esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance of Built-Up Columns
###### Abstract  
This study presents an experimental investigation into the mechanical performance of built-up columns composed of four cold-formed square steel tubes under axial load. The four tubes were assembled together with several C-shaped connectors through two self-tapping screws in each junction. The influence of parameters including spacing between tubes, type of connectors and transverse diaphragm were analyzed based on the failure modes, ultimate loads, load-displacement relationships and load-strain relationships measured in the tests. Moreover, a further numerical analysis was carried out to study the effect of the number of connectors, web height of connectors and installing connectors at column ends by means of the verified finite element models. Finally, the numerical results were compared with the strengths predicted by the AISI-S100-2012 code. Results show that the performance of built-up columns can be influenced by the change in the number of connectors and ratio of web height of connectors to spacing between tubes as well as the installation of connectors at column ends. In addition, the current AISI-S100-12 specifications do not provide a good prediction of the built-up columns composed of four cold-formed square steel tubes.  
Keywords:built-up columns; cold-formed steel; buckling; experimental investigation +
Footnote †: journal: Applied Sciences
## 1 Introduction
Studies have estimated the prevalence to range from 0.5% to 4% in the general population [6; 7; 8; 9]. APAD may be more commonly encountered in certain populations, such as individuals with underlying congenital syndromes or those with a history of cervical spine trauma. The detection of APAD in orthodontic patients is typically incidental during routine imaging for orthodontic treatment planning. Studies investigating the sex distribution of APAD have yielded conflicting results, with some suggesting a slight female predominance and others finding no significant difference between genders. The prevalence of female incidence might be attributable to embryological causes, as all synchondroses of the C1 fuse slightly earlier in females, as reported by [PERSON] et al. (2022). In the posterior arch, the posterior synchondrosis fused at 5.4 years in males and at 4.4 years in females [5]. While traditional X-rays and MRI offer valuable diagnostic insights, cone-beam computed tomography (CBCT) provides superior imaging for the assessment of posterior atlas arch deficits. The high-resolution, three-dimensional capabilities of CBCT enable the precise evaluation of complex bony structures and spatial relationships within the atlas, significantly enhancing the diagnostic accuracy and clinical management of such anomalies [3, 4, 7, 10, 11]. APAD is typically incidentally identified on imaging studies performed for unrelated reasons, and its prevalence may vary across different age groups. While congenital APAD may manifest from birth, it is often discovered incidentally in older individuals during imaging studies for degenerative spinal conditions or traumatic injuries. The incidence of non-congenital APAD may escalate with age due to the cumulative effects of degenerative changes in the cervical spine [2, 6, 8].
# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Classification Techniques for Scene Text Recognition
This dataset was collected by Beijing University of Posts and Telecommunications. The reported results were produced on \(6,7,8\) and 9 subregions. The best result was computed by fixing the sub region on seven and obtained 94.80% accuracy. In another experiment, they evaluated their proposed amended SIFT feature's (Char-SIFT) performance compared to the standard SIFT technique. Their result showed good accuracy as 94.66% on linear discriminative analysis; whereas, on each feature, i.e., Gabor, gradient and Char-SIFT, they reported accuracies of 96.58%, 97.52% and 97.86%, respectively. Fully-Convolutional Networks (FCN): Another very interesting paper on text detection and recognition was presented by [75]. The fully-convolutional network classifier was proposed and trained on multi-oriented Chinese text. They considered the local and global aspect of a given text for correct text localization. They first trained the model to predict the salient features of a given text image, then the hypothesis for the text line was estimated by combining salient features and character components. The false hypothesis was removed by FCN after computing the centroid. They reported very good results on three text detection benchmarks, i.e., MSRA-TD500, ICDAR2015 and ICDAR2013. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Classifier Techniques** & **Script** & **Database** \\ \hline [PERSON] et al. [10] & Not Reported & Latin & Char74k, ICDAR2003-Characters \\ [PERSON] et al. and & SVM & Arabic, Latin & ARASTEC, Char-74k, ICDAR2003 \\ [PERSON] et al. [31; 35] & Nearest neighbour & Latin & ICDAR2011 \\ [PERSON] et al. [14] & Convolutional neural & multilingual & CVSI ’MLe2e dataset \\ [PERSON] et al. [61] & network and K-means & Latin, Kannada & Own compiled \\ [PERSON] et al. [54] & Nearest neighbour, & Latin, multilingual & ICDAR, SWT, MSRG \\ [PERSON] et al. [11] & Numal network & Latin, multilingual & ICDAR, SWT, MSRG \\ [PERSON] et al. [59] & Minimum Euclidean distance classifier & Chinese & ET19B \\ [PERSON] et al. [60] & NR & Chinese, Japanese, Korean & Datasets A, B, C, D, \\ [PERSON] et al. [75] & Fully-convolutional network & Chinese & (own compiled) \\  & & MSRA-TD500, ICDAR2015 \\  & & and ICDAR2013 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification approaches applied to cursive and non-cursive scene text.
