esa-sceva/satcom-chunk-collection huggingface dataset:
# A Biologically Inspired Self-Organizing Underwater Sensor Network
## 7 Conclusions  
In this paper, we propose a method to coordinate message transmission in a large-scale wireless underwater sensor network. The coordinator consists of a slot distributor, which provides each node with the slots it should occupy, and a forwarding guide, which indicates the next node to relay the message packet. As a result, with the communication coordinator, the communication process can be coordinated in a self-organized way, and it fits for networks consisting of a large number of randomly deployed nodes. The network can converge to the state without communication collision, and the coordinate information will not use many communication resources, which fits for underwater acoustic communication. The message can be transmitted to the base station following the shortest path defined with expected waits, outperforming the Aloha protocol.  
The main shortcoming of the current method is that we are using a fixed-length TDMA framework. Thus, the frame length will grow with the addition of nodes to the network. This problem can be solved with a dynamic-length TDMA framework, and that is what we are working on at present.  
Figure 14: Time comparison to send a message packet to the base station with the proposed method and the classic Aloha protocol using different speaking probabilities \(p\).  
**Author Contributions:** Conceptualization, G.L. and Y.Z. ([PERSON]); methodology, G.L.; software, Y.Z. ([PERSON]) and Z.W.; validation, C.C. and Y.W.; writing--original draft preparation, G.L.; writing--review and editing, C.C.; funding acquisition, G.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Fund of Science and Technology Department of Liaoning Province and State Key Laboratory of Robotics, China (2021-KF-22-12), National Natural Science and Foundation of China (NSFC 62003206), and Shenzhen Science and Technology Program (JCYJ20220818101607015).  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 7 Evaluation of Results
### Performance Analysis
#### 7.2.4 Trade-Offs and Long-Term Impacts
For teams with limited exposure to functional programming, this shift can pose a difficulty for the initial adaption, as the transition may affect short-term productivity. However, reactive programming can offer considerable benefits by promoting a deeper understanding of asynchronous patterns, reducing errors associated with thread management, and improving efficiency. Organizations must weigh the benefits of reactive programming against the learning curve and the costs of training and onboarding [69]. Notably, the reactive ecosystem has grown substantially, with multiple materials, courses, and tools that aim to support training and development. Many solutions and frameworks are actively researched and maintained to support the transition towards reactive systems, even tools that can perform automated refactoring of asynchronous Java code into reactive counterparts, which can provide a helpful bridge by reducing some of the manual efforts and enabling quick prototyping, while allowing the teams to focus on understanding the reactive principles, easing the transition process and improving long-term productivity [70]. The reactive paradigm is particularly beneficial in systems with scalability and high concurrency requirements, such as applications that demand real-time responsiveness or high-throughput processing, as the non-blocking architecture can optimize resource utilization. However, the long-term impact of maintaining and updating depends on how well the initial solution was developed and whether it adheres to best practices. Poorly implemented reactive systems can experience bugs due to complex operators or chainings, which can make the system potentially more challenging to refactor or extend. Asynchronous systems, while more straightforward in general, may require repeated architectural adjustments to scale effectively, which can delay code updates and progress long-term. Reactive programming holds significant promise for scalability, provided the development team has the appropriate expertise. Balancing the benefits and challenges of each paradigm requires careful consideration of team proficiency, project requirements, and long-term goals.
