esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
Pressure measurement between soft tissue and the soft padding of the pHMI is generally a difficult matter as human soft tissue hampers the reliability of measurements [64]. The pneumatic sensor was chosen in order to apply a system that could be easily integrated into the different pHMIs, without destroying the integrity of the pHMI and reducing their comfort. Using this type of sensor should not have any impact on the NIRS outcomes, because the sensor is flat, soft, and does not take much further space in the interface. Nevertheless, it is difficult to generalize the occurring pressures and to infer the total circumferential pressure of the pHMI. Due to the locally defined measurement, both pressure peaks and lows may have been recorded. To directly relate circumferential pressures to NIRS outcomes, a measurement method directly within the interface structure itself, for example, using strain gauges, would be necessary.  
Deep tissue oxygenation was monitored directly under the tourniquet or pHMI, respectively. The NIRS readings may have been influenced by the pressure of the tourniquet or pHMI on the NIRS optodes and the tissue since the tissue underneath the optodes was compressed and possibly slightly displaced during occlusion.  
NIRS measurements in standard occlusions are usually performed on relaxed muscles in a supine or sitting position to minimize the hydrostatic effects and thus establish comparable baseline conditions in the circulatory system. Since measurements in a sitting or even lying position were not possible with the exoskeletons, the NIRS investigation was limited to the area underneath the arm interface, which is at heart level. However, it would have been important to see how oxygen saturation distal to the interface, for example, on the forearm or hand, would have reacted to the occlusions of the pHMI. More basic research while standing would be needed to obtain valid results for areas further distal.  
The study sample size is relatively small with 12 subjects. The study population was chosen very specifically to guarantee high-quality NIRS signals through low participants ATT [41]. The generalizability of the data may be reduced due to these two issues. Nevertheless, the results indicate an effect that has not received much attention within exoskeleton research to date. For further studies dealing with the effects of exoskeleton interface pressure on blood supply, it will be worthwhile to increase the generalizability and impact by choosing a larger sample size and a more diverse study population.
###### Abstract  
The fracture of microcapsules is an important issue to release the healing agent for healing the cracks in encapsulation-based self-healing concrete. The capsular clustering generated from the concrete mixing process is considered one of the critical factors in the fracture mechanism. Since there is a lack of studies in the literature regarding this issue, the design of self-healing concrete cannot be made without an appropriate modelling strategy. In this paper, the effects of microcapsule size and clustering on the fractured microcapsules are studied computationally. A simple 2D computational modelling approach is developed based on the eXtended Finite Element Method (XFEM) and cohesive surface technique. The proposed model shows that the microcapsule size and clustering have significant roles in governing the load-carrying capacity and the crack propagation pattern and determines whether the microcapsule will be fractured or debonded from the concrete matrix. The higher the microcapsule circumferential contact length, the higher the load-carrying capacity. When it is lower than 25% of the microcapsule circumference, it will result in a greater possibility for the debonding of the microcapsule from the concrete. The greater the core/shell ratio (smaller shell thickness), the greater the likelihood of microcapsules being fractured.  
self-healing concrete; microcapsule; capsular clustering; circumferential contact length; core/shell thickness ratio; XFEM; cohesive surface
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 6 Conclusions  
In this review, we present a comprehensive exploration of the evolving landscape of PA, characterized by the integration of smart sensors and advanced technologies. The papers discussed highlight the transformative potential of PA in improving crop management, resource use, and overall agricultural sustainability.  
The challenges and limitations highlighted in these studies, such as sensor deployment issues, data accuracy problems, and the need for sustainable energy sources, underline the complexity of implementing precision farming systems. However, the review also points to the promising future of this field, where emerging technologies like the IoT, big data analytics, and Artificial Intelligence are expected to play a significant role.  
The integration of IoT is set to revolutionize data collection, providing real-time information on crop health, soil conditions, and environmental changes. The analysis of big data will enable more sophisticated predictive modeling, enabling farmers to make informed decisions. AI, with its ability to automate decision-making processes, provides advanced solutions for crop management, disease detection, and resource optimization.  
The synergy between smart sensors and advanced analytics, as illustrated by the articles reviewed, is the key to overcoming current limitations and making precision agriculture the foundation for efficient, sustainable farming practices. As ongoing research develops and technologies continue to improve, an optimistic trajectory remains for PA, making a significant contribution to global food security and environmental sustainability.  
**Author Contributions:** Conceptualization, A.S., E.Z., R.S., D.T. and M.F.; methodology, A.S., E.Z., R.S., D.T. and M.F.; software, A.S., E.Z., R.S., D.T. and M.F.; validation, A.S., E.Z., R.S., D.T. and M.F.; formal analysis, A.S., E.Z., R.S., D.T. and M.F.; investigation, A.S., E.Z., R.S., D.T. and M.F.; resources, A.S., E.Z., R.S., D.T. and M.F.; data curation, A.S., E.Z., R.S., D.T. and M.F.; writing--original draft preparation, A.S., E.Z., R.S., D.T. and M.F.; writing--review and editing, A.S., E.Z., R.S., D.T. and M.F.; visualization, A.S., E.Z., R.S., D.T. and M.F.; supervision, A.S., E.Z., R.S., D.T. and M.F.; project administration, A.S., E.Z., R.S., D.T. and M.F.; funding acquisition, A.S., E.Z., R.S., D.T. and M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was carried out within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILENZA (PNNR)--MISSIONe 4 COMPONENTE 2, INVESTIMENTO 1.4--D.D. 1032 17/06/2022, CN0000022). This manuscript reflects only the authors' views and opinions, neither the European Union nor the European Commission can be considered responsible for them.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 1 Introduction
The notable advantage of AutoTDGen is reducing the risks of randomly generating redundant, low-quality, and unreliable test cases. In contrast to the existing automated test generation tools, following a model-based approach utilizing the ADs of the SUT as a test base incorporated with the optimization heuristic not only automates the test generation process but also reduces the risk of generating incorrect random test cases by accurately identifying what behavior of the system to test with what data values, and it exposes many requirement related issues in the early stage of software development life cycle. This paper extends our previous work [23] with the following additional contributions. * Automate the entire test data generation process. For this purpose, we introduce a search-based approach using a GA with a fitness function designed to reward maximum coverage of def-use pairs. * In addition to automating the test data generation process, this approach, unlike our previous study on manually annotating the flow of data in an AD and converting it into an intermediate test model [24], can directly generate test cases from models of the SUT without transforming them into intermediate test models. * We have empirically compared and contrasted the effectiveness of AutoTDGen with that of DFAAD and EvoSuite. To compare the fault detection effectiveness and statement and branch coverage performance of AutoTDGen with the two alternative approaches, we performed an experimental investigation using three software systems chosen from open-source libraries. Through the experiments, we also aimed to evaluate if there is any variation in the types of faults detected by the proposed AutoTDGen and alternative approaches along with the interaction relationship between the test coverage and fault detection effectiveness of the adopted techniques and subject properties. The rest of this paper is organized as follows: Section 2 introduces related work. Section 3 presents the background of this study, revisiting the data flow representation and concepts in an AD. It also provides an example and some definitions. Section 4 introduces the main concepts and activities for automatic test data generation using the AD and search-based technique. An experimental investigation to assess and compare the proposed technique with two alternative approaches in terms of fault detection effectiveness and coverage performance is described in Section 5. Section 6 discusses the findings based on the reported results. Finally, Section 7 discusses possible threats to the validity of this research, with the conclusion and future work presented in Section 8.
## 3 Test Bench Layout
### Batteries  
The batteries are the auxiliary power source and must handle the high-frequency component of the power demand. For the experimental setup, two series-connected 12 V lead-acid batteries with a capacity of 40 Ah are adopted and directly connected to the DC bus. The main properties of the batteries are reported in Table 2. For this preliminary setup of the test bench, lead-acid batteries were preferred to other higher-performing technologies such as Li-ion or NiMH batteries. This choice was motivated by their easier implementation, lower costs and simple management, which does not require a battery management system. Future improvements might introduce batteries with different, higher-performing technologies. However, the implementation of batteries based on other technologies, such as Li-ion batteries, must be accompanied by additional elements to improve the system safety and management [43]. In order to take a precautionary approach, the batteries are connected to the DC bus through a fuse.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Dataset
#### 2.1.2 BraTS Dataset  
The BraTS 2015 dataset is a standard benchmark dataset for MRI segmentation tasks. It includes 220 multi-modal scans of patients with high-grade glioma (HGG) and 54 with low-grade glioma (LGG). T1-weighted, contrast-enhanced T1+C, T2-weighted, and FLAIR images are available. The data had a common dimension of 240 \(\times\) 240 \(\times\) 155 with 1 mm\({}^{3}\) resolution. The annotation contains five classes: 0 for background, 1 for necrotic core (NC), 2 for edema (OE), 3 for non-enhancing core, and 4 for enhancing core. The evaluation follows the rules of the competition by merging the predictions into three sets: whole tumor (classes 1,2,3,4), core (classes 1,3,4), and enhancing core (class 4). The train to test ratio was 10:1 in each of the experiments we conducted.
