esa-sceva/satcom-chunk-collection huggingface dataset:
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 2 Materials and Methods
### Linear Regression Analysis  
Linear regression is a linear approach to modeling the relationship between dependent variable y and one or more independent variables x. In linear regression, the relationships are modeled by using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Given a dataset \(\left\{y_{i},\ x_{i1},\ \ldots,\ x_{ip}\right\}_{i=1}^{n}\) of n statistical units, a linear regression model assumes that the relationship between the dependent variable \(y_{i}\) and the p of repressors \(x_{i}\) is linear. The model has the following form.  
\[y_{i}=\beta_{1}x_{i1}+\cdots+\beta_{p}x_{ip}+\varepsilon_{i}=X_{i}^{T}\beta+ \varepsilon_{i},\ \mathrm{i}=1,\ldots,\mathrm{n},\]  
Figure 5: Setting up drought disaster periods in view of damage from agricultural droughts.  
In the given equation, \(\beta\) is the coefficient of each independent variable, and p is the number of parameters estimated by linear regression. \(T\) means transpose, and \(x_{i}^{T}\beta\) means internality of \(x_{i}\) and \(\beta\). Moreover, \(\varepsilon_{i}\) is an error term, an error variable, which is an unobserved random variable, and means an error between the dependent variable and the independent variable. This is called linear regression because it assumes that the dependent variable is in a linear function relationship to the independent variable. However, it is wrong to think that the graph of \(y_{i}=X_{i}^{T}\beta+\varepsilon_{i}\) is a straight line and \(y_{i}\) is a linear function of \(x_{i}\). For example, there is the following linear regression. Since y = \(\beta_{1}+\beta_{2x}+\beta_{3}x^{2}+\varepsilon\) is linear with respect to \(x\) and \(x^{2}\), it can be said to be a linear regression even if the graph having the \(x\)- and \(y\)-axes is not linear. This expression is expressed in vector format, so it can be expressed as follows.  
\[\text{y}=\text{X}\beta+\varepsilon\]  
The meaning of each term in this equation is as follows.  
\[\text{y}=\left(\begin{array}{c}y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{array}\right)\ \text{X}=\left(\begin{array}{c}X_{1}^{T}\\ X_{2}^{T}\\ \vdots\\ X_{n}^{T}\end{array}\right)=\left(\begin{array}{c}x_{11}&\ldots&x_{1p}\\ x_{21}&\ldots&x_{2p}\\ \ldots&\ddots&\ldots\\ x_{n1}&\ldots&x_{np}\end{array}\right)\ \beta=\left(\begin{array}{c}\beta_{1}\\ \beta_{2}\\ \vdots\\ \beta_{n}\end{array}\right)\ \varepsilon=\left(\begin{array}{c}\varepsilon_{1}\\ \varepsilon_{2}\\ \vdots\\ \varepsilon_{n}\end{array}\right)\]
## 2 Materials and Methods
### Experimental and Task Design  
This study focused on evaluating the effects of using a BSIE in realistic conditions, specifically the presence of fatigue and awkward postures, while performing repetitive trunk flexion tasks. Thus, a 2 \(\times\) 2 \(\times\) 2 design was selected with the independent factors as assistance (without exoskeleton (N)/with exoskeleton (E)), posture (symmetry (S)/asymmetry (AS)), and time (beginning (B)/end (ED)). To assess the effects of assistance with a BSIE, we selected a passively actuated BSIE, named BackX Model AC (SuitX, Emeryville, CA, USA), set at medium support (\(\sim\)25 lbs. of support).  
The experimental setup included a portable adjustable stand in front of the participant such that the subject could bend at a -45\({}^{\circ}\) sagittal flexion angle. For simulating awkward postures, \(\sim\)45\({}^{\circ}\) asymmetry in the transverse plane towards the left was chosen, as per similar recent studies [26]. Lastly, to investigate the effects of fatigue, we incorporated a back-fatiguing task that involved sustaining trunk flexion for short durations that was performed intermittently. Cyclic flexion-extension was performed at the start and end of each session to assess the effects of fatigue. Three sessions were scheduled on separate days (with a gap of 48 h) per participant to avoid a potential carry-over effect between the levels of the assistance (E/N) factor, as shown in Figure 1. Among the three sessions, the first session included training, while the other two consisted of performing tasks.
