esa-sceva/satcom-chunk-collection huggingface dataset:
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 7 Conclusions  
Photonic packaging is an essential step in realizing highly integrated Si photonic devices for both small-scale prototyping and commercial mass-production. The packaging of a photonic device is often the most expensive element of the overall module fabrication, and can involve major technical challenges that need to be addressed at the PIC design stage. Simultaneously satisfying the optical, electrical, and thermal design considerations for a PIC requires a 'joined up' approach that goes well beyond the most widely recognized issue of Fiber-to-PIC coupling. Researchers in packaging are increasingly working with advanced design tools, and evolving packaging equipment, to meet these challenges. As photonic devices and technologies transition out of the laboratory and into the market, photonic packaging is also maturing to keep pace--its focus is shifting from testing and prototyping towards standardized and scalable commercial implementation.  
This review paper includes Photonic Packaging developed under several European and Irish research calls, including: FP7-PLAT4M (318178), FP7-FABULOUS (318704), H2020-TIPS (644453), H2020-CARDIS (644798), and SFI/12/RC/2276 and SFI/14/ADV/RC2761 from Science Foundation Ireland. In addition, the authors would like to thank [PERSON], [PERSON], and [PERSON] for their technical assistance in assembling and imaging samples and modules.  
This review paper collects the results and contributions from many projects and procedures developed by the Photonic Packaging Group at the Tyndall National Institute in Ireland. [PERSON] and [PERSON] prepared the review manuscript; [PERSON]-[PERSON] made the grating-coupler measurements; [PERSON] developed the high-speed electronic interconnects; [PERSON] made the thermal measurements; [PERSON] made the Zemax simulations; [PERSON] developed the VCSEL flip-chip; [PERSON] made the grating-coupler flip-chip measurements; [PERSON] made the _COMSOL_ simulations; [PERSON] designed the high-speed transmission lines; [PERSON] developed the flip-chip and wire-bonding; [PERSON] prepared the SolidWorks imagery; [PERSON] developed the Lumerical simulations for evanescent-coupling.  
The authors declare no conflict of interest.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Research Contribution (Non-3 GPP): MM Strategies in 5G Network  
The key dimension of current industrial and academic research is to offer such techniques that can be utilized for tackling user mobility issues, continuity of service, and on-demand-based service provision [95; 96]. These techniques can be broadly classified into the following levels: (i) network contribution at the core, (ii) access, and (iii) extreme edge network level.
###### Abstract  
Spike sorting, an indispensable process in the analysis of neural biosignals, aims to segregate individual action potentials from mixed recordings. This study delves into a comprehensive investigation of diverse unsupervised classification algorithms, some of which, to the best of our knowledge, have not previously been used for spike sorting. The methods encompass Principal Component Analysis (PCA), K-means, Self-Organizing Maps (SOMs), and hierarchical clustering. The research draws insights from both macaque monkey and human pancreatic signals, providing a holistic evaluation across species. Our research has focused on the utilization of the aforementioned methods for the sorting of 327 detected spikes within an in vivo signal of a macaque monkey, as well as 386 detected spikes within an in vitro signal of a human pancreas. This classification process was carried out by extracting statistical features from these spikes. We initiated our analysis with K-means, employing both unmodified and normalized versions of the features. To enhance the performance of this algorithm, we also employed Principal Component Analysis (PCA) to reduce the dimensionality of the data, thereby leading to more distinct groupings as identified by the K-means algorithm. Furthermore, two additional techniques, namely hierarchical clustering and Self-Organizing Maps, have also undergone exploration and have demonstrated favorable outcomes for both signal types. Across all scenarios, a consistent observation emerged: the identification of six distinctive groups of spikes, each characterized by distinct shapes, within both signal sets. In this regard, we meticulously present and thoroughly analyze the experimental outcomes yielded by each of the employed algorithms. This comprehensive presentation and discussion encapsulate the nuances, patterns, and insights uncovered by these algorithms across our data. By delving into the specifics of these results, we aim to provide a nuanced understanding of the efficacy and performance of each algorithm in the context of spike sorting.  