# Seven-Year Retrospective Study on Conometric Retention for Complete Fixed Prosthesis
## 3 Results  
Data from twenty-three patients (14 females and 9 males with an average age of 67 years, with a range of 54-82) were retrieved from medical records. A total of 92 implants and Syncone abutments were evaluated. Implants and abutments features are reported in Table 1. Implants were 3.5 and 4.5 mm in diameter and 8, 9.5, and 11 mm in length. Most of the implants were 3.5 mm in diameter (70 vs. 22) and were comprised of between 8 and 9.5 mm in length (43 and 38). Most of the abutments used were straight or slightly inclined (0\({}^{\circ}\) and 7.5\({}^{\circ}\)) and with a height between 1.5 mm and 3.0 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & Implants & & & \\ \cline{2-8} Length & \multicolumn{6}{c}{Diameter (mm)} \\ \cline{2-8} (mm) & \multicolumn{2}{c}{3.5} & \multicolumn{2}{c}{4.5} & Total & \\ \cline{2-8}  & intra & extra & intra & extra & intra & extra & \\ \hline
8 & 4 & 28 & 2 & 9 & 6 & 37 & 43 \\ \hline
9.5 & 23 & 5 & 7 & 3 & 30 & 8 & 38 \\ \hline
11 & 10 & 0 & 0 & 1 & 10 & 1 & 11 \\ \hline Total & 37 & 33 & 9 & 13 & 46 & 46 & 92 \\ \hline \multicolumn{8}{c}{SynCone abutments} \\ \multicolumn{8}{c}{Sulcus height of the abutments (mm)} \\ \cline{2-8} degree & \multicolumn{2}{c}{1.5} & \multicolumn{2}{c}{3.0} & \multicolumn{2}{c}{4.5} & Total & \\ \cline{2-8}  & Intra & extra & Intra & extra & intra & extra & intra & extra & Total \\ \hline
0\({}^{\circ}\) & 9 & 11 & 23 & 15 & 1 & 1 & 33 & 27 & 60 \\ \hline
7.5\({}^{\circ}\) & 4 & 7 & 6 & 7 & 0 & 1 & 10 & 15 & 25 \\ \hline
15\({}^{\circ}\) & 0 & 0 & 2 & 4 & 0 & 0 & 2 & 4 & 6 \\ \hline
22.5\
## 3 Methods
### Synchronization in Phase Space
We compare the SYNC approach to Pearson correlation coefficient computed as:  
\[R=\frac{\sum_{n=1}^{N}(x_{n}-\bar{x})(y_{n}-\bar{y})}{\sqrt{\sum_{n=1}^{N}(x_{ n}-\bar{x})^{2}}\sqrt{\sum_{n=1}^{N}(y_{n}-\bar{y})^{2}}}, \tag{4}\]  
where \(\left\{x_{i}\right\}_{i=1}^{N}\) and \(\left\{y_{i}\right\}_{i=1}^{N}\) are the two time series and \(\bar{x}\) and \(\bar{y}\) denote their sample means. We also compared the SYNC index to spectral coherence \(C_{xy}(f)\) across the low-frequency band (0.01-0.1 Hz):  
\[C_{xy}(f)=\frac{|\sum_{f}S_{xy}(f)|^{2}}{\sum_{f}S_{x}(f)\cdot\sum_{f}S_{y}(f)}, \tag{5}\]  
where \(S_{x}(f)\) and \(S_{y}(f)\) are the power spectrums of the time series estimated using [PERSON]'s method [44] and \(S_{xy}(f)\) is their cross spectrum. In Figure 1, the CRPs and the extracted LOS are represented for three different cases:  
1. a sine wave synchronized with itself throughout the observation period \(0\)-\(T\);a sine wave and a second sine wave fully synchronized during half of the observation period 0-\(T/2\);
3. a sine wave and a second intermittently synchronized sine wave during a first observation interval 0-\(T/4\) and a second observation interval \(T/2\)-\(3T/4\). For the first case, \(R=1\), \(C_{xy}=1\) and \(SYNC=1\), since the LOS is complete. For the second case \(R=0.7\), \(C_{xy}=0.16\) and \(SYNC=0.5\), while for the third case \(R=0.7\), \(C_{xy}=0.15\) and \(SYNC=0.25\). The Person correlation measures the overall statistical similarity between the two time series, the coherence metric measures their linear relationship in the frequency domain while the SYNC metric takes into account also the intermittency.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 3 Materials and Methods: A Gamified AI-Driven System