## 4 Simulation Study
### Case Study 3: Under Random Attacks  
In this case study, the focus is on the performance of the proposed method under random attacks compared with the results under targeted attacks. The initial conditions are the same as in case study 2. Then, the analyzed network is subjected to random attacks. The nodes with low controllability indexes are disconnected randomly, and the minimum number of driver nodes of the remaining network is also calculated after every attack. The results are averaged over 100 realizations and the mean values are also plotted in Figure 5. It is shown that the minimum number of driver nodes is not clearly changed after five such attacks, which means that the network is very robust in response to random attacks, and the failure of those nodes may not obviously affect the controllability of the system. Furthermore, the identification results before and after five random attacks are plotted in Figure 7a,b. The results show that the controllability index distribution before and after random attacks is very similar, which means the driver nodes distribution is almost not changed. Roughly speaking, the nodes with higher controllability index distribution are more stable than the ones with higher controllability index distribution.  
Figure 5: Increase in \(N_{D}\) of the network after attacks.  
Figure 6: Effect of targeted attacks on controllability index distribution. (**a**) Original distribution; (**b**) Distribution after five targeted attacks.  
controllability indexes before attacks still have higher ranking in this analysis.
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion
### Angular and Linear Molar Distalization Movement
#### 4.1.2 Characteristics of the Appliances Positioned Buccally  
Maxillary molar distalization using appliances positioned buccally, supported by TADs, ranged from 1.83 mm using a single MI [24] to 4.2 mm using the Vestibular Force Vector [35], while the distal tipping was extended from 0.6\({}^{\circ}\) to 4.8\({}^{\circ}\) using a single MI [20].  
However, miniscrew placement in the buccal interradicular site showed dissimilar distalization features, using one MI or two MIs as an anchorage unit. Distalization movement with one MI varied from 1.83 mm to 2.8 mm along with distal tipping from 3.19\({}^{\circ}\) to 4.8\({}^{\circ}\)[20; 24]. Additionally, when two MIs were inserted buccally, they resulted in 2.91 mm-4.2 mm of distalization, while distal tipping ranged from 0.6\({}^{\circ}\) to 2.48\({}^{\circ}\)[37].  
In general, the current study revealed that distalization appliances anchored in the palate and supported by TADs achieved greater distalization bodily movement along with minimal distal tipping of the maxillary first molars in comparison with the Pendulums or the buccal methods supported by TADs.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Challenges in Sex Determination and Lip Print Classification  
A critical challenge in cheiloscopy lies in the classification of lip prints, particularly for sex determination, a task complicated by the inherent subjectivity of analog methods. While the Suzuki and Tsuchihashi classification system has been widely adopted [16; 18], its reliability is undermined by ambiguities in pattern definitions and inter-observer variability, as highlighted in prior critiques [19; 20; 21]. Our findings corroborate these limitations: analog prints frequently exhibited overlapping vertical and branched grooves, complicating unambiguous categorization. This variability was most pronounced in sex-based analyses, where subtle morphological differences (e.g., groove density and curvature gradients) proved inconsistently detectable via visual inspection alone.  
Digital cheiloscopy addresses these challenges by enabling quantitative curvature mapping of 3D lip models, thereby reducing reliance on subjective pattern matching. For instance, digital scans can allow precise measurements of groove angles and branching frequencies, metrics unavailable in analog workflows. Although some studies posit lip prints as potential sex determinants [22], our results emphasize that analog methods, lacking standardized digital protocols. are inherently prone to interpretive inconsistencies. Thus, the transition to 3D methodologies is not merely advantageous but necessary to establish forensic credibility in this domain.