biosignals; unsupervised classification; spike sorting; PCA; K-means; self-organizing maps; hierarchical clustering +
Footnote †: journal: _arxiv  
Article
## 3 Data Fusion and Multimodal Integration
### Data Fusion Techniques  
#### 3.1.1 Understanding Data Fusion in Multimodal Learning  
Data fusion involves combining information from multiple sources to generate a more accurate and complete understanding of a system. In fault diagnosis, this often means aggregating inputs from sensors that monitor different physical phenomena such as temperature, vibration, pressure, and acoustics. By merging these diverse signals, one can create a more holistic view of system health [33].  
Multimodal learning enables models to process and learn from different types of data, such as visual, auditory, or sensor-based inputs. Data fusion is a foundational aspect of this approach, facilitating the integration of these varied inputs into a single learning framework. This not only allows the model to uncover relationships between modalities but also leads to more accurate predictions [34].  
Formally, the fusion process can be expressed as a function that combines data from \(m\) distinct modalities:  
\[\mathbf{D}_{\text{fusion}}=f(\mathbf{D}_{1},\mathbf{D}_{2},\dots,\mathbf{D}_{ m}) \tag{6}\]  
Here, \(f(\cdot)\) represents the fusion function, a weighted combination, statistical method, or a learned function optimized through machine learning.
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### AdaBoost Classifier  
The AdaBoost classifier, known as Adaptive Boosting [82], is conceptually similar to the RF algorithm in the way that it combines many weak learners (such as DT classifiers) to develop a strong predictor. Contrary to the RF method, where DTs are generated independently/parallelly, AdaBoost constructs a forest of DTs sequentially based on the classification skills of previous DTs. In fact, DT learners within the AdaBoost classifier are called \"stumps\", simply because they have one node and two leaves. The stumps alone may feature predictive skills only slightly better than a random guess, but a group of stumps, with each one progressively learning from the misclassified cases of its predecessor, \"boosts\" the final classification accuracy. In practice, initially, AdaBoost assigns equal weights to all training samples and selects the first decision stump with the highest classification skill expressed by a logistic loss function. Further, the weights of the misclassified samples from the first stump are increased so the second stump is more focused on those samples. This procedure ends whenever all of the samples are correctly classified or the maximum number of stumps has been reached. Ultimately, for each sample, the predictive skills of positive stumps (with a \"yes\" label) and negative stumps (with a \"no\" label) are summed up separately, and the final label is assigned according to a larger sum. AdaBoost is suited for imbalanced datasets as it is insensitive to overfitting but underperforms for noisy datasets [41].
## 3 Research Framework of Internet of Emergency Services
### Coordination and Communication  
3.4.1. Importance of Coordination and Communication in Emergency Management and Response  
Coordination and communication are critical components of emergency management and response. Effective coordination and communication among emergency responders, organizations, and the public can significantly enhance emergency preparedness, response, and recovery efforts [41]. During an emergency, multiple agencies and organizations are involved in the response, including emergency management agencies, law enforcement, fire departments, medical services, and others. Each of these entities may have different responsibilities, protocols, and communication systems, which can lead to confusion and miscommunication if not properly coordinated. Proper coordination and communication can help to ensure that emergency responders are aware of the situation and can respond quickly and effectively. It also helps to prevent duplication of efforts, ensure that resources are allocated appropriately, and reduce the risk of errors or misinterpretation of information. Effective communication with the public is also crucial during emergency situations. The public needs to be informed of the situation, potential risks, and the appropriate actions to take to stay safe. It is important to provide clear, accurate, and timely information through multiple channels, such as social media, emergency alerts, and public announcements. Coordination and communication are essential for effective emergency management and response. This requires a coordinated effort from all involved parties, including emergency responders, organizations, and the public.
