esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Based Stakeholder Identification in Technical Change Management
## 2 Fundamentals
### Text Classification  
Text classification is an application of NLP [24]. Overall, NLP comprises approaches to automatically analyze and process human language with special algorithms and to elaborate correlations and patterns [25; 26; 27; 28]. Languages are very extensive, contain an infinite number of ways to form sentences, and ambiguities, as words as well as phrases can have different meanings in different contexts [25; 29]. In this regard, developing approaches that can automatically understand language and derive insights from textual information is a challenging task [25; 29]. To solve this task, NLP combines computational linguistics with statistical, machine learning, and deep learning models [29; 30].  
Within this contribution, text classification is used to analyze the information provided in CRs (e.g., change description or change cause) to determine whether specific stakeholders in the change process are affected by a change. The target variable of the classification problem is therefore the relevance of a CR for a certain business unit (relevant or not relevant). For this purpose, a hierarchical attention network (HAN) according to [PERSON] et al. [31] is applied. The approach of [PERSON] et al. [31] was developed on the basis of several assumptions. On the one hand, text documents have a hierarchical structure in which words form sentences, and sentences, in turn, form the text document. On the other hand, words within sentences have different meanings depending on the context and thus should not be considered in isolation. Furthermore, different parts of a text document are more or less relevant for the classification task. The aim of the model is to map the hierarchical structure of text documents and thus to identify relevant parts of the text document in order to be able to make meaningful classification decisions. For this purpose, the HAN is divided into two levels, which represent the hierarchical structure of text documents, as visualized in Figure 1. An exemplary implementation of a HAN can be found in [PERSON], [PERSON] [32].  
The first level consists of a word encoder and an attention layer on the word level. The second level consists of a sentence encoder and an attention layer on the sentence level. The word and sentence encoders are implemented by bidirectional gated recurrent units (GRU), which are used to map words and sentences with respect to their contexts. Within the attention layer, multilayer perceptrons are applied to identify informative words or phrases. The input variables for the HAN are vector representations (cf. Section 2.4) of the words of a text document, which are converted via the two layers into a vector representation of the text document. A Softmax function is used at the endpoint of the HAN to classify the documents, and the model quality is evaluated on the basis of the negative log-likelihood [31]. As hyperparameters, the maximum number of words and sentences in a document must be defined prior to the application of the HAN.
## 4 Results and Discussion
### YOLOv11n-seg Model Selection and Comparison with Other Models
#### 4.4.1 YOLOv11n-seg Model Architecture
* Mask precision: 0.848. * Mask recall: 0.845. These values confirm that the YOLOv11n-seg model performs exceptionally well in accurately segmenting and classifying objects.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Projected 6G Applications in Colombia's Economy  
Agriculture, livestock, and agroindustry represent strategic sectors within the Colombian economy, especially due to their impact on rural development, food security, and the inclusion of small producers. In this context, 6G networks emerge as key enablers for transforming these sectors through the incorporation of advanced technologies such as the IoT, smart sensors, AI, and ubiquitous connectivity [62]. Figure 5 presents a visual summary of the sectoral areas identified in Colombia's 2022-2026 National Development Plan (PND) where a significant impact of 6G networks is projected, highlighting the central role of agriculture in this transformation. Robust connectivity in rural areas would enable real-time monitoring of climatic variables, soil, and the health status of crops and animals, improving the efficiency of production processes. Furthermore, the integration of digital platforms would facilitate access to markets, financial services, and technical training, promoting the traceability and sustainability of the agri-food chain. These advances, supported by public policies aimed at productive transformation with a territorial focus, position the agricultural sector as a priority beneficiary of 6G.  
The integration of 6G technologies in the Colombian agricultural sector not only responds to a technological need, but also to a structural commitment to territorial development and productive equity. The possibility of connecting remote rural areas through ultra-high-speed, low-latency infrastructure represents an unprecedented opportunity to reduce the socioeconomic gaps that have historically limited the competitiveness of Colombian agriculture. Furthermore, the deployment of solutions based on AI, robotics, and cyber-physical systems offers a glimpse of an automated, resilient, and adaptive agricultural ecosystem in the face of phenomena such as climate change and market variability. In this sense, the scenarios described in Figure 5, derived from the 2022-2026 National Development Plan (PND), not only open up possibilities for the modernization of the sector, but also for the structural transformation of the Colombian rural production model. However, this progress requires effective coordination between public policies, investment in digital infrastructure, and strengthening local capacities. It is essential that connectivity initiatives be accompanied by training and technological appropriation processes, ensuring that small producers can actively participate in the agro-industrial digital ecosystem.  