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 4 Discussion
Based on the International Classification of Functioning, Disability, and Health [81], 22 of the outcome measures in the included studies are considered measures of body function and structure (BBT (Box and Block Test), Br-Stage (Brunnstorm stage), CCI (Co-Contraction Index), EMG (Electromyograph), Goniometry, GP/GS (Grip Pressure/Grip Strength), HMR (Hmax/Mmax Ratio), ICF (Intracortical Fascilitation), MAS (Modified Ashworth Scale), MEP (Motor-Evoked Potential), MI (Motricity Index), MMAV (Motor Map Area and Volume), MMT (Manual Muscle Test), MOT (Muscle Onset Time), MR (Modulation Ratio), PS (Postural Sway), SICF (Short Interval Intracortical Facilitation),SICI (Short Interval Intracortical Inhibition), STEF (Simple Test for Evaluating hand Function), WMT (Weinstein Monofilament Test), %BW shift (% Body Weight Shift), %MVC (% Maximal Voluntary Contraction)); nine measured activity (BI (Barthel Index), EPS (End Point Stability), FAS (Functional Ability Scale), FIM (Functional Independence Measure), YJHFT (Jebsen-Taylor Hand Function Test), KE (Kinematic Evaluation), mRS (modified Rankin Scale), QuickDash (The Disabilities of the Arm, Shoulder and Hand Score), WMT (Wolf Motor Function Test)); three assessed the health condition (CSP (Cortical Silent Period), FMS (Fugl-Meyer Scale), NIHSS (National Institutes of Health Stroke Scale)); and three measured personal factors (FIM (Functional Independence Measure), VAS (Visual Analog Scale), VNRS (Verbal Numerical Rating Scale)). Thus, the focus of the included studies was on effect of FMV on health and function, with little or no focus on participation and quality of life. This could be partly due to the overall short durations of those studies, which could make investigating changes in participation and quality of life difficult. This finding could also be attributed to the infancy of FMV therapy for stroke rehabilitation and the lack of accessible and sustainable FMV devices for researchers, clinicians, and individuals with stroke. This review was limited to articles published in English. Regarding the locations where the research was conducted, only one was conducted in the United States; one was in Germany; one in France; three in Japan; four in Korea; and twelve in Italy. This can lead to bias, as studies published in languages other than English were not included. In addition, it is known that significant results have greater likelihood of publication than do studies that do not have significant results. Effect sizes were not reported for most outcome measures because of the inconsistency of outcome measures and insufficient data. We did not conduct meta-analysis because the focus of this review was vibration technology and protocol, because of the heterogeneity of treatment protocols, dosages, and assessment, and because of the inability to contact the authors of many articles.
## 3 Results  
For a major flood, such as in 1998, the operating water level of the gate could be determined based on the safe water level, which is a designed water level for flood protection [23] at each hydrological station (see Table 2). We take gate #8, for example, to explain how the IFCS works to mitigate flood disasters. In the current study, the gate will be opened when the water level is greater than the safe water level (i.e., 32.50 m at Chenglingji station), until the water level reaches its peak value. For the 1998 type flood, the flooding process can be divided into three main flood waves, i.e., first flood wave (3-11 July), second flood wave (22-30 July), and the third flood wave (16-25 August) (see Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **Safe Water Level** & **Station** & **Safe Water Level** & **Station** & **Safe Water Level** \\ \hline Nanzui & 34.20 & Xiangyin & 33.59 & Luijiao & 33.03 \\ \hline Xiaohezui & 33.85 & Yintian & 33.10 & Chenglingji & 32.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The safe water level at each hydrological station (unit: m).  
Figure 5: The application of the IFCS in Dongting Lake. (**a**) The layout of the IFCS; (**b**) The water body in the dry season of Dongting Lake (December 2006).  
Figure 6: The flooding process in Dongting Lake at selected stations in 1998.
## 3 Proposed Methodology
### Model Architecture  
We constructed an Arabic BERT model for text classification from a pre-trained BERT model by fine-tuning the 109-million parameters with the labeled data. The network architecture of the constructed Arabic BERT model (AR-ABERT4 TWC) is shown in Figure 1. As presented in Algorithm 2, the input vector, which is a combination of token embedding, segment embedding, and positional embedding, has a shape of \((n,768)\) where \(n\) is the size of the input sequence length, and the model can take an input sequence up to 512 sizes. The custom BERT model consists of 12 encoder layers with 12 self-attention heads, which are bi-directional in nature, and 12 hidden layers. Each self-attention head in the encoders computes the key, value, and query for each input token in a sequence, which is then utilized to produce a weighted representation of the sequence. All the outputs obtained from self-attention heads in similar layers are merged and routed through a fully linkedlayer. Every layer is preceded by layer normalization and is linked with a skip connection. The final output is taken through the [CLS] token vector, and it is passed through the linear layer and finally through the \(SOFT_{Max}\) layer for classification. The final output of the classification model is to merge the output of the normalization process in both linear layer and \(SOFT_{Max}\) classification layer.  