### System Architecture
#### 3.1.3 AI Analytics and Simulation Core  
The back-end infrastructure supports advanced analytics through AWS SageMaker and Python-based ML integrations. Data fusion and time-series modeling are employed to identify behavioral trends, predict mood variations, and deliver tailored feedback. AI models are trained to analyze combined indicators, including gameplay performance, survey responses, and environmental variables, offering insights into individual mental health trajectories. Figure 2 displays a snapshot of stored user data, capturing gameplay metrics, mood ratings, and contextual information.
## 4 Discussion  
In the recent literature, PDFU healing is still described as a challenging and protracted process with high recurrence rates, negatively affecting the patients' quality of life and potentially leading to amputation [42; 43; 44]. A normal wound closure follows a dynamic process comprising four sequential phases: hemostasis, inflammation, proliferation and remodeling [45]. However, the process of wound healing is affected negatively in patients with diabetes. Specifically, the process starts with a decrease of fibrinolysis with an imbalance of cytokines affecting wound healing. Moreover, angiogenesis is decreased by hyperglycemia. Cell migration (keratinocytes and fibroblasts) is decreased with a negative impact on extracellular matrix production and thus causing a deficient re-epithelialization [46].  
Several methods have been proposed to treat PDFUs [20; 21; 22; 24]. However, the current crucial issue is to relieve the pressure because the wound never heads until the pressure is off-loaded. There are many methods to relieve it [47] including body weight reduction, total contact casts [48], corrective shoes with customized insoles [49] and even surgical correction, such as metatarsal head resection and ray amputation [20; 21; 22; 33]. In this context, MIS has been proposed as an effective intervention [24]. Its main objectives are primarily the healing of PDFUs by relieving the pressure on them; secondarily, the prevention of ulcer recurrence, transfer lesions and local infections [6].  
Many reports showed the more traditional surgical procedures (metatarsal head resection, metatarsal osteotomy and exostectomy, metatarsal head resection, arthrodeis) and tendon balancing (Achilles' tendon lengthening, gastrocnemius recession, and/or plantar fascia release) can achieve good to excellent results on diabetic ulcer healing,  
Figure 3: Funnel plot for the effect size of overall complication rate showing evidence of publication bias with two studies being trimmed/removed. White circles are the effect sizes of the studies included in the present systematic review and meta-analysis, while black circles represent effect sizes not reported by published studies, probably due to publication bias. If the corresponding observed effect sizes are trimmed/removed, the overall effect size (white diamond) becomes the “adjusted” overall effect size (black diamond).  
regardless of their grade severity and dimensions [1; 2; 3]. However, open surgical bone procedures are often correlated with high complication rates, such as wound recurrence or postoperative infection, while tendon balancing presents risks of tendon overlengthening, rupture and plantar flexion weakness [21; 38; 50].  
To the best of our knowledge, no previous systematic review has provided quantitative evidence synthesis regarding the mere efficacy of MIMOs for diabetic foot ulcerations.  
The goal of the present systematic review and meta-analysis was to identify the available body of evidence of these MIMOs regarding their effectiveness in promoting PDFU healing and in preventing their recurrence and to summarize the rates of postoperative complications.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Burst Selection  
The Sentinel-1 standard acquisition mode (TOPS) consists in sweeping the antenna beam from the aft to the fore, thereby illuminating a portion of the ground called a \"burst\". Three sub-swaths are successively covered to broaden the swath. The Sentinel 1-A and 1-B satellites are tasked to maintain synchronicity of burst acquisition, which is essential to ensure an optimal interferometric coherence. However, Sentinel-1 acquisitions do notnecessarily cover the same burst coverage. In other words, the acquisition start/stop times (w.r.t., the ascending node crossing time, or ANX time) may vary from one acquisition to another. As input, FLATSIM is given the north and south latitude limits of the area of interest. Then, starting from these a priori limits, an optimization step is required to select the best set of acquisitions that simultaneously maximizes the latitudinal extent of the dataset and the number of acquisitions, without including gaps in the processing. The FLATSIM strategy consists in an iterative search for the optimal number of consecutive bursts that achieves the largest number of complete acquisitions on the chosen set of consecutive bursts (Figure 1). Note that dates of acquisition which are incomplete from one or more of the three sub-swaths are discarded. Up to about 40 contiguous bursts can be selected.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions  
### Principles of Ergonomics Applied to Human-Machine Interactions  
#### 3.1.1 Physical Ergonomics (Posture, Fatigue, and Physical Effort)  
Physical ergonomics focuses on the anatomical and biomechanical compatibility between the human body and the physical demands of tasks in human-machine interactions. A poor working posture, prolonged physical effort, and muscle fatigue are primary actors associated with work-induced musculoskeletal disorder problems (WMSDs), which are prevalent in repetitive and manual handling operations across industrial environments [36]. Ergonomic misalignment can lead to high physical strain, reduced productivity, and long-term health consequences, especially in aging workforces or those performing highly repetitive tasks [37].  
Recent advancements in sensor technology and wearable systems have enabled more precise monitoring of posture and physical exertion in real-time. Devices such as inertial measurement units (IMUs), EMG, and pressure sensors allow for the objective identification of biomechanical risks and physical overload by tracking joint angles, movement velocity, and muscular activation during tasks [38]. These data can be processed through machine learning algorithms to estimate ergonomic indices and support preventive interventions [38, 39]. Consequently, haptic feedback systems integrated into smart workwear are proving effective at training workers to adopt a better posture and reduce the time spent in harmful positions, thereby reducing local fatigue and long-term strain [40].  
The integration of collaborative robots (cobots) further enhances physical ergonomics by reducing human physical effort through task sharing. In hybrid workstations, cobots assist with heavy lifting, repetitive motions, or awkward positioning, enabling a redistribution of physical demands and lowering the incidence of musculoskeletal injuries [41]. When designed with anthropometric considerations and real-time adaptability, such systems contribute to sustainable productivity and improved occupational well-being [42].  
Table 1 provides a structured overview of the main physical ergonomic factors that influence the human-machine interaction (HMI). It highlights how elements such as posture, fatigue, and physical load impact operator performance, safety, and task execution [39, 40]. The Table 1 also emphasizes the role of technological solutions, such as wearable sensors, haptic feedback systems, and collaborative robots (Cobots), in mitigating ergonomic risks by enabling real-time monitoring, posture correction, and physical workload redistribution [41]. By linking each factor to its ergonomic implications and referencing validated sources, the table offers a concise framework for identifying critical intervention points in HMI system design [41].
