esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
## 2 Mechanical Structure of the Robot  
Herein, we study a wall-climbing robot with fast and continuous motion on a flat non-ferromagnetic material wall. We choose omnidirectional wheel locomotion because of its advantages of fast speed, continuous motion, and flexible locomotion. Furthermore, for the non-ferromagnetic wall environment, this paper designs a non-sealed negative pressure air chamber adhesion, which has good robustness, stable attachment, and no special requirements for wall roughness.  
A schematic diagram of the omnidirectional mobile robot designed for this paper is shown in Figure 1. The angle between adjacent wheels is \(\Phi=2\pi/3\). \(XOY\) and \(X^{\prime}O^{\prime}Y^{\prime}\) are the world and the robot coordinate system, respectively. \(O\) is the axis origin of the world corrdinate system and \(O^{\prime}\) is the axis origin of the robot corrdinate system. The three omnidirectional wheels are denoted as \(W_{1}\), \(W_{2}\), and \(W_{3}\), where the geometric center of \(W_{1}\) is along \(O^{\prime}X^{\prime}\).  
In order to provide sufficient adhesion force, an adhesion module shown in Figure 2 has been designed. The culvert fan acts as a negative pressure generator, and the double-layered rubber ring acts as a negative pressure air chamber. As a non-contact adhesion, our chamber does not affect the locomotion by the friction with the wall. The advantage of non-contact adhesion facilities the development of autonomous control of the robot.  
Assuming the fluid in the progress of adhesion is ideal, according to the Bernoulli equation for ideal fluids, we have  
\[\begin{cases}F_{a}=(p_{0}-p_{c})S\\ p_{0}+\frac{\rho_{a}v_{0}^{2}}{2}=p_{c}+\frac{\rho_{a}v_{c}^{2}}{2}\\ Q=Ldv_{c}\end{cases} \tag{1}\]  
where \(F_{a}\) is the adhesion force, \(p_{0}\) is the pressure of the atmosphere, \(p_{c}\) is the pressure in the chamber, \(v_{0}\) is the velocity of the fluid around the chamber's upper surface, \(v_{c}\) is the velocity of the fluid in the chamber, \(\rho_{a}\) is the density of air, \(Q\) is the fluid flow, \(S\) is the area of the chamber's upper surface, \(L\) is the Perimeter of the chamber's lower surface, and \(d\) is the distance from the chamber's lower surface to the wall.  
Combining three equations in (1), we have  
\[F=\frac{\rho_{a}Q^{2}S}{2L^{2}d^{2}} \tag{2}\]  
According to (2), the flow of the generator should be as large as possible. The radius of the air chamber does not affect the adhesion force directly.  
Figure 1: Schematic diagram of the three-wheeled omnidirectional robotic system.  
Figure 2: Schematic diagram of the negative pressure air chamber adhesion.  
The robot mechanism combining the three-wheeled omnidirectional chassis and the negative pressure air chamber is shown in Figure 3. In this structure, the geometric centers of the wheels are evenly distributed on a circumference, and each wheel is driven by a brushless motor.
# A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts
## 1 Introduction
As mentioned above, given the importance of AA for Islamic law texts, the available research is limited, compared to that on general Arabic language AA. This work aims to address this research gap by investigating the effectiveness of leveraging different Arabic pretrained transformer models for addressing multi-class AA in Islamic law texts. We aim to answer the following research question: to what extent can Arabic pretrained transformer models provide satisfactory performance for the task of Arabic authorship attribution in Islamic legal texts? We adopt an experimental approach to our investigation. Because no dataset has been designed specifically for this task, we design and build our own dataset using Islamic legal text resources from Al-Maktaba Al-Shamela. We conduct several experiments using four Arabic transformer-based models: AraBERT, AraELECTRA, ARBERT, and MARBERT. We conclude that pretrained transformer models, specifically ARBERT and AraELECTRA models, have shown significant results for the addressed task in this work. The contributions of this work are summarized as follows:  
1. Survey the literature on approaches used for AA in Arabic language works, including Islamic studies. 2. Develop a new, specialized Islamic legal texts dataset. 3. Implement four different Arabic pretrained transformer models (SOTA approaches) for classical Arabic AA. We fine-tune the models and compare their performance. 4. Recommend the best values for the hyperparameters for classical Arabic AA by analyzing the obtained results. The remainder of the paper is structured as follows. In Section 2, we review related work in the area of Arabic AA. Section 3 presents our methodology, provides details of the dataset collection process, and discusses our experimental setup and evaluation metrics. Section 4 discusses the results. Finally, Section 5 presents the conclusions of this study and suggestions for future work.