Figure 5: Prioritized sectoral scenarios for the implementation of 6G networks in Colombia, as established in the National Development Plan (PND), 2022–2026.
# Ransomware-Resilient Self-Healing XML Documents
## 2 Background
### Ransomware
#### 2.1.1 Ransomware Lifecycle  
The authors of [16] analyzed 25 ransomware families and found that they all possess similar dynamics. They differ somewhat, however, according to the ransomware versions in place, but exhibit a similar overall high-level pattern. In general, the ransomware lifecycle spans the following six steps [16]:  
* Ransomware distribution: Like other malicious software programs, Ransomware uses social-engineering strategies to seduce victims to click links that lead to ridiculous content or download a malicious dropper or payload that causes infection.  
* Infection: The malicious code is downloaded at this stage, and the execution of the code begins. At this stage, a victim's machine will have been compromised by Ransomware, with the underlying files still not yet encrypted. Encryption is a reversible process, involving highly intensive CPU calculations operations. Encryption does not readily happen in a typical ransomware attack as it requires time for data evaluation by the malware and the scope for data encryption. Once this stage becomes active, all the automatic detection systems will have stopped. The firewall, proxy, antivirus, and intrusion detection programs will have been compromised to allow all malicious communications to take place, ultimately putting the ransomware in total control.
* C2 Communications: The malicious code continues to maintain access to its command-and-control server (C2) at this stage. Here, an attacker manages a C2 server and begins to send commands to the compromised system. The primary C2 communications objective with Ransomware entails the acquisition of an encryption key. Once that is complete, different systems are changed, and persistence is determined.
* File search-scanning: This is when things start to slow down a bit. The malware searches the computer to find files to encrypt first. It also scans for cloud data that are synced through folders and shown as local data. Then it starts searching for file shares. This may take time, depending on how much activity there is across the network. The goal is to examine the available information and determine the victim's level of permissions (e.g., list, published, delete).
* Encryption: The encryption starts once all data have been inventoried. Local file encryption may take minutes, but it may take several hours to encrypt a network file; this is because data on network file shares are locally copied and encrypted in most ransomware attacks. Then this is followed by uploading the encrypted files and removing the original ones. This phase takes a bit of extra time.
* Ransom demand: At this stage, a victim will receive a ransom message instructing them to render ransom; the Ransomware message is issued immediately once encryption has taken place. The Ransomware shows a screen that instructs its victim to pay before criminals delete the key to decrypt the files. The last function usually performed by Ransomware is to end and uninstall itself from a victim's machine. At this point, the hackers are ready to receive the ransom to their Bitcoin wallet.
## 2 Materials and Methods
### Optical Properties: Gloss and Internal Transmittance  
The Kubelka-Munk theory was used to establish the properties of spectral reflectance. The transparency of the film was established by applying the Kubelka-Munk theory for multiple scattering to the reflection spectra [19]. The surface reflectance spectra of the films were determined from 400 to 700 nm with a CM-3600d Spectro-colorimeter (Minolta Co., Tokyo, Japan) on a black and white background. Considering that as light passes through the film, it is absorbed and partially scattered, the absorption (K) and scattering (S) coefficients are quantified. The internal transmittance (Ti) of the films was quantified using Equation (4). Thus, \(R_{0}\) is the reflectance of the film on an ideal black background. Parameters \(a\) and \(b\) were calculated using the Equations (5) and (6), where \(R\) is the reflectance of the sample layer supported by a known reflectance \(R_{g}\). Measurements were taken in triplicate for each sample on the free film surface. A wavelength of 450 nm was considered for the analysis.  
\[Ti=\sqrt{(a-R_{0})^{2}-b^{2}} \tag{4}\]  
\[a=\frac{1}{2}\ (R+\frac{R_{0}-R+R_{g}}{R_{O}R_{g}}) \tag{5}\]  
\[b=\sqrt{a^{2}-1} \tag{6}\]  
The gloss was determined according to the ASTM D523 [20] standard method, on the surface of the free film, with an angle of incidence of 60\({}^{\circ}\), using a flat surface gloss meter (model 3 nh, Shenzhen Threenh Technology Co., Shenzhen, China). For this, measurements were taken in triplicate for each sample, and three films of each formulation were considered. All the results were expressed as gloss units, about a highly polished surface of black glass standard with an approximate value of 100. The transparency and gloss measurements were carried out with films conditioned one week in hermetic desicators at 25 \({}^{\circ}\)C and 53% RH.