# Answer Set Programming for Regular Inference
## 2 Preliminaries
### Non-Deterministic Finite Automata  
A _non-deterministic finite automaton_ (NFA) is a five-tuple \(A=(\Sigma,Q,s,F,\delta)\) where \(\Sigma\) is an alphabet, \(Q\) is a finite set of states, \(s\in Q\) is the initial state, \(F\subseteq Q\) is a set of final states, and \(\delta\) is a relation from \(Q\times\Sigma\) to \(Q\). Members of \(\delta\) are called _transitions_. A transition \(((q,a),r)\in\delta\) with \(q,r\in Q\) and \(a\in\Sigma\), is usually written as \(r\in\delta(q,a)\). Relation \(\delta\) specifies the moves: the meaning of \(r\in\delta(q,a)\) is that automaton \(A\) in the current state \(q\) reads \(a\) and can move to state \(r\). If for given \(q\) and \(a\) there is no such \(r\) that \(((q,a),r)\in\delta\), the automaton stops and we can assume it enters the rejecting state. Moving into a state that is not final is also regarded as rejecting but it may be just an intermediate state.  
It is convenient to define \(\delta\) as a relation from \(Q\times\Sigma^{*}\) to \(Q\) by the following recursion: \(((q,ya),r)\in\delta\) if \(((q,y),p)\in\delta\) and \(((p,a),r)\in\delta\), where \(a\in\Sigma\), \(y\in\Sigma^{*}\), and requiring \(((t,\varepsilon),t)\in\delta\) for every state \(t\in Q\). The _language accepted_ by an automaton \(A\) is then  
\[L(A)=\{x\in\Sigma^{*}\mid\text{there is $q\in F$ such that $((s,x),q)\in\delta$}\}. \tag{1}\]  
Two automata are _equivalent_ if they accept the same language.  
Let \(A=(\Sigma,Q,s,F,\delta)\) be an NFA. Then we will say that \(x\in\Sigma^{*}\) is: (a) _recognized by accepting_ (or _accepted_) if there is \(q\in F\) such that \(((s,x),q)\in\delta\), (b) _recognized by rejecting_ if there is \(q\in Q-F\) such that \(((s,x),q)\in\delta\), and (c) _rejected_ if it is not accepted.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods  
### Data and Instrumentation  
Data for 75 days were collected from three stations (15, 30 and 30 days from SNA0P, SAO0P and SJCU stations, respectively). SNA0P (2.84\({}^{\circ}\)W, 71.67\({}^{\circ}\)S) is located in Antarctica, SAO0P (46.65\({}^{\circ}\)W, 23.55\({}^{\circ}\)S) and SJCU (45.96\({}^{\circ}\)W, 23.21\({}^{\circ}\)S) are located in Sao Paulo, Brazil. The receiver type at SNA0P and SJCU stations is Septentrio PolaRxS 2.9.0 and that at SAO0P station is Septentrio PolaRxS 2.9.6. Data from 14 days (7, 3 and 4 days from SNA0P, SAO0P and SJCU stations, respectively) with scintillation were used for the PPP improvement experiments, where scintillation events were identified using the methods presented in [PERSON] et al. [27]. The 14 days are: 7 September 2017, 8 September 2017, 13 September 2017 (SAOOP station); 4 September 2017, 7 September 2017, 8 September 2017, 13 September 2017 (SJCU station) and 18 February 2016, 2 April 2016, 13 April 2016, 9 May 2016, 6 June 2016, 28 July 2016, 13 October 2016 (SNA0P station).  
One day data without scintillation was used to estimate the convergence time of the PPP software for each station. The scintillation-free data on 29 May 2016 at SNA0P, 1 September 2017 at SAO0P and 1 September 2017 at SJCU were selected (see Section 3.1.1). MP and ROTI were respectively derived from the 1/60 Hz and 1/30 Hz data, while S4 and \(\sigma_{\Phi}\) at 1 min interval were generated from the 50 Hz data.  