```
1 Let \(TE\)\(\leftarrow\) Token Embedding
2 Let \(SE\)\(\leftarrow\) Segment Embedding
3 Let \(PE\)\(\leftarrow\) Positional Embedding
4 Input: BERT = \(TE||SE||PE\)
5 Let \(n\)\(\leftarrow\) input sequence length size
6 Let \(d\)\(\leftarrow\) embedding dimension
7 Calculate \(Input_{vector}\left(n,d\right)\) such that \(max(d)=512\) byte
8\(\forall\ x_{i}\in x\
i x_{i}=\{x_{1},x_{2},\ldots,x_{n}\}\in TE\)
9\(\forall\ s_{i}\in s\exists\ s_{i}=\{s_{1},s_{2},\ldots,s_{n}\}\in SE\)
10\(\forall\ p_{i}\in p\
i p_{i}=\{p_{1},p_{2},\ldots,p_{n}\}\in PE\)
11 Input: \(Input_{vector}=\{x,s_{i},p\}\)
12 Apply Encoder/Transformer Layers
13 SET Encoder \(layer(L,H,D)=(12,12,12)\) such that Encoder \(Layer(L)=12\), \(Self-attention\ Layer\ (H)=12,Hidden Layer\ ((D)=12\)
14\(\forall\ Input_{token}\ Seq\ (Input_{vector})\ Calculate Output\ O_{i}\ (H)=\{key,value,query\}\)
15 SET \(Multi\ Head\ Self-attention\ (input)\) \(=Attention\ \Big{(}Context_{before}+Context_{after}+\ Seq\ (Input_{vector})\Big{)}\)
16 SET \(Attention\ (x)=\{key,value,query\}\ \forall\ Input_{token}\ (x)\) such that \(key=f(x),value=g(x),query=h(x)\)
17 Merge Output \(\sum_{i=1}^{n}{\rm O}_{i}(H)\)
18 SET Output = Merge \((\sum_{i=1}^{n}{\rm O}_{i}\ (H)+fully\ linked\ layer)\)
19 Apply Layer Normalization for Every Layer
20 Link Layer Normalization with Skip Connection
21\(Output_{normalization}=Layer\ Normalization\ (output)+Skip\ Connection\ (output)\)
22 Pass Final Output through Linear Layer
23 Pass Final Output through \(SOFT_{Max}\) Layer
24\(FinalOutput=\begin{array}{l}LinearLayer(Output_{normalization})\\ +SOFT_{Max}(Output_{normalization})\end{array}\)
```  
**Algorithm 2****ARABERT4 TWC**
###### Abstract  
The mechanical properties of frozen fissured rock masses are crucial considerations for engineering in frozen earth. However, there has been little research on the mechanical properties of frozen fissured sandstone, including its strength, deformation, and geometric parameters. In this study, sandstone samples with three open en echelon fissures were observed using high-speed photography and acoustic emissions during uniaxial compression tests. The aim was to investigate sandstone's strength, deformability, and failure process in order to elucidate the effects of freezing on its mechanical properties. In the frozen-saturated and dried states, the uniaxial compression strength (UCS) initially decreases and then increases with an increase in fissure inclination angle. Conversely, the UCS of samples in the saturated state continuously increases. The UCS follows a decreasing trend, as follows: frozen-saturated state > dried state > saturated state. The initial crack angle decreases as the fissure inclination increases in all states, irrespective of temperature and moisture conditions. However, the initial crack stress and time show an increasing trend. The uniaxial compression strength (UCS) of frozen fissured sandstone is influenced by four mechanisms: (1) ice provides support to the rock under compression, (2) ice fills microcracks, (3) unfrozen water films act as a cementing agent under tension or shearing loads, and (4) frost damage leads to softening of the rock.  
uniaxial compression; frozen fissured sandstone; crack coalescence; crack initiation +
Footnote †: journal: Applied Sciences
## Material and Methods
### Reagents and Antibodies  
The drugs Z-Phe-Tyr(tBu)-diazomethylkletone (FYdmk) (219427, Calbiochem, San Diego, CA, USA), U18666A (662015, Calbiochem, San Diego, CA, USA) and tetrandrine (sc-201492, Santa Cruz Biotechnology, Santa Cruz, CA, USA) were dissolved in DMSO according to supplier's instructions. Working solutions were prepared by dilution of the stock solution with culture medium. Doxycycline hydrate (DOX) (D9891, Sigma-Aldrich, San Luis, MS, USA) for cell-cell fusion reporter expression activation was prepared as 50 mg/mL stock solution in DMSO and stored at \(-80\)\({}^{\circ}\)C. The stock solution was diluted in culture media supplemented with Tet-free approved FBS (Clontech, Takara Bio, Mountain View, CA, USA) to 500 ng/mL. Recombinant conjugated protein EGF-AlexaFluor555 for EGF intravesicular accumulation analysis was purchased from Invitrogen(tm) (E35350) (Carlsbad, CA, USA). Filipin III (F4767) for unesterified cholesterol detection was obtained from Sigma-Aldrich (San Luis, MS, USA).  