## 3 System Model
### Security Model  
As shown in Figure 2, in order to ensure the wireless communication security between edge nodes and the end-user, the PKG method is used to generate the secret keys [20; 21; 22; 23]. This model assumes that the edge node and the end-user node use a time-division duplex (TDD) for communication purposes. Initially, the nodes send multiple training signals to each other in a particular time slot and obtain channel characteristics such as CSI during the channel estimation phase. It is necessary to determine whether the received CSI is legal or not before quantization. Thus, IAD is introduced. If it is an impersonation attack signal, the proposed model will estimate the channel and obtain CSI. Otherwise, it will conduct de-correlation and re-sampling on the legitimate CSI. Afterward, it performs the quantization step to quantify the sample value into the binary initial key bit sequence.  
The physical layer key generation method includes the following steps:  
* The transmitting and receiving nodes send training signals to each other for channel estimation to obtain CSI and determine whether they are impersonator signals or authentic signals.
* The legitimate signals received are quantified.
* A part of the quantized data are used as the initial key.
* The two parties (sender and receiver) perform key consistency negotiation to correct the quantization-mismatched output bits, thereby obtaining the same key to communicate.  
Figure 2: The key generation model.  
The filtered signals are transferred to the upper layer because the impersonation attack is roughly detected at the physical layer by building the security model. Hence, the communication and computation overhead is significantly reduced. Consequently, the overall system performance is improved in our proposed method.
## 4 Results  
For the bridge prototypes described in Section 3, the effectiveness of deck isolation was assessed, combining the previously introduced material and geometrical characteristics with the seismic demand associated with the three seismic scenarios.  
The first yield moment of the piers was calculated using the software program VCASLU v.7.8 [29], which adopts the bending moment--curvature formulation of IBC [20], while the shear strength was calculated according to the recommendations of Chapter 6 of Eurocode 2 [30]. In the definition of both resistances, the axial load in the piers due to permanent loads and 20% of the variable traffic loads was taken into account. For the continuous deck scheme, the deck load was distributed on the piers based on their tributary loading areas; the accuracy and limits of such simplification will be discussed later.  
By way of example, Table 5 shows the calculation flow for assessing the effectiveness of a bridge prototype characterized by the following properties:  
* Static layout: simply supported span
* Type of deck: 20 m span, RC slab with T beams
* Pier section: circular, 2 m diameter
* Pier height: 5 m
* Seismic scenario: Zone 1, municipality of Reggio Calabria  
The Elastic Limit acceleration (2.26 m/s\({}^{2}\)) is bracketed between the As-Built acceleration (9.14 m/s\({}^{2}\)) and the Equivalent Substructure acceleration (1.44 m/s\({}^{2}\)), revealing the effectiveness of deck isolation.
## 4 Equalization Technologies
### Vnle  
Thanks to FFE and DFE, the linear impairments can be efficiently eliminated. However, the residual nonlinear distortion mainly induced by the nonlinearity of devices and square-law detection can also severely impact the transmission performance of the PAM4 system. One of the most popular equalization techniques is VNLE, whose structure is shown in Figure 15. Note that the VNLE can be implemented based on FFE or DFE and we focus on FFE based VNLE in this paper. Considering the exponential growth of computational complexity, the third-order VNLE is introduced here.  
The output of VNLE is expressed as [75]  
\[z(k)=\sum_{l=0}^{n_{1}-1}h_{l}x(k-l)+\sum_{l=0}^{m_{2}-1}\sum_{i=0}^{l}h_{l,j}x (k-l)x(k-i)+\sum_{l=0}^{n_{3}-1}\sum_{i=0}^{l}\sum_{j=0}^{i}h_{l,ij}x(k-l)x(k-i )x(k-j) \tag{6}\]  
where \(h_{l}\), \(h_{l,j}\) and \(h_{l,ij}\) are the tap weights of 1 st-order, 2 nd-order and 3 rd-order kernels, respectively. \(x(k)\) is the input signal of FIR in VNLE at the sampling instant \(k\). In addition, \(n_{1}\), \(n_{2}\) and \(n_{3}\) respectively represent the number of taps for the linear part, 2 nd-order nonlinear part and 3 rd-order nonlinear part. Generally, the linear impairments of the PAM4 system and the self-phase modulation (SPM) of SSMF can be successfully eliminated by the first and third-order kernels of VF, while the second-order kernels are utilized to compensate the nonlinearity of devices such as modulator and PD. The kernel coefficients are commonly updated by LMS algorithm considering complexity. To simplify the convergence procedure, the kernels of each order are evolved at different speeds [75], which can be depicted as a gradient vector \(\mu\)\(=[\mu_{1},\mu_{2},\mu_{3}]\). However, there is no clear procedure on how to set the values of this vector.  