# Monitoring Forest Phenology in a Changing World
## 1 Introduction  
Phenology is the study of the timing of recurrent biological events (or phenophases), the causes of these timings in regard to the biotic and abiotic drivers, and the connection among phases of the same or different species [1]. Here, phenology is defined by both describing observable events and also by describing the links and cascading indirect effects that phenological events can have on an ecosystem [2]. This obvious interrelation of phenology with so many aspects of an ecosystem is why it was hardly considered a scientific discipline until the 1900s [3], despite the recording of phenological events dating back thousands of years [4] (Figure 1). Nevertheless, the connection of phenology with multiple facets of the ecosystem makes monitoring it critical for developing our understanding of ecological processes and for managing resources in the face of climate and habitat change [5, 6]. As such, there has been a rapid acceleration in the study of phenology and the associated development of new methods and technologies to aid this endeavour (Figure 1). The past two decades in particular have seen increased effort to build interdisciplinary phenological research frameworks that utilise developments in technology to monitor phenology and its links with ecosystem function across species, space and time [4, 6, 7].
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 2 Problem Definition
### Sensor Model  
The surveillance region is assumed to be a ground plane as depicted in Figure 2. Minimizing the effect of UAV's state, the detection range of gimbaled sensor can be adjusted by a universal joint, which is a three degree rotation system. The maximum angle between two rotation axes of the universal joint is denoted as \(\theta\), and the posture change of UAV in flight is not considered. In Figure 2, \(\mu_{i}\) is the projection of UAV \(i\) on ground plane, and \(h\) is the altitude of UAV. Accordingly, \(R_{s}=h\tan\theta\) is the sensing radius of UAV, and the sensing regions can be expressed as \(z=\{\mathbf{x}|\|\mathbf{x}-\mu_{i}\|\leq R_{s}\}\), which are depicted as the shaded parts in Figure 2. It is assumed that each UAV independently takes measurements over the grids completely within its sensing region, and a grid is assumed to be detected if it is wholly within the sensing region \(z\). As the gray ones, the grids detected with UAV motions are depicted in Figure 3, and the red circles represent the sensing range of UAV.
# Novel Highly Dispersed Additive for Proton-Conducting Composites
###### Abstract  
The proton conductivity and structural properties of (1-\(x\))CsH\({}_{2}\)PO\({}_{4}\)-\(x\)ZnSnO\({}_{3}\) composites with compositions of \(x\) = 0.2-0.8 were studied. Zinc stannate ZnSnO\({}_{3}\) was prepared by the thermal decomposition of zinc hydroxostannate ZnSn(OH)\({}_{6}\), which was synthesized by hydrolytic codeposition. To optimize the microstructure of ZnSnO\({}_{3}\), thermal decomposition products of ZnSn(OH)\({}_{6}\) were characterized by thermal analysis and X-ray diffraction, Fourier transform infrared spectroscopy, low-temperature nitrogen adsorption, and electron microscopy. The study reveals that the thermolysis of ZnSn(OH)\({}_{6}\) at temperatures of 300-520 \({}^{\circ}\)C formed an X-ray amorphous zinc stannate with a high surface area of 85 m\({}^{2}\)/g possessing increased water retention, which was used as a matrix for the formation of the composite electrolytes CsH\({}_{2}\)PO\({}_{4}\)-ZnSnO\({}_{3}\). The CsH\({}_{2}\)PO\({}_{4}\) crystal structure remained in the composite systems, but dispersion and partial salt amorphization were observed due to the interface interaction with the ZnSnO\({}_{3}\) matrix. It was shown that the proton conductivity of composites in the low-temperature region increased up to 2.5 orders of magnitude, went through a smooth maximum at \(x\) = 0.2, and then decreased due to the percolation effect. The measurement of the proton conductivity of the ZnSnO\({}_{3}\)-CsH\({}_{2}\)PO\({}_{4}\) composites revealed that zinc stannate can be used as a heterogeneous additive in other composite solid electrolytes. Therefore, such materials can be applied in hydrogen production membrane reactors.  
thermolysis of ZnSn(OH)\({}_{6}\); zinc stannate; composites; composite solid electrolytes; cesium dihydrogen phosphate +
Footnote †: journal: Applied Sciences
### Wavelet Transforms  
A study carried out by [PERSON] et al. as well as that of [PERSON] and [PERSON] shows that, instead of the Fourier transform, wavelet transforms can be implemented in protection systems [23, 24]. Since empirical mode decomposition (EMD) is not used in wavelet transform, mode mixing cannot occur. Where the Fourier transform requires all functions to be periodical, the wavelet transform requires a mother wavelet for all calculations to be based on. The selection of this mother wavelet defines the application of the wavelet trans  
Figure 1: Simple multiline system.  