Mouse monoclonal allophycocyanin(APC)-conjugated antibodies (mAbs) against human surface entry factors Axl (FAB154A), human integrin \(\beta\)1 (FAB17783A), Mer (FAB8912A), or DC-SIGN (FAB161A) and their respective isotype controls, IgG\({}_{1}\) (IC002A) and IgG\({}_{2B}\) (IC0041A) as well as AlexaFluor488-conjugated mAb against human integrin \(\alpha\)V (FAB1219G-025) and its IgG\({}_{1}\) isotype control (IC002G) were purchased from R&D systems (Minneapolis, MN, USA). Mouse APC mAb against human TIM-1 (353905) or APC-mIgG\({}_{1}\)\(\kappa\) isotype control (400121) were obtained from Bidegend Inc. (San Diego, CA, USA). For immunoblot detection of intracellular factors rabbit polyclonal antibodies (pAbs) directed against human procathepsin L + cathepsin L ab (ab200738) or mouse antihuman Niemann Pick-C1 mAb (ab55706, ab134113) were purchased from Abcam (Cambridge, UK). Antihuman procathepsin B and cathepsin B rabbit mAb (31718) was acquired from Cell Signaling Technology Inc. (Danvers, MA, USA). Rabbit pAbs directed against human two pore channel segment 1 (TPC1) (LS-C110014) was purchased from LifeSpan Bioscience Inc. (Seattle, WA, USA) or against human two pore channel segment 2 (TPC2) (ACC-072) from Alomone labs (Jerusalem, Israel). Mouse mAb against human \(\beta\)-tubulin (T7816) or GAPDH (G9545) were purchased from Sigma-Aldrich (San Luis, MS, USA). Horseradish peroxidase-coupled (HRP) goat pAbs directed against mouse (A4416) or against rabbit (A6154) IgGs were purchased from Sigma-Aldrich (San Luis, MS, USA).  
Mouse pAb conjugated with Fluorescein isothiyanate (FITC) and directed against human IgG, Fc\({}_{Y}\) fragment specific ab (209-095-098) for fusion protein cell binding assays was purchased from Jackson ImmunoResearch Europe Ltd. (Newmarket, UK).
## 4 Cognitive Disengagement Syndrome (CDS) and Twin Studies
### Twin Studies on CDS  
Up to now, only a few studies have employed twin methodologies to investigate the heritability of CDS, highlighting the relevance of non-shared environmental factors in the development of this syndrome [12,67]. However, none of these studies specifically aimed to focus exclusively on the etiology of CDS. [PERSON] et al. [67] examined the etiological relationships among ADHD dimensions, including Intententive Problems (INP), Hyperactivity-Impulsivity Problems (HIP), and CDS. These findings indicated moderate to substantial intercorrelations among these dimensions, with shared genetic influences ranging from 65% to 83% and shared environmental contributions between 29% and 44%. Despite this overlap, this study highlighted that CDS seems to represent a distinct construct, as it is predominantly influenced by unique non-shared environmental factors (72%). This evidence appears to be further supported. Specifically, [PERSON] et al. [68] conducted a longitudinal twin study over a 10-year period to assess the (in)stability of these two constructs. The findings indicated that although CDS and ADHD exhibit a high and stable correlation over time, ADHD-HYP symptoms tend to decrease, whereas ADHD-IN symptoms demonstrate greater stability. Conversely, CDS symptoms showed an increasing trend across different time points. To date, the only twin study that has assessed the etiology of CDS independently from ADHD is the one by [PERSON] et al. [12]. This study aimed at investigating the genetic and environmental factors underlying the comorbidity between CDS, Somatic Anxiety, and Generalized Anxiety Disorder. Similarly to [PERSON] et al. [67], this study highlights that CDS seems to be predominantly influenced by unique non-shared environmental factors (67%), while the variance explained by unique genetic factors seems to be moderate (29%).
## 4 Stability Analysis  
This section presents the stability analysis of the SMC scheme. Let us use \(\lambda_{M}(A),\ \lambda_{m}(A)\) for the largest and smallest eigenvalue of a matrix \(A\). We denote the Euclidean norm for an \(n\times 1\) vector \(x\) by \(\|x\|=\sqrt{x^{T}x}\). The inertia matrix is symmetric, positive definite, and bounded by \(0<\lambda_{m}(H)\leq\|H(r)\|\leq\lambda_{M}(H)\). The matrix \(\dot{H}(r)-2\mathcal{C}\bigl{(}r,\dot{r}\bigr{)}\) is skew-symmetric.