## 1 Introduction
### KPIs and Digital Twin Platforms in Manufacturing  
A lot of KPIs have been defined in literature (Manufacturing, Environmental, Design, and Customer) [7], and, despite the fact that methods have been developed for the precise acquisition of measured values [8], the interconnection of the values (Figure 1) can lead to mistaken decision making when one searches for the root cause of an alarm occurring in a manufacturing environment.  
In addition, regarding manufacturing-oriented dashboard systems, they aid in the visualization of complex accumulations, trends, and directions of Key Performance Indications (KPIs) [9]. However, despite the situation awareness [10] that they offer, they are not able to support the right decision made at management level by elaborating automatically the performance metrics and achieving profitable production [11]. In the meantime, multiple criteria methods are widely used for decision support or the optimization of production, plant, or machine levels [12; 1], based (mainly) on the four basic manufacturing attributes i.e.,: Cost, Time, Quality, and Flexibility levels [1]. This set of course can be extended. The \"performance\" that is collected from the shop-floor has been considered as measured values (MV) and their aggregation or accumulation into higher level Key Performance Indicators, as initially introduced by [13]. The goal of all these is the achievement of digital manufacturing [14].  
On the other hand, the term digital twin can be considered as an \"umbrella\", and it can be implemented with various technologies beneath, such as physics [15], machine learning [16], and data/control models [17]. A digital twin deals with giving some sort of feedback back to the system and it varies from process level [15] to system level [18], and it can even handle design aspects [19].  
Regarding commercial dashboard solutions, they enable either manual or automated input of measured values for the production of KPIs [20; 21; 22]. However, their functionality is limited to the reporting features of the current KPI values, typically visualized with a Graphical User Interface (GUI), usually cluttered, with gauges, charts, or tabular percentages. Additionally, there is need to incorporate various techniques from machine learning or Artificial Intelligence in general [23] and signal processing techniques [24]. The typical functionality that is found in dashboards serves as a visual display of the most important information for one or more objectives, consolidated and arranged on a single screen, so as for the information to be monitored at a glance [9]. This is extended in order to analyze the aggregated KPIs and explore potential failures in the future by utilizing monitored production performances. Finding, however, the root cause of a problem occurring, utilizing the real time data in an efficient and fast way, is still being pursued.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Evaluation of QoE Aspects  
Based on experimental results, it is found that the versions encoded at the 4 K resolution and QP of 22 for all the three contents achieve very high scores in terms of presence, perceptual quality, and acceptability. In particular, the presence scores for Content #1, Content #2, and Content #3 are, respectively, 4.22 MOS, 4.33 MOS, and 4.28 MOS. The corresponding perceptual quality scores are 4.44 MOS, 4.44 MOS, and 4.56 MOS, respectively. This perceptual quality range are similar to those obtained in [17; 41]. Also, their acceptability rates are all equal to 100%. This means that versions encoded at the 4 K resolution as provided on existing video streaming platforms (e.g., YouTube and Facebook) are acceptable to users.  
Since the acceptability can be considered as the overall quality of a service [6; 8], the impacts of the QoE aspects on the acceptability are also examined. As shown in Table 5, there is a statistically significant effects of the presence and the perceptual quality on the acceptability (i.e., \(p<0.05\)). In addition, the effect sizes are both \"large\" (i.e., \(\eta^{2}>0.14\)). This means that both the quality of displayed video as well as the presence must be satisfactory, so that a 360 video service is acceptable to users.  
With respect to the cybersickness, there is a high percentage (i.e., 89% for device set D1 and 94% for device set D2) of the viewers in our experiment reporting nausea and dizziness when watching video versions. Therefore, the cybersickness is a serious problem for 360 video. However, as discussed later, the cybersickness strongly depends on the content motion of a video.  
In the following, the influences of different factors on QoE are discussed in detail.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 3 Problem Description and System Model
### System Model
#### 3.2.4 Performance Metrics  
The 5G core network infrastructure provider, while providing physical resources to the tenants, attempts to maximize its operating profit. As such, the main goal of the resource provisioning for slices is to maximize resource provisioning revenue by provisioning as many slice requests as possible. In this paper, we use slice acceptance ratio, long-term average provisioning revenue and provisioning revenue-to-cost ratio as metrics to evaluate the performance of the provisioning algorithms. They are defined below.  