## 5 Variability of the \(Q_{p}/R_{s}\) Ratio with the CIE Standard Sky Types
When only three CIE categories were considered (clear = 3, partial = 2, and overcast = 1), \(r(Q_{P},CIE_{cloudness})=0.52\) (\(p\)-value \(<0.001\)) and \(r(R_{s},CIE_{cloudness})=0.52\) (\(p\)-value \(<0.001\)), and \((Q_{P}/R_{s},CIE_{cloudness})=-0.22\) (\(p\)-value \(<0.001\)). From these results, a weak relationship can be established between the value of the \(Q_{P}/R_{s}\) ratio and the CIE standard sky classification. However, previous results were confirmed in this research, in so far as the ratio of photosynthetic photon flux density to broadband solar irradiance, \(Q_{p}/R_{s}\), presented its highest values over cloudy conditions and decreased with clear sky conditions, following the CIE Standard Sky classification as a reference for defining the sky conditions.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 3 Concepts of the CPS Communication Hub
### Conceptual Model for Cyber-Physical Systems
The _physical resources zone_ includes all the physical CPS entities (devices) that are able to bind the physical world with the virtual world clouds. Such entities may need gateway capabilities towards the mobility zone, operation over the Internets/Intranets, operation with the clouds/virtual means of IoT platforms, operation with vertical SP, operation with the local networks of physical devices, and operation with access and authorization systems. The _authorization and trust zone_ consists of horizontal service providers that are especially focused on providing services of authorized access control and trust to all the other stakeholders in the other zones. The zone-based conceptual architecture of CPS systems captures huge variety in the CPS value networks in a very simple structure relying on a horizontal approach [12]. The contribution of this article, called _trustworthy communication hub for cyber-physical systems_, is depicted in the middle and right side of the Figure 2, and overviewed in the following. Figure 2: Conceptual model of trustworthy communication hub for cyber-physical systems. The right side of the Figure 2 represent an example view of the relationships between owners of physical CPS resources (devices). An owner can be an individual person, a group of people, or an organization, who may have agreements between each other. For example, person D has an agreement with CPS service provider B and with the group of people G, which can be, for example, family of person D. The natural needs of each owner are to control the _access_, _usage_ of his/her/is own the _physical CPS resources_ (_devices_), and the _exposed information_. Today, there are multiple vertical CPS SPs providing services for the owners; however, there is no proper means for the owners to referred control process. The CPS hub has been developed to enable the referred control process for the owners. In the CPS hub solution, each owner is expected to have their own communication space, representing the owner in a virtual world. The communication space acts on behalf of the owner to link and represent the services, networks, contents, and physical CP resources (devices) of the owner in the virtual world. The security policies of the owner are used to define the rules for the _access_, _usage_ of the resource, and _exposed information_. The referred security policies of a specific owner are inherently the same for all the stakeholders in all the zones; therefore, the horizontal services of authorization and trust zone are envisioned to be useful for all stakeholders in the CPS value chains.
## 2 Material Statistics  
### Micro-Scale Statistics  
Statistics of mechanical and physical properties of common E-glass fibre and epoxy are shown in Table 1. Random variables considered in micro-scale mainly include \(E_{f}\) ([PERSON]'s modulus of fibre), \(\
u_{f}\) ([PERSON]'s ratio of fibre), \(\rho_{f}\) (density of fibre), \(E_{m}\) ([PERSON]'s modulus of matrix), \(\
u_{m}\) ([PERSON]'s ratio of matrix), \(\rho_{m}\) (density of matrix) and \(V_{f}\) (fibre volume ratio). It has been experimentally shown that E-glass fibre tends to be mechanically isotropic [26]. Experimental observation on the statistics of fibre and matrix properties is generally rarely reported. In Table 1, the coefficient of variation (standard deviation/mean value) is assumed to be 5% if a reference is not given. All micro-scale properties shown in Table 1 are assumed to follow normal distribution in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Mean Value** & **Standard Deviation** & **Distribution Type** \\ \hline \multicolumn{4}{c}{**E-Glass Fibre**} \\ \hline \(E_{f}\) & 72 GPa & 5 GPa [27] & Normal \\ \(\
u_{f}\) & 0.25 & 0.05 [26] & Normal \\ \(\rho_{f}\) & 2540 kg/m\({}^{3}\) & 127 kg/m\({}^{3}\) & Normal \\ \hline \multicolumn{4}{c}{**Epoxy**} \\ \hline \(E_{m}\) & 3.45 GPa & 0.086 GPa [27] & Normal \\ \(\
u_{m}\) & 0.35 & 0.0175 & Normal \\ \(\rho_{m}\) & 1100 kg/m\({}^{3}\) & 55 kg/m\({}^{3}\) & Normal \\ \hline \multicolumn{4}{c}{**Fibre Volume Ratio**} \\ \hline \(V_{f}\) & 0.60 & 0.03 & Normal \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of properties of fibre and matrix.