###### Abstract  
Background: The objective of this study was to digitally evaluate the effect of the cervical emergence of restorations in terms of gingival tissue volume. Materials and methods: In this study, 31 upper front teeth (from canine to canine) were selected and prepared using the biologically oriented preparation technique (BOPT). Each tooth had undergone a change in its provisional process with different emergence profile angles: 30\({}^{\circ}\) and 60\({}^{\circ}\). Each sample was digitalised (STL file) using an intraoral scanner. By using various pieces of manipulation software for STL files, a volumetric analysis protocol was carried out. Changes in gingival thickness and position were measured at the buccal and the lingual positions. Results: By increasing the angulation of the emergence profile to 60\({}^{\circ}\), the gingival thickness increased by \(0.255\pm 0.381\) mm (\(p=0.001\); \(t\)-test) at buccal and by \(0.038\pm 0.381\) mm at lingual (\(p=0.600\); \(t\)-test). In relation to the gingival height, at buccal, there is an apical displacement of \(0.262\pm 0.246\) mm (\(p=0.001\); \(t\)-test), and at lingual, there is an apical displacement of \(0.390\pm 0.279\) mm (\(p=0.001\); \(t\)-test). It was shown that an increase in the provisional emergence profile angle significantly diminishes the gingival height while significantly increasing gingival thickness. The [PERSON]'s correlation coefficient, \(r=0.636\), was moderately high and significant (\(p=0.001\); Pearson). Conclusions: The soft tissue surrounding the BOPT restored tooth evolved according to the prosthetic emergence angle of the provisional restoration, enabling us to plan for an apico-coronal and bucco-lingual displacement of the gingival tissue prior to the definitive restoration.  
B 2020, 10, 4108; doi:10.3390/app10124108
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 5 Results
### Results from Multi-Technology Fusion
#### 5.1.3 Case 3: Low-Density Deployment of High-Precision Technologies  
In this third case, the scenario is set up as a more realistic situation with less dense high-precision devices than in the other Cases. In this case, the low-density of devices in the scenario makes it impossible to locate a target by using the high-precision information from UWB and WiFi devices, since the visibility of at least four reference points is not guaranteed. Therefore, in this case, the missing ranges are complemented with LTE ranges, which are less accurate. Figure 3b represents, in light blue dots, the positions where the location service is provided by using the LTE data. In Figure 7, there is a comparison of the location error between the only LTE and multi-technology fusion between LTE with UWB anchors and WiFi AP ranging information.  
In addition, the CDF of Case 3 has been also included when using fusion with 1 UWB anchor, 1 WiFi AP or LTE plus both technologies as shown in Figure 8. In this Case, precise ranging information greatly enhances the localisation accuracy of the multi-technology system.  
Figure 6: CDF of the horizontal error for Case 1 and 2.  
Figure 7: Horizontal error distribution of LTE and fusion for Case 3.  
Comparing Figure 9 (imported from [10]) with Figure 8, the fusion algorithm stands out in both works showing the benefits of using high-precision data in areas only covered by low-precision ranging technology such as LTE. In Case 3, just as in the simulation of [10], LTE provides a worse precision performance when it is used in isolation. There is a contrast between the results obtained in this work and in [10] because in the simulation the case of the study was ideal in which some real-world conditions such as reflections, clutters, and interferences, were omitted.
_Appl. Sci._**2013**, \(3\), 278-287; doi:10.3390/app3010278  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Laser Induced Multiphoton Effects in Nano-Graphene Molecules**  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Nanjing National Laboratory of Microstructures, Laboratory of Solid State Microstructures, and  
School of Physics, Nanjing University, Nanjing 210093, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Physics, Indiana State University, Terre Haute, IN 47809, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-812-237-2043; Fax:+1-812-237-4396.  
_Received: 28 November 2012; in revised form: 19 February 2013 / Accepted: 3 March 2013 / Published: 13 March 2013_  
**Abstract:** We perform first-principles calculations to study the high-order harmonic generation induced in graphene nanostructures by the laser field. Three distinct signals are noticed: the integer higher-order harmonic generation (HHG), the shifted fractional order peaks from the integer order harmonics, and the intrinsic emissions. Due to the small gap between HOMO and LUMO of graphene molecule, the HHG can be generated for the infrared laser pulse with the photon energy ranging from 20 meV to \(\sim\)1 eV. The intrinsic emission corresponds to the electron excitation between eigenstates. Using a laser pulse with a photon energy of 0.042 eV and amplitude of 0.2 V/A, HHGs up to 19 th order are identified. Unsaturated graphene molecule is an excellent medium for HHG. Moreover, the HHG signals are very sensitive to the hydrogen passivation. Our results also indicate that HHG can be a promising method for detecting the product in the fabrication of graphene molecules.  
**Keywords:** high harmonic generation; graphene; nonlinear processes
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 3 System Component
### Testbed  
The 40 m \(\times\) 30 m emergency room where the prototype system was installed is enclosed with entrances and concrete walls. There are four treatment sections, all of which have no doors and are partitioned by concrete walls, a computed tomography (CT) room, two X-ray rooms, a control room for CT and X-ray scans, nurses' and doctors' offices, a waiting room, a staff station, a storage room and a hallway. Concrete walls and metal doors also partition the control room, CT room and X-ray rooms. The emergency room has about 100 medical assets, and four nurses spend about 30 min manually checking whether each asset is within the target area. Nurses selected 21 mobile medical objects, a subset of the \(\sim\)100 assets, to evaluate the developed system. The medical asset tracking system is composed of an application, a gateway, 38 anchor nodes deployed within the emergency room or on the boundary of the area and 21 mobile nodes attached to 21 mobile medical objects, one for each object. The medical assets here are two wheelchairs, four ventilators, five syringe pumps and 10 IV poles. Using the system, one nurse can finish the work within one minute. Figure 3 shows a map of the emergency room enclosed with bold solid lines. They indicate the boundary of the monitoring area.  
From an operational perspective, the system required maintenance approximately once every two weeks to exchange the batteries on the mobile assets. Each mobile asset was randomly mobile with a wakeup-and-report interval of 30 s. The RSSI-based location estimations showed an accuracy of \(\sim\)2.5 m, which was enough to satisfy the staff's requirements in a non-line-of-sight (NLOS)-dominated environment. On the other hand, the anchor nodes were powered using wall plugs; thus, they were awake for the entire testing period.
## 5 Experiments
### Problem Instances  
The instances used for numerical experiments were created randomly with varying complexity and constrainedness. For each instance, all the parameters including the number of hardware units, software components, network links, and their specifications are generated randomly. We conducted numerical experiments on problems with 15, 33 and 60 hardware units and 23, 34, 47 and 51 software components.  
Additionally, the proportion of software components that require the services of each other is expressed as a percentage which can be 10%, 25%, 50%, 75%, and 100%. As an example, the instance H33C47I10 has 33 hardware units, 47 software components, 10% of which interact with each other and require to be deployed either in the same hardware unit or in hardware units that have a network between them. It is important to note that there is no guarantee for a feasible solution for any of the problems.  
In our optimisation process, there are two objectives, namely, reliability and communication overhead. However, before optimising these objective, we first minimise the amount of violations over the existing constraints in order to achieve feasibility. As a next step, we maximise reliability and the communication overhead, simultaneously.  
To obtain the numerical results, we executed each algorithm 30 times with a 600 s time limit. The results at the end of the time limit are reported and analysed.
## 4 Overview of the Privacy-Protection Regulations and Frameworks
### The GDPR in Organisational Data Sharing  
In the context of organisational data sharing, the enforcement of privacy regulations in recent years has brought a significant amount of transformation in the data-sharing practices [41]. These legal regulations have been implemented by the governments to protect the privacy of individuals by allowing them to have privacy rights when sharing their information. The most comprehensive privacy regulations, such as the GDPR, have been effective in governance, awareness, and monitoring of the usage of individual data, forcing organisations to protect privacy more proactively [42].  
Privacy regulations have introduced a major change in how organisations share data. The requirements for receiving consent prior to data collection, as well as the organisational transparency in data sharing, have encouraged organisations to re-evaluate their data-acquisition and privacy practices, leading to provisioning clear, concise, and easily accessible privacy policies that explain data processing and data sharing [43]. This means organisations must clearly communicate the purposes for which data will be used, which entities will have access to these data, and how these data are protected.  
These privacy regulations have also impacted business partners and third-party entities, which also need to show their compliance with the privacy regulations by including appropriate data-protection measures [44]. Any third-party entity that wishes to collaborate with first-party organisations must have due-diligence processes where organisations assess the data-handling processes before sharing any data. For example, if a hospital wishes to share a certain patient's health data with a research institute, the hospital must ensure the research institute complies with the necessary data-protection standards.  
In recent years, many governments have developed privacy regulations to govern and maintain the privacy protection of their citizens around the world. The European Union released their privacy regulation in 2018, setting out strict rules and principles for the processing of personal data to ensure the privacy- and data-protection rights of individuals. The GDPR consists of significant protection of personal data throughout its lifecycle, most notably when sharing data with other organisations. For third-party organisations, they must establish GDPR-compliant data-processing agreements with their clients. Being GDPR-compliant entails the responsibility and the obligations of both parties to guarantee that the data processing is protected and processed in accordance with the GDPR.  
The key principles of the GDPR are (1) lawful processing, (2) purpose limitation, (3) data minimisation, (4) accuracy, (5) storage limitation, (6) integrity and confidentiality, and (7) accuracy [45]. These principles define a summary of the GDPR; failure to comply with these rules can result in significant fines and legal consequences.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion  
### Dataset and Analysis  
This research uses the widely adopted UCI repository dataset for performance evaluations. This unbalanced dataset contains a total of 5574 English SMS messages, in which 747 SMS messages are spam and 4827 are ham, as shown in Table 1. These messages were collected from Grumbletext--a UK public forum (www.grumbletext.co.uk), the SMS corpus from the National University of Singapore, and [PERSON]'s Ph.D. thesis [58].  
The experimental code is developed with Python 3.7 and Python packages including pyhanlp, pomeranate, and collections. The code runs on a MacBook with an Intel Core i7-7820 CPU and 16 GB of memory.  
We first split the SMS, extract words, and remove the stop words. This resulted in 9955 meaningful words being extracted in total to form the set of observation states \(W\). Then, we calculate the term frequency of each word in each SMS. The summary of the statistical results is shown in Table 2. We find that:  
* 9272 words appear only once in a single SMS and accounts for 93.13%.
* The words that appear three times and above only account for 1.03% in total.  
Apparently, most of the words only occur once in a single SMS. Therefore, the feature extraction algorithms, like TF.IDF, does not work well for SMS spam detection.
## Appendix A. Distance Estimation Error in TOA-Based Localization  
We assume that the measurement of the time \(t\) required for the signal to travel between two nodes (one-way or round trip) is affected by an additive Gaussian random noise with variance \(\sigma_{t}^{2}\). The variance of the TOA measurement can be reduced if \(n\) measurements are averaged, as mentioned in Section 3. Specifically, the averaged measured value of the time \(t\) can be expressed as:  
\[\tilde{t}_{av}=t+\mathcal{N}\left(0,\frac{\sigma_{t}}{\sqrt{n}}\right) \tag{23}\]  
On the other hand, the distance \(d\) between transmitter and receiver is easily related to the time \(t\): \(d=c\cdot t\) for one-way TOA measurements or \(d=c\cdot t/2\) for two-way TOA measurements, where \(c\) is the speed of light. Thus, the estimated distance \(\tilde{d}\) will be a Gaussian random variable:  
\[\tilde{d}=d+\mathcal{N}\left(0,\sigma\right) \tag{24}\]  
where the value of \(\sigma\) depends on the measurement type:  
For one-way TOA measurements:  
\[\sigma=c\cdot\sigma_{t}/\sqrt{n} \tag{25}\]  
For two-way TOA measurements:  
\[\sigma=c\cdot\sigma_{t}/2\sqrt{n} \tag{26}\]  
It can be noticed that the error (or variance) in the distance estimation does not depend on the actual distance between transmitter and receiver. From these expressions, we can obtain the number of packets that we need to receive in order to achieve a given accuracy in the distance estimation.
## 3 SDWSNs and IEEE 802.15.4e TSCH
### IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH)  
This section introduces the time-slotted channel hopping (TSCH) technology, which serves as the MAC layer in the architecture we've chosen. The definition of TSCH is highlighted first, followed by the scheduling concept.