While limited by the high computation complexity of fully-connected VNLE, it is not practical to be implemented directly in the real applications. Various methods have been proposed to reduce the computational complexity of VNLE and the interested readers can refer to [76; 77; 78; 79; 80] for a detailed discussion on this topic. Taking modified Gram-Schmidt orthogonal decomposition as an example, the performance of sparse VNLE is described as shown in Figure 16[31]. The dependence of the BER on the back-to-back system without VNLE, and with VNLE or sparse VNLE are compared. As is shown, the required ROP at the FEC threshold is decreased by 0.7 dB for the VNLE. When sparse VNLE is employed, the increase in the ROP is less than 0.2 dB compared to VNLE. Figure 6b plots the dependence of the BER on the ROP for 10 km and 20 km transmission. No penalties are observed relative to the back-to-back system. Therefore, the sparse VNLE can maintain basically the same performance with a reduction of computational complexity, which is an excellent optimization for VNLE.
## 4 Discussion  
The CO\({}_{2}\) sorption capacity was previously studied by [PERSON] et al. (2017) through the same gravimetrical sorption methods and experimental conditions using mesoporous material MCM-41 (M) containing supported ionic liquids (SIL) with 50% load (50). The ionic liquids had the same (MeO)\({}_{3}\)Sipmim cation and four different anions (Cl\({}^{-}\) (CL), BF\({}_{4}^{-}\) (BF4), PF\({}_{6}^{-}\) (PF6), and TF\({}_{2}\)N\({}^{-}\) (TF2N)). This study evaluated the influence of the anion nature on the CO\({}_{2}\) sorption capacity (see Figure 4).  
Figure 4: CO\({}_{2}\) adsorption in IL supported in MCM-41 at 298 K (2017).  
Figure 3: CO\({}_{2}\) adsorption at 298 K.  
The results represented in Figure 4 clearly show that the IL containing a chloride anion (SILCLM50) had better CO\({}_{2}\) adsorption. For comparison, SILCLX20 (xerogel process) from our study (see Figure 3) presents a greater capacity to adsorb CO\({}_{2}\), reaching a value of 0.35 g CO\({}_{2}\)/g adsorbent (at 1.0 MPa) than SILCLM50 (supported on MCM-41) which attains only 0.11 g CO\({}_{2}\) /g adsorbent (at 1.0 MPa) (see Figure 4). In any case, supported ILs (in xerogel or MCM-41) provide higher CO\({}_{2}\) adsorption capacity than when used as solvents due to the contribution of the support. The comparison of the results reported in Figures 3 and 4 indicates clearly that when using xerogel support a lower amount of IL (20%) (see Figure 3) is required than when MCM-41(50%) is employed.  
Additionally, the literature reports results that described the use of different materials, such as Zeolite 13X at 303 K [43], \(\beta\)-zeolite at 303 K [44], and MCM-41 at 298 K [25]. The best result obtained was by [PERSON], P.D., using the zeolite 13X, 0.0550 g CO\({}_{2}\) being adsorbed per g of adsorbent. The addition of amine, that has an affinity with CO\({}_{2}\)[5] to the zeolite does not increase the quantity of CO\({}_{2}\) adsorbed.  
The best CO\({}_{2}\) adsorption amount obtained in this study (SILCLX20: 0.35 g CO\({}_{2}\)/g adsorbent) is approximatively 500 and 200% higher than when the zeolite 13X and (MeO)\({}_{3}\)Sipmim.Cl supported in (MCM-41 (SILCLM50, see Figure 4) are used as an adsorbent, respectively.  
After the CO\({}_{2}\) adsorption, the PTGA was depressurized without heating. It was verified that the sample weight remains equal to its value before the CO\({}_{2}\) adsorption process. This result indicates that CO\({}_{2}\) was physically adsorbed, and that the adsorbent material was not modified during the adsorption experiments enabling its recovery.  
The higher thermal stability of the hybrid material and the possibility of their reuse in physical separation systems are properties that indicate these materials have the potential for CO\({}_{2}\) capture.
## 2 Literature Review and Hypotheses
### Flow Theory  
First introduced by [PERSON] (1975) [29], the concept of flow is described as an overall experience that people feel when fully involved in an activity. [PERSON] and [PERSON] (1996) showed that the characteristics of flow experience include: (1) a seamless sequence of responses facilitated by machine-interactivity; (2) intrinsic enjoyment; (3) a loss of self-consciousness; and (4) self-reinforcement [30]. [PERSON] and [PERSON] (2004) regarded flow as a psychological state in which people experience extreme enjoyment when they engage in an activity with total involvement, control, concentration and intrinsic interest [31]. [PERSON] and [PERSON] (2005) indicated that flow occurs when the skills and challenges of users exceeded the threshold values and reached a good match [32]. By contrast, users feel apathy when both skills and challenges are lower than the threshold values. They also showed that if their skills exceeded challenges, