern Romania on chemical composition. Investigating the specific biological activities of local genotypes can highlight local garlic as a valuable resource for pharmaceuticals and dietary supplements. Initiatives made to identify, characterize, and conserve local genotypes are essential for preserving biodiversity and for securing genetic resources for the future.