esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Surprise Adequacy Tools for Corner Case Data Description and Detection
## 2 Surprise Adequacy  
To evaluate the quality of testing data in AI assurance, a good way is to study neurons behavior in terms of a given deep learning (DL) model. Generally, the more the diversity of neuron behaviors, the better the quality of testing data. There are various methods to describe neuron behaviors in DL models. For example, neuron coverage was proposed in [20], and its activation status was taken as the behavior of a testing data point responding to the DL model. Similarly, values and signs of neuron outputs can both be used to describe neurons' activation behaviors in [27].  
While, compared with those metrics reflecting independent behaviors of testing and training sets, in [21] an interesting idea was proposed to describe the difference between the testing set's behaviors and that of the whole training set. To realize this idea, the activation status of neurons in DL models is first denoted. Assuming a set of inputs \(X=\{x_{1},x_{2},\dots\}\) and a trained DL model M consisted of a set of neurons \(\mathbf{N}=\{n_{1},n_{2},\dots\}\). For a given testing data \(x\in X\) and an ordered (sub)set of neurons \(N\subseteq\mathbf{N}\), the activation behavior (namely activation trace) of \(x\) on \(N\) is expressed by the vector of activation values and it is denoted as  
\[\alpha_{N}(x)=[a_{1}(x),a_{2}(x),\dots,a_{|N|}(x)]^{T} \tag{1}\]where each element \(a_{n}(x)\) corresponds to the activation value of \(x\) with respect to an individual neuron \(n\) in \(N\). Hence, the set of activation traces for \(X\) is denoted as \(A_{N}(X)=\{\alpha_{N}(x)\mid x\in X\}\).  
Then, \(A_{N}(\mathit{Tr})\) is calculated based on the training dataset \(\mathit{Tr}\), which records neurons' activation behaviors on all samples in \(\mathit{Tr}\). Subsequently, the activation behavior of testing data \(\mathit{Te}\) is also obtained as \(A_{N}(\mathit{Te})\). Finally, combining \(A_{N}(\mathit{Tr})\) and \(A_{N}(\mathit{Te})\), surprise adequacy (SA) was defined to describe the relative novelty of testing inputs with respect to the training data. It is actually denoted as the quantitative similarity measure between \(A_{N}(\mathit{Tr})\) and \(A_{N}(\mathit{Te})\) in the following form.  
\[\text{SA}=\mathit{SimilarityMeasure}(A_{N}(\mathit{Te}),A_{N}(\mathit{Tr})) \tag{2}\]  
In [21], there are two kinds of similarity measurement proposed to formalize \(SA\). One is the likelihood-based SA (LSA), and the other one is distance-based SA (DSA). These two SAs are validated feasible to capture the relative surprise of testing data in DL systems and describes testing data's behaviors, which implies SAs are useful to evaluate the quality of testing data.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
###### Abstract  
IEEE 802.11 ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11 ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11 ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.  
IEEE 802.11 ah; dense IoT networks; restricted access window (RAW); real-time RAW optimization; dynamic traffic +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Instrumentation and Measurement of Inputs and Response Parameters
#### 2.2.3 Draft Force  
Cylindrical load cells (Novatech Measurements Limited, East Sussex, UK; F214) were used for measuring the draft force encountered by the rotary tool (R\({}_{\text{VA}}\)) during operation. The rated output of load cells was 1.2 mV/V, the bridge resistance was 350 \(\Omega\) and the excitation voltage was 10 V. A couple of these load cells were mounted on a single-point dynamometer one end of which was connected to the tool carrier and the other end to the soil processing trolley. These load cells were also calibrated to identify the sensitivity and linearity in force measurements at different loads. The draft and drawbar power requirements of the developed prototype were calculated using Equations (4) and (5):  
\[D=H_{1}+H_{2}, \tag{4}\]  
\[DP=D\times v, \tag{5}\]  
where \(D\) is the draft force (N); \(H_{1}\) and \(H_{2}\) are the horizontal components of pull forces (N) measured by load cells 1 and 2, \(DP\) is the drawbar power (kW); \(v\) is the actual forward speed of operation (ms\({}^{-1}\)).
# Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide
## 6 Conclusions  
The SHM system illustrated in this paper represents a low-impact solution for monitoring the structural status of a road tunnel damaged by a large landslide. Moreover, it was deployed on an existing structure without significant works for implementation. The monitoring network provides real-time measurements every half hour and does not interfere with traffic; in three years the tunnel was closed only on the day of installation.  
The collected data allow us to delineate the interaction between the tunnel and the landslide and permit us to consider future scenarios for the tunnel durability and serviceability. Measuring crack widening increases the overall resilience of the structure by indicating the areas where maintenance work should focus. Moreover, the determination of a causal relationship between the tunnel lining displacements and possible triggering factors is essential to design effective countermeasures.  
Water from rainfall and snowmelt percolates easily within the slope through the highly fractured dolomitic rock and the very coarse dolomitic deposit near the crown. The effect is the accumulation of water at the contact between the low permeability clay-shales and the dolomite. The change in the water pressure regime and the softening of the clay-shales along the slip surface trigger displacements in the block slide. It is therefore advisable to extend the drainage tunnel until it reaches the dolomitic domain to properly drain the system and reduce landslide displacement.  
Figure 13: Vector representation of the rotation of the clinometers in the tunnel; bottom left: the rotation recorded by clinometer C04 associated with the displacements recorded by crackmeter F15.  
The independent segments design has proven to be able to sustain small landslide-induced displacements. However, if the slip surface cuts through one segment, some damage may eventually arise. In this case, a SHM could be implemented to support tunnel management by providing data about the rate of expansion of cracks, the convergence of the lining, and responses to triggering factors in real time. Finally, a web-based platform could support data analysis and a prompt response in the case of paroxissistic phenomena.  
We thank ANAS Compartimento di Tieste and in particular [PERSON]. ANAS Compartimento di Trieste funded the deployment of the monitoring system. Other resources already available at CNR-IRPI provided the funds for the research activity. ANAS Compartimento di Trieste was not directly involved in the manuscript design and production.  
[PERSON] conceived the monitoring system and supervised its installation with the contribution of [PERSON]; [PERSON] and [PERSON] analysed data; [PERSON] designed the web-based platform; [PERSON] wrote the paper, [PERSON] and [PERSON] revised it.  
The authors declare no conflict of interest.
## 1 Introduction
Therefore, this paper uses BiGRU to extract the uncertainty relationship between wind power and energy storage action and realizes the prediction classification of energy storage actions. The attention mechanism in neural networks can focus on crucial information, reduce attention to other information, and even filter out irrelevant information, thus solving the problem of information overload and improving the efficiency and accuracy of task processing [24; 25]. It is mainly used to guide the model focusing on essential features in the power system to improve its performance [26]. If the temporal pattern attention mechanism (TPA), which is more sensitive to temporal features [27], is used in wind power prediction, it can improve the model prediction accuracy [28]. Therefore, this paper uses TPA to extract important temporal information in the BiGRU hidden layer to improve model performance. Motivated by it, this paper proposes a joint control strategy for the wind storage system based on TPA-BiGRU. The contributions are summarized as follows. 1. The wind storage system is controlled by adopting the advanced rolling optimization control strategy (AROCS) and PI control strategy, and a dataset with excellent wind storage control characteristics is constructed. 2. An evaluation standard for the output deviation of wind power active power is proposed. This standard takes the assessment requirements of China Southern PowerGrid for wind power integration as an example, which can be consistent with the grid-connected requirements of conventional units. 3. A wind storage joint control strategy based on the TPA-BiGRU algorithm is proposed, which solves the problem of non-convergence of mathematical modeling methods. It can obtain the storage control quantity in real-time and dynamically, which improves the stability and accuracy of the wind storage system under the premise of ensuring economic benefits. The rest of this paper is organized as follows: Section 2 describes the structure of the proposed model and the evaluation criteria. Section 3 presents the basic theory and design of the model. Section 4 shows the model's training process. In Sections 5 and 6, the effectiveness of the control strategy proposed in this paper is verified by experimental comparison and conclusions are drawn.
## 2 Problem Formulation
### Robot Model  
Given a robot team \(R=\{R_{1},R_{2},\ldots,R_{M}\}\) consisting of \(M\) homogeneous circular rigid mobile robots with wireless communication capabilities, a traditional representation defines each robot: \(R_{i}=(x_{i},y_{i},\theta_{i},r_{i},s_{i},c_{i})\), where \(i\in[1..M]\) and \(X_{i}(t)=\{x_{i}(t),y_{i}(t),\theta_{i}(t)\}\) represents the configuration vector of the robot \(i\) at time \(t\) (position of its centre and heading with respect to the inertial frame), \(r_{i}\) represents the radius of the robot body, and \(s_{i},c_{i}\) represent the sensory capabilities as maximum radius of sensing and maximum range of communication, respectively.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 7 Discussion
### Analysis of Simulation Results  
The simulation experiments across four distinct scenarios demonstrated the effectiveness of integrating LLM-driven agents into decentralized warehouse systems. In scenarios without capacity pooling, the LLM agent consistently outperformed its random counterparts by achieving higher warehouse utilization and greater overall profitability. Specifically, in Scenario 1 (single slot, no pooling), the LLM agent won the majority of simulation rounds and achieved superior warehouse utilization, demonstrating its ability to strategically allocate limited resources. However, in Scenario 2, where warehouse agents had five storage units and no access to pooling, the AI agent's performance was less dominant. Here, the AI employed a strategy of reserving capacity for higher-value offers in later slots, which ultimately led to underutilization. This reveals a limitation in the AI's predictive ability when operating in isolation from collaborative mechanisms. The introduction of the capacity pool in Scenarios 3 and 4 significantly improved AI performance. In Scenario 3 (single slot with pooling), the LLM agent exhibited enhanced decision-making by leveraging the pool to offload or forward offers efficiently, outperforming all competitors. Scenario 4 (multi-slot with pooling) provided the richest environment for testing strategic complexity. The LLM agent won most simulations, optimized its token yield per offer, and strategically submitted the highest number of successful pool offers while still accepting fewer low-value offers than  
Figure 14: Number of pooled and rejected offers in the best AI game.  
Figure 15: Storage utilization in the worst AI game.  
its peers. These findings underscore the LLM agent's capacity to maximize profit not just through utilization, but also by optimizing offer evaluation and timing.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
Therefore, CH selection is based on the specific capabilities of the SNs;
* Cluster Rotation: This criterion is used to determine the mechanism involved in the method to replace a CH. Certain methods replace their CHs periodically, while others replace their CHs after a pre-determined period or upon reaching a specified energy level. Each method tries to unify the network's energy usage level by adopting some energy threshold mechanisms.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 3 Models and Goals
### Goals  
Our primary concern in this research work is to achieve a high level of location privacy in a vehicular network. For this purpose, various factors and parameters are required. The factors include vehicle speed, traffic density, moving direction, and other road context information. The following goals are set to be achieved in this research work:  
1. Location anonymisation at diverse vehicle traffic conditions.
2. Virtual pseudonym exchange process to protect location under low traffic conditions.
3. Dynamic mix context zone creation based on different road parameters such as vehicle speed, direction, and traffic density.
4. The pseudonym change and exchange process are based on the road context information.
5. Preventing an adversary from linking various pseudonyms of a vehicle at different location spots.
6. Reducing the impact of privacy protection on the efficacy of road network applications.
# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 2 Related Work
### Comparison and Discussion
These approaches tended to flatten out the XML hierarchical structure. As a result of the XML hierarchy's absence, the quality of the ontology learning results could worsen, since an ontology should have a hierarchy structure. To address this absence, we considered the XML hierarchy structure in pivot models by exploiting the hierarchy imposed by the XML language. Furthermore, none of the reviewed approaches dealt with ontology refinement through coupling several methods to answer the missing knowledge during ontology learning. They usually used DBpedia as in [5] or predefined rules as in [4] to extract missing knowledge for refining learned ontologies. Contrary to this, we spent more effort on improving the refinement phase. Therefore, LOD such as DBpedia, WordNet, and lexico-syntactic patterns in metadata will be applied as references to accomplish the refinement phase. In contrast to [11], we do not learn an ontology from LOD; however, we make use of LOD, WordNet, and metadata as background knowledge for refining a learned ontology. Finally, this comparative study highlights that the previously discussed approaches in the scope of preexisting ontology evolution [4; 5; 12; 13] did not consider the ontology alignment, in contrast to the ontology merging. Indeed, close to [4], we consider an evolution process that is driven by the task of ontology learning. The proposed evolution process includes alignment and merging activities to consider the evolution of an initial ontology-based model using learning elements resulting from the learning process. Following the above discussion, we aim to close gaps within the related works by proposing an ontology-based model evolution approach that follows ontology learning to evolve an initial ontology-based model to answer the changes encountered in dynamic environments at runtime. In addition, we take into consideration the requirement for hierarchical XML pivot models to reorganize and improve the learned ontology representation. Furthermore, we focus on improving the ontology refinement phase, which relies on using different external background knowledge such as DBpedia, WordNet, and metadata. To sum up, the main strengths of the proposed approach are fivefold: (1) the fully automatic ontology-based model evolution guided by ontology learning once changes in the surrounding environments occur at runtime; (2) the support of heterogeneous input data; (3) the use of a hierarchical XML pivot model to maintain the input's hierarchy and to construct the learned ontology's hierarchy; (4) the use of multiple pieces of background knowledge together with a pattern-based semantic analysis, including simple patterns such as regular expressions to ensure the refinement of the learned ontologies; and (5) the neglect of expert intervention.
# Conversation-Based School Building Inspection Support System
## 3 Methodology
### Notification Module (N-Module)  
The notification module (N-module) is intended to inform assessors to start either routine inspections or post-event inspections. It determines which schools require inspections and sends direct notifications to the registered assessors of those schools. The notifying messages are sent to assessors' IM applications by the chatbot in their contact list as if a real person sent the notifications. After being notified, the assessors may start their inspections by interacting seamlessly with the chatbot without switching to another application or device. The N-module improves the efficiency of selecting critical schools based on earthquake data and enhances the intuitiveness of the application through the notifications for assessors to start inspections.  
Based on the different types of inspections, the N-module has two notifiers:  
1. Routine notifier, which works on a schedule and sends messages to inform assessors of regular inspections;
2. Post-event notifier, which is activated by earthquakes and informs the registered assessors of schools located in areas where the intensity scales reached the critical threshold.  
The routine notifier sends notifications to the registered assessors on selected dates. For example, schools in Taiwan may specify dates in July for the annual inspection since few students are present during their summer vacation. In addition, multiple dates for different inspection scopes can be specified. For example, assessors can schedule individual buildings for inspections on dates in different weeks or schedule inspections to focus on specific types of inspected targets. On the scheduled dates, the routine notifier sends messages detailing the scope of the inspection via IM from the chatbot to the registered assessors.  
The post-event notifier automatically selects the registered assessors that are required to be notified. For each school building, an intensity scale is assigned as the threshold based on the  
Figure 1: The system architecture.  
building's condition and expert opinion. When an earthquake occurs, the system obtains detailed information on the event, including the epicenter, the magnitude, and the observed intensity scales in different areas. The post-event notifier compares the observed intensity scales with the assigned threshold of school buildings and determines the buildings that require inspections. For the critical schools housing these buildings, the post-event notifier sends messages to the registered assessors from the chatbot via IM applications.
_Applied_  
_sciences_  
_Article_  
**Mechanical and Thermal Behavior of Ultem(r) 9085**  
**Fabricated by Fused-Deposition Modeling**  
**[PERSON] 1,*, [PERSON] 1**, [PERSON] 2, [PERSON] 2 and [PERSON] 1  
1 Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (C.B.)  
2 Pininfarina S.p.A., Via Nazionale 30, 10020 Campahiano, Italy; [EMAIL_ADDRESS] (P.C.); [EMAIL_ADDRESS] (G.L.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-0110904708  
Received: 30 March 2020; Accepted: 28 April 2020; Published: 1 May 2020  
**Featured Application: Ultem 9085 is a relative new material with well-known flame-retardant properties that has many applications in digital manufacturing and rapid prototyping. Thanks to its high mechanical performance, this material has potential applications in many fields, especially aerospace, automotive, and military industries which require a high strength-to-weight ratio.**  
**Abstract:** Fused-deposition modeling (FDM) is an additive manufacturing technique which is widely used for the fabrication of polymeric end-use products in addition to the development of prototypes. Nowadays, there is an increasing interest in the scientific and industrial communities for new materials showing high performance, which can be used in a wide range of applications. Ultem 9085 is a thermoplastic material that can be processed by FDM; it recently emerged thanks to such good properties as excellent flame retardancy, low smoke generation, and good mechanical performance. A deep knowledge of this material is therefore necessary to confirm its potential use in different fields. The aim of this paper is the investigation of the mechanical and thermal properties of Ultem 9085. Tensile strength and three-point flexural tests were performed on samples with XY, XZ, and ZX building orientations. Moreover, the influence of different ageing treatments performed by varying the maximum reached temperature and relative humidity on the mechanical behavior of Ultem 9085 was then investigated. The thermal and thermo-oxidative behavior of this material was also determined through thermal-gravimetric analyses.  
**Keywords:** fused-deposition modeling; mechanical properties; thermal behavior