Data from all 75 days were used to generate the mild threshold (MT) and extreme threshold (ET) that were used to define outliers for the observation removal strategy. This approach is explained in detail by [PERSON] et al. [27] and is summarized below, where MT and ET are defined as:  
\[\text{MT}=\text{ Q}_{3}+1.5\text{IQR} \tag{1}\]  
\[\text{ET}=\text{ Q}_{3}+3\text{IQR} \tag{2}\]  
\[\text{IQR}=\text{ Q}_{3}-\text{Q}_{1} \tag{3}\]  
where \(\text{Q}_{3}\) and \(\text{Q}_{1}\) are the upper (3) and lower (1) quartiles for height or 3D positioning errors. MT is mild threshold and ET is extreme threshold.
## 3 Experimental Setup
### Training Results  
Figure 10 shows the trends in training accuracy and loss for each encoding method across 50 epochs.  
The training accuracy curves show that the 3 Phase-DWT method quickly reaches a high accuracy, demonstrating a strong early performance. Despite using a single-channel input, the Id-STFT method also records the highest training accuracy. In contrast, among the three-phase methods, 3 Phase-STFT converges more slowly and attains a lower accuracy.  
Figure 9: Training and validation accuracy and loss for each of the six encoding methods: (a) 3 Phase-RGB; (b) 3 Phase-DWT; (c) 3 Phase-STFT; (d) Id-Linear; (e) Id-DWT; (f) Id-STFT.  
Similarly, within the Id-based methods, Id-DWT converges slowly and yields the lowest training accuracy.  
In terms of training loss, both 3 Phase-DWT and Id-STFT exhibit a rapid and stable reduction, indicating effective learning. Conversely, 3 Phase-STFT and Id-DWT maintain higher loss values and a slower convergence.  
These results confirm that all six methods were trained successfully, with no significant oscillations in the learning curves, suggesting an appropriate learning rate. Furthermore, the absence of any increase in training loss indicates that overfitting did not occur.
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 3 Methods
### Attribute Community Layout
#### 3.1.2 Repulsive Force  
The repulsive force is the force that an attribute community is repelled by others, and its function is to spread the entire network layout on the plane. For community \(p\), the formula for the repulsive force exerted by the community \(q\) is as follows:  
\[F_{r}(p,q)=\alpha(t)\cdot strength_{r}(p,q)\cdot\delta \tag{11}\]  
where \(strength_{r}(p,q)\) is the repulsive strength of the community \(p\) by the community \(q\), \(\delta\) is a multi-segment function, the formula is as follows:  
\[strength_{r}(p,q)=\sqrt{\frac{n_{c_{p}}+n_{c_{q}}}{n}}\cdot S_{max} \tag{12}\]  
\[\delta=\begin{cases}1,d_{pq}\leq L_{pq}\\ k,d_{pq}>L_{pq}\end{cases} \tag{13}\]  
where \(S_{max}\) is the maximum setting of the repulsive strength and the default value of \(S_{max}\) is 600, and \(k\) is a coefficient between 0 and 1. When the distance between the communities is less than the target distance, the node is subject to greater repulsion. Finally, the horizontal and vertical velocity changes of the attribute community \(p\) under the action of repulsive force are as follows:  
\[\Delta v_{rx}(p)=-\frac{x_{pq}}{d_{pq}}\cdot F_{r} \tag{14}\]  
\[\Delta v_{ry}(p)=-\frac{y_{pq}}{d_{pq}}\cdot F_{r} \tag{15}\]
## 2 Background and Related Work
### Cache Attacks  
Cache memories create covert channels that can be exploited to extract sensitive information. When a process tries to access some data, if it is already loaded into the cache (namely, a cache hit), the time required to recover these data is significantly lower than the access time in the case the data have to be retrieved from main memory (cache miss). Therefore, if the execution time of a cryptographic process depends on the previous presence (or not) of the accessed data on the cache memory, this time information can be exploited to gain private information (such as secret keys) from cryptographic processes. Traditionally, cache attacks have been classified as:  
* _Time driven attacks_: The information is learned by observing the timing profile for multiple executions of a target cipher.
* _Trace driven attacks_: The information is gained by monitoring the cache directly, considering that the attacker has access to the cache profile when running the target process.