# Effect of Laser Treatment on Intrinsic Mechanical Stresses in Titanium and Some of Its Alloys
## 4 Conclusions  
The laser pulse treatment of the Ti, Ti15 Mo, and Ti7 Nb6 Al alloys resulted in changes in different material and mechanical surface characteristics, such as surface topography, roughness, chemical composition, hardness and Young's modulus, and level of intrinsic stresses. The effects were dependent, to a varying extent, on the alloy kind.  
The impacts of the laser beam on the surfaces were observed as an appearance of pores and short cracks within the thin subsurface layer, presumably caused by the high lateral tensile stresses present in the brittle surface-remelted layer.  
The hardness of the remelted alloys ranged between 66 and 126 MPa, being the lowest on single-phase titanium and higher for two alloys.  
The laser surface processing increased the roughness as a well-known consequence of melting and high-speed crystallization of the surface layer. The effect of the alloy type was negligible because of similar energy absorption levels by all Ti-based materials.  
The laser treatment made the surface layer tougher as proved by the hardness increasing by over 30%, and Young's modulus decreasing by over 15%. The effects resulted from the formation of a small-grain recrystallization zone within the remelted layer. The chemical composition significantly influenced the hardness and Young's modulus.  
All materials possessed residual tensile stresses, in particular, the Ti15 Mo alloy. This effect was specific to the measurement depth, the number of laser pulses, and the laser power.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13106276/s1](https://www.mdpi.com/article/10.3390/app13106276/s1)]([https://www.mdpi.com/article/10.3390/app13106276/s1](https://www.mdpi.com/article/10.3390/app13106276/s1)), Figure S1: Three-dimensional hardness maps of the BM and annealed BM of Ti7 Nb6 Al samples, Figure S2: Three-dimensional hardness maps of the BM and annealed BM of Ti15 Mo samples, Figure S3: Three-dimensional hardness maps of the LT and annealed LT of Ti7 Nb6 Al samples, Figure S4: Three-dimensional hardness maps of the LT and annealed LT of Ti15 Mo samples.  
Conceptualization, M.J.; methodology, M.J. and M.B.; software, M.J., M.B. and D.B.K.; validation, M.J. and A.Z.; formal analysis, M.J., M.B. and A.Z.; investigation, M.J., M.B. and A.Z.; resources, M.J. and M.B.; data curation, M.J.; writing--original draft preparation, M.J., M.B. and A.Z.; writing--review and editing,  
**Acknowledgments:** The authors would like to thank the students [PERSON] and [PERSON] and the employees of Gdansk University of Technology, [PERSON], [PERSON] and [PERSON] for their technical assistance in some of the tests.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 2 The Proposed Deep CNN Architecture
### The \(1\times 1\) Kernels  
The \(1\times 1\) convolution kernel was proposed firstly by [PERSON] et al. [25], which has two functions. Firstly, by adding the \(1\times 1\) kernels in front of other kernels in the kernel module, the computational cost can be reduced. The total number of multipliers is the number of multipliers that need to be calculated for each of output values times the number of output values. As illustrated in Figure 4, two techniques are given for inputting \(n_{1}\times n_{2}\times f_{1}\) and outputting \(n_{1}\times n_{2}\times m_{1}\), the first method is to use only \(s\times 1\) convolution kernels, and an alternative architecture is to use the \(1\times 1\) convolution kernels and then the \(s\times 1\) convolution kernels.  
Figure 4: Convolution operation: (**a**) Based on \(\mathrm{HI}_{2}\) (**b**) using \(1\times 1\) convolution.  
Figure 3: The kernel module.  
The total number of multipliers for the first technique \(tm_{1}\) and the second technique \(tm_{2}\) can be calculated by  
\[tm_{1}=n_{1}\times n_{2}\times m_{1}\times s\times f_{1}, \tag{2}\]  
\[tm_{2}=n_{1}\times n_{2}\times m_{2}\times f_{1}+n_{1}\times n_{2}\times m_{1} \times s\times m_{2},\]  
where \(m_{2}\) represents the number of feature maps after the \(1\times 1\) kernels.  
If \(tm_{1}>tm_{2}\), then  
\[(m_{1}/m_{2}-m_{1}/f_{1})>1/s, \tag{3}\]  
hence, the computational load can be reduced.  
The second function of the \(1\times 1\) kernel is to change the number of feature maps and implement linear combination of multiple feature maps. The input data is processed by the \(1\times 1\) kernel, and then the number of the feature maps is increased, providing the convolution layer with the data for feature extraction. In addition, the \(1\times 1\) convolution kernel is used for dimension reduction before the fully-connected layer, which can further enhance the correlation between the extracted features and RUL.