**Slice acceptance ratio (\(\lambda\)):** It is the ratio of the number of slices successfully provisioned to the total number of slice requests that arrive over a period of time. Then,  
\[\lambda=\lim\limits_{T\rightarrow+\infty}\frac{\sum\limits_{t=0}^{T}S_{m}(t)} {\sum\limits_{t=0}^{T}S(t)}, \tag{7}\]  
where \(S(t)\) is the total number of slice requests at time \(t\) and \(S_{m}(t)\) is the number of slice requests provisioned successfully at time \(t\).  
**Long-term average provisioning revenue (\(\mu\)):** Here, we assume the unit price of CPU capacity and bandwidth is 1. Then, the provisioning revenue of slice request \(G^{S}\) at time \(t\) is defined as:  
\[REV(G^{S},t)=\sum\limits_{v^{S}\in V^{S}}c(v^{S})+\sum\limits_{e^{S}\in E^{S}} b(e^{S}). \tag{8}\]  
The long-term average provisioning revenue is represented as:  
\[\mu=\lim\limits_{T\rightarrow+\infty}\frac{\sum\limits_{t=0}^{T}\sum\limits_{ G^{S}\in S_{m}(t)}REV(G^{S},t)}{T}. \tag{9}\]  
**Provisioning revenue-to-cost ratio (\(\eta\)):** The provisioning cost of slice request \(G^{S}\) at time \(t\) is  
\[COST(G^{S},t)=\sum\limits_{v^{S}\in V^{S}}c(v^{S})+\sum\limits_{e^{S}\in E^{S} }|L(p^{I}(e^{S}))|b(e^{S}), \tag{10}\]  
where \(p^{I}(e^{S})\) is the physical path hosting the slice link \(e^{S}\) and \(L(p^{I}(e^{S}))\) denotes the set of physical links in \(p^{I}(e^{S})\). Hence, we define provisioning revenue-to-cost ratio \(\eta\) as:  
\[\eta=\frac{REV}{COST}=\lim\limits_{T\rightarrow+\infty}\frac{\sum\limits_{t= 0}^{T}\sum\limits_{G^{S}\in S_{m}(t)}REV(G^{S},t)}{\sum\limits_{t=0}^{T}\sum \limits_{G^{S}\in S_{m}(t)}COST(G^{S},t)}. \tag{11}\]
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 6 The SDN/NFV Domain
### Transmission Layer  
Today, the push towards 5G services and applications that require, among others, low latency and high capacity, poses new technological challenges. For example, the increased use of streaming, edge and cloud applications popular at portable devices requires architectural changes to satisfy the 5G/B5G mobile network specifications. Especially in Cloud RAN (C-RAN) optical fibre-based mobile fronthaul, provides the required flexibility, low latency and high capacity [61]. This context, Open Base Station Standard Initiative (OBSAI) and Common Public Radio Interface (CPRI) are common transmission techniques in 4G fronthaul networks, though inadequate for massive 5G/B5G services. Later, Ethernet-based CPRI (e-CPRI), digitalizing the RF signal is commonly used in 5G fronthaul network rollouts, due to its flexibility, efficiency and low quantisation resolution required [62; 63; 64].  
Despite all these benefits, e-CPRI requires important Digital Signal Processing (DSP) resources in the Remote Radio Head (RRH) that increases the system power consumption. RoF technique has recently proposed as provides the required bandwidth while simplifies the interface of the RRHs [65; 66; 67; 68]. NOMA-CAP modulation has recently been investigated as a promising B5G modulation format candidate to increase the capacity and flexibility of future mobile networks. In the framework of the ALLIANCE project, we have experimentally demonstrated the convergence of a NOMA-CAP wireless waveform with a single-carrier wired signal in a PON scenario using RoF technology [69]. Figure 5 shows the reference scenario for this work where the Pulse Amplitude Modulation 4-level (PAM4) has been used for legacy systems and NOMA-CAP for the future B5G fronthaul. Two NOMA levels (strong and weak) have been considered per each CAP band [69]. We have also first experimentally demonstrated NOMA-CAP as a modulation format for split-enabled optical interconnects with a capacity of up to 630 Gb/s using 7-core MCF and requiring an electrical bandwidth of 25 GHz. More details about this experiment and results can be found in [70]. In addition, we have provided an optical power budget enhancement in 50-90 Gbps IM-DD PONs with NOMA-CAP using Semiconductor Optical Amplifier (SOA)-based amplification [71]. Finally, this technique has also been used to provide a flexible resource provisioning for polarisation independent coherent PONs thanks to NOMA and multiCAP modulation, increasing at the same time the data-rate, up to 20 Gbps, and providing flexible reach among and existing PON, 20 km to 40 km, and a new extended nested PON, extra 20 km to 70 km, and users, from initial 32-64 to more than 380 users [72].
## 1 Introduction
As one of the best indicators of vegetation growth state and coverage, the time series normalized difference vegetation index (NDVI), derived from remote sensing images, is widely applied in vegetation change monitoring in arid areas [27; 28]. So, in this context, the vegetation dynamic in the period from 2000 to 2020 and the role of climate change, especially precipitation and temperature, and human activities, especially land use change, in the vegetation dynamics were analyzed and revealed based on remote sensing data NDVI, meteorological data, and land use/coverage change data.
## 6 Integration of Terrestrial Scanning, Terrestrial Photogrammetry and Aerial Photogrammetry
The texture quality value informs us about the maximum detail that can be obtained from the images. The result is a 3D model that met the required output quality criteria, including LOD 300, color and photorealism. The final model, shown in Figure 11, serves mainly for presentation purposes as the digital twin of the real object, but also defined the basis for further analysis and required outputs. Figure 10: Denoised and filtered point cloud connected with terrestrial and aerial photogrammetry (**a**) and histogram of interconnection points (**b**).
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 5 Result and Discussion  
### Food Accessibility Results  
The present study calculated the accessibility values of different foods based on the enhanced two-step algorithm. Figure 9 shows the spatial distributions of accessibility of different types of food, with the point size in the figure representing the accessibility capability. Among the nine main urban areas in Chongqing, areas with high food accessibility were generally distributed in the center of each district. In Figure 9, the black dots represent the districts that were not accessible within a 20 min walk (\(AI=0\)). The results showed no areas in the city that were inaccessible for grains, oils, eggs, vegetables, or fruits, whereas there were a few areas along the borders of each district or in newly developed areas that were inaccessible for seafood, meat, and milk. This result could mainly be attributed to many of the newly developed neighborhoods having incomplete facilities. In addition, there was a relative lag in access for some foods within a 20 min walk. Moreover, rural areas are often found at the periphery of urban areas; households in these rural areas raise some poultry, which was not considered in the present study.  
Figure 9: _Cont._  
The AI values of various food categories were categorized and plotted as box-and-whisker plots (Figure 10). When combining all types of accessibility indicators, the accessibility of grain and oil exceeded those of several other food categories, reflecting the high  
Figure 9: Spatial distribution of food accessibility in Chongqing.  
accessibility of staple foods in Chongqing. The food category of fruits and vegetables had the second-highest accessibility, whereas the accessibility values of seafood, meat, and eggs were relatively small. These results are consistent with the larger situation in China of more affordable fruits and vegetables and lower demand for meat. Figure 10 was not used to filter the outlets.
## 3 Results
### Electric Field Strength Prediction Map  
The first image (Figure 11) shows a map with numerous red dots, representing the locations of electromagnetic field (EMF) measurements scattered throughout the urban area in the center of Paris. The map provides a clear view of each data point's location but does not convey the intensity or concentration of values between these points. Therefore, it is necessary to refer to the second image, which is an overlay of the electric field strength map on the same urban area. This map uses different colors (yellow, green, and purple) to represent the intensity or density of the variable throughout the region. Areas with warmer colors, such as yellow or red, indicate higher values, while areas with cooler colors, such as green or purple, indicate lower values. This heatmap was generated using the optimal model, k-nearest neighbors (k-NN) with \(k=12\) and \(p=2\), as the interpolation method (Figure 12).
# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 5 Experimental Results
### Classification Analysis Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Feature Type** & **Acc.** & **F1-Score** & **Recall** & **Precision** \\ \hline \multirow{3}{*}{RF} & Statistics & 77.2 & 75.1 & 73.9 & 76.3 \\  & TF-IDF & 81.8 & 80.9 & 83.4 & 80.5 \\  & Bag of Words & 81.1 & 78.6 & 77.9 & 81.1 \\  & Statistics + TF & 85.6 & 84.1 & 84 & 85 \\  & IDF+ Bag of Words & & & & \\ \hline \multirow{3}{*}{SVM} & Statistics & 79.6 & 79 & 70 & 60 \\  & TF-IDF & 81.2 & 82.7 & 87.2 & 78.7 \\  & Bag of Words & 80.6 & 81.1 & 81.8 & 80.4 \\  & Statistics + TF & 83.5 & 83.8 & 85.5 & 82.1 \\  & IDF+ Bag of Words & & & & \\ \hline \multirow{3}{*}{NB} & Statistics & 68.2 & 71.3 & 76.3 & 67.6 \\  & TF-IDF & 78.6 & 76.1 & 75.6 & 80.5 \\  & Bag of Words & 79.8 & 78.4 & 78.9 & 79.7 \\  & Statistics + TF & 82.5 & 81.5 & 83.4 & 80.8 \\  & IDF+ Bag of Words & & & & \\ \hline \multirow{3}{*}{XGBOOST} & Statistics & 76.3 & 76.1 & 75.6 & 80.5 \\  & TF-IDF & 85.6 & 84.1 & 84.0 & 85.8 \\ \cline{1-1}  & Bag of Words & 83.1 & 82.6 & 84.4 & 81.6 \\ \cline{1-1}  & Statistics + TF & 88.3 & 83.1 & 84.3 & 88.4 \\ \cline{1-1}  & IDF+ Bag of Words & & & & \\ \hline LSTM & 91.7 & 92.6 & 90.5 & 94.8 \\ CNN & Word2 vec & 90.6 & 92.8 & 93.8 & 91.8 \\ LSTM-CNN & 93.8 & 93.4 & 94.1 & 93.2 \\ \hline \hline \end{tabular}  
* Acc. represents accuracy; RF = Random Forest, SVM = Support Vector Machine, NB = Naive Bayes, XGBoost = Extreme Gradient Boosting, LSTM = Long Short-term Memory, CNN = Convolutional Neural Networks.  
\end{table} 
Table 3: Performance Results of the Classification Models. the location of predictive contexts and higher prediction abilities of certain n-grams in a sentence in contrast to the entire sentence [85].
## 4 Discussion  
Lanthanides have now been widely applied in medicine. Their applications include therapy and imaging. Unlike other metals, lanthanides are luminescent, stable, and biosafe [14]. Among the lanthanides, Tb is a typical lanthanide with strong green fluorescence and has potential for biomedical labeling or imaging. This material has also been studied for use as a carrier for drugs such as a measles virus antibody [15] or cobra venom antigens [10]. Due to the advantages of Tb, we chose this material to produce a nanocomplex, **TMC**, which is Tb\({}^{3+}\) nanorods double conjugated with CD133 and PD-L1 mAb for the purpose of CSC labeling and therapeutic solution. TbPO\({}_{4}\)H\({}_{2}\)O formed nanorods 30-50 nm in diameter and 300-800 nm in length. After surface functionalization, the nanorods were successfully double conjugated with CD133 and PD-L1 mAb with high efficiency (60%-100%). The labeling ability of **TMC** to detect CSCs is equivalent to that of the reference CD133-FITC. However, CD133 is also expressed in stemlike cells throughout the body. Thus, mAb against other CSC-specific markers such as EpCAM, CD44, CD24, etc., will be double conjugated with our TbPO\({}_{4}\)H\({}_{2}\)O@silica-NH\({}_{2}\)-mAb\({}^{\ast}\)CD133 (instead of PD-L1 mAb) in order to improve the specific targeting activity of the nanocomplex for fundamental CSC research or for future clinical applications.  
Together with CSC probing, **TMC** with PD-L1 mAb was produced in a structure selected for the purpose of cancer treatment. PD-L1 is a ligand of PD-1, and the interaction of PD-L1 and PD-1 in immune cells may cause inactivation of these cells [16]. However, PD-L1 acts as an antiapoptotic receptor in response to Fas ligation. Therefore, PD-L1 is closely related to cancer stem cell proliferation [17]. PD-L1 antibodies are commercially available to clinically treat several types of cancer [18]. Herein, although **TMC** only slightly inhibited the growth of NTERA-2 cells in vitro, the nanocomplex strongly inhibited the growth of these 3D NTERA-2 spheroids when co-cultured with macrophages. According to [PERSON], PD-L1 monoclonal antibodies enhance the ability of macrophages to proliferate and activate, leading to increased numbers of TAM (tumor-associated macrophages) and thereby inhibiting the growth of tumor tissues [17].