Figure 2: Classification of signal processing methods.  
form [11]. A wavelet from the Daubichies family has been found to be the best suited for power systems transient analysis. This family of wavelet transforms allow for the analysis of fast decaying, short-duration transient signals [25]. A continuous wavelet transform (_CWT_) of a signal \(x(t)\) with mother wavelet \(m(t)\) is defined by the equation:  
\[CWT(g,h)=\frac{1}{\sqrt{g}}\int_{-\infty}^{\infty}x(t)m(\frac{t-h}{g})dt \tag{1}\]  
where \(g\) is the scale factor, \(h\) is the translation factor, and \(t\) is time; all three continuous.  
In this application of a wavelet transform, it is not possible to use a _CWT_, as the faulted signal needs to be detected and resolved as it occurs and cannot be sampled over all time. To solve this problem, a discrete wavelet transform (_DWT_) is needed. This means that \(g\), \(h\), and \(t\) in the previous equation need to be transformed to a discrete form. By implementing these changes to the _CWT_ formula, a _DWT_ can be represented by:  
\[DWT(p,q)=\frac{1}{\sqrt{g_{o}}p}\sum_{k}x(k)m(\frac{k-qh_{o}g_{o}p}{g_{o}}) \tag{2}\]  
In Equation (2) the parameters \(g\) and \(h\) of Equation (1) were changed to be the functions of integers \(p\) and \(q\), respectively. \(k\) is a variable integer and is equal to the sample number of an input signal [26; 27; 28; 29; 30; 31; 32; 33].  
One of the implementations of a _DWT_ is known as multiresolution analysis. In Figure 3, the process of multiresolution analysis is shown. Firstly, the signal \(x(n)\) is passed through both high and low pass filters. The outputs from each filter are then decimated by two, this gives the detailed (D) and approximate (A) coefficients at that level (L). The approximation obtained at level 1 is then used as the input signal for level 2, where the process is repeated. This carries on until the desired level is reached [11].  
Furthermore, in Figure 3, if the sampling rate is H, the data obtained at D1 is between H/4 and H/2 of the signal frequency. D2 will obtain the information between H/8 and H/4, where D3 will obtain the information between H/16 and H/8. The remaining information between H/16 and 0 will be in A3. By decomposing a signal using this method, it is easier to process information found in different frequency ranges while still keeping the data in the same period. A sampling rate of 200 kHz is used as it offers the best resolution against processing power.
## 2 Establishment of Wedge Sliding Body Model
### Discrimination Method of Tunnel Face Stability  
To reasonably determine the stability of tunnel face, this paper introduces the critical stability coefficient (\([K_{min}]\)) [35], and then comprehensively and quantitatively evaluates the reinforcement effect of the pipe roofing support, tunnel face bolting support, tunnel face grouting support and reserved core soil. First, the initial stability of the tunnel face and whether the excavated tunnel face needs to be reinforced are judged. The discrimination method of tunnel face stability is as follows. When \(P\leq 0\) and \(K_{0}\) > \([K_{min}]\), it indicates that the initial tunnel face is in a stable state, and thus support is not required. When \(\left\{P\leq 0\,\ K_{0}\leq[K_{min}]\ \right.\), it indicates that the initial tunnel face is in an unstable state or its safety reserve is insufficient, so support is needed. The stability coefficient (\(K\)) in Section 2.2 is used to determine the tunnel face stability after reinforcement. The discrimination steps are: 1 when \(K\leq[K_{min}]\), it indicates that the supporting measures cannot meet the stability requirements, and the supporting strength needs to be increased until the stability coefficient meets \(K\) > \([K_{min}]\). 2 when \(K\) > \([K_{min}]\), it shows that the supporting measures can ensure the stability of the tunnel face and have a certain safety reserve.  
The critical stability coefficient of a tunnel face must be determined. Existing studies [35] show that the sliding surface of rock slope or soil rock combination slope developed with an inclined structure surface has an approximately regular plane, and its stability is usually analyzed by rigid body limit balance condition. Based on the wedge sliding body model established in this paper, the failure mode of tunnel face in shallow buried tunnel is the overall collapse failure of the wedge sliding body and the slip surface is also manifested as the plane, which has the same collapse mode as the rock slope and earth rock slope combined. Therefore, the instability judgment of the tunnel face in shallow buried tunnel can refer to the judgment standard of rock slope or soil-rock slope. The wedge sliding body in front of the tunnel face is regarded as a secondary temporary slope combined with the existing specifications [36; 37], and the critical stability coefficient ([\(K_{min}\)]) can be determined as 1.20.
## 5 Experimental Results
### Comparison with The-State-Of-The-Art  
The results were compared against the signature presented by [PERSON] et al. (2019) for OS prediction in NSCLS, as introduced in Section 4. Further, to be one of the most cited works in the state-of-the-art, such a radiomic signature was also used by [PERSON] et al. (2017) and [PERSON] et al. (2016).  
Table 6 reports the performance computed with [PERSON] et al.'s signature on our dataset: the largest AUC value was obtained considering the CTV with Random Forest as classifier, and it is equal to 76.92%. Using the same VOI our signature gets an AUC equal to 82.78%, which suggests that the inclusion of the 3D LBP descriptors boosts the discrimination power and leads to larger performance. Despite the fact that the best results of Tables 3 and 6 are obtained with different classifiers, comparing the corresponding columns of the two tables further validates the previous assertion. Also note that the best combination in Table 6 is based on a Random Forest classifier with the CTV, which is also one of two best configurations in our experiments: this confirms our results that the CTV is the most informative volume of interest.
###### Abstract  
Properly determining reservoir wettability is crucial for enhancing oil recovery and optimizing production strategies; this is particularly the case for tight oil reservoirs. The lower-fourth member of the Shahejie Formation (Es4x) in the Bonan Sag of the Bohai Bay Basin is a typical tight sandstone play, which is characterized by a low permeability and poor fluid mobility and quite variable daily production. Despite efforts in reservoir stimulation, the incremental production remains negligible. A detailed investigation of the reservoir wettability was carried out using four representative tight sandstone samples from Es4x to better understand the production behavior of the tight oil reservoir. We employed a suite of analytical methods, including Environmental Scanning Electron Microscopy, contact angle measurement, spontaneous imbibition, and Nuclear Magnetic Resonance, to comprehensively evaluate the wettability characteristics of the reservoir at different scales. Two samples, C1 and C2, exhibit oil-wet characteristics at both pore and macro scales, with Sample C1 showing weak oil-wet behavior and Sample C2 demonstrating strong oil-wet behavior. In contrast, the other samples, C3 and C4, display strong water-wet characteristics across different scales. The pore size threshold between water-wet and oil-wet conditions for samples C1 and C2 is 0.1 \(\upmu\)m, while that for samples C3 and C4 is 1 \(\upmu\)m. Grain-coating chlorine and grain-coating illite are the primary clay minerals contributing to the oil-wet pore walls of the reservoir, whereas dispersed sheet-like chlorite and rosette chlorite predominantly exhibit water-wet characteristics. The tight oil reservoir in Es4x exhibits pronounced wettability heterogeneities, with distinct regions displaying either oil-wet or water-wet characteristics, significantly impacting the mobility and producibility of the tight oil in this reservoir interval.  
tight sandstone; wettability evaluation; Shahejie formation; tight oil mobility; Bonan Sag