* _Access driven attacks_: The information is retrieved from the sets of the cache accessed during the execution of the target process  
The cache memory was first mentioned as a covert channel to extract sensitive information by [PERSON] [26] in 1992. In 1996, [PERSON] [27] introduced the first theoretical attacks, as did [PERSON] [28] in 1998 describing the possibility of performing attacks based on cache hit ratios. In 2002, [PERSON] [29] studied a theoretical example of cache attacks for DES, which was also used by [PERSON] [30] to study timing side-channels created due to table lookups. In 2004, [PERSON] [5] proposed the first time-driven attack after observing non-constant times when executing cryptographic algorithms. Although the attack he presented was not practical, his correlation attack has been investigated extensively and even retried between VMs recently [31]. Soon after, another attack was proposed by [PERSON] [32], who suggested that an attacker could determine cache ways occupied by other processes, measuring access times to all ways of a cache set. He found that these times are correlated with the number of occupied ways.  
In 2006, [PERSON] et al. [1] proposed two techniques, which have been widely used since then, named _Evict+Time_ and _Prime+Probe_. These techniques are intended to allow an attacker to determine the cache sets accessed by a victim process. A significantly more powerful attack that exploits shared memory and the completely fair scheduler (CFS) was proposed by [PERSON] [33]. The same principles of the attack were later exploited by [PERSON] and [PERSON] [2], who named the attack _Flush+Reload_. The target of this attack was the L3 cache, as it is inclusive and shared among cores. From those, the _Flush+Reload_ and the _Prime+Probe_ attacks (and their variants) standout over the rest due to their higher resolution.
## 2 Materials and Methods
### Sample Preparation of Composite Material
#### 2.3.3 Sample Preparation  
Referring to the experiments conducted by [PERSON] [23], when designing the composites' ratios, the mass fractions of multi-walled carbon nanotubes and polyetheretherketone were fixed at 6% and 64%, respectively. CF, CF-D, CF1, CF2, CF3, CF4, and polyetheretherketone were mixed uniformly and put into a high-speed ball mill to co-mingle, preparing the composite materials CF-PEEK (CP), CF-D/PEEK (CDP), MWCNT/HA/CF/PEEK-1 (MHCP1), MWCNT/HA/CF/PEEK-2 (MHCP2), MWCNT/HA/CF/PEEK-3 (MHCP3), and MWCNT/HA/CF/PEEK-4 (MHCP4). Afterward, they were placed in a vacuum drying oven for backup. The mass fraction of each component is shown in Table 1.  
The main reason for setting the CP at 30% carbon-fiber-reinforced polyetheretherketone is that this ratio usually achieves a good balance of properties in the material. Carbon fiber enhances the strength and stiffness of the polymer while maintaining a relatively low density, resulting in excellent mechanical properties. The 30% carbon fiber content allows for cost control while maintaining good performance, making this formulation widely available in the marketplace.  
The multi-walled carbon nanotube/nanohydroxyapatite/carbon-fiber-reinforced polyetheretherketone composites were fashioned into mechanical specimens using a hot press molding process. The specific hot press process flow was as follows: Firstly, the hot press mold was heated to 200 \({}^{\circ}\)C. Secondly, the composite powder was put into the mold for preheating. After the mold was closed, it was heated to 250 \({}^{\circ}\)C, 280 \({}^{\circ}\)C, and 310 \({}^{\circ}\)C; then, pressurization and release were repeated several times. When the temperature reached approximately 370 \({}^{\circ}\)C, the pressure was maintained at approximately 10 MPa, the holding time was approximately 20 min, and the cooling water was activated until the temperature decreased to approximately 150 \({}^{\circ}\)C, after which the pressure was removed from the mold. Therefore, the temperature change process of the whole carbon fiber composite hot compression molding is as follows: initial mold heating, continuous heating, constant temperature, and cooling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Form**} & \multicolumn{4}{c}{**Mass Fraction (\%)**} \\ \cline{2-5}  & **MWCNTs** & **HA** & **CF** & **PEEK** \\ \hline CP & 0 & 0 & 30 & 70 \\ CDP & 0 & 0 & 30 & 70 \\ MHCP1 & 6 & 0 & 30 & 64 \\ MHCP2 & 6 & 6 & 24 & 64 \\ MHCP3 & 6 & 12 & 18 & 64 \\ MHCP4 & 6 & 18 & 12 & 64 \\ \hline \end{tabular}
\end{table}
Table 1: Composite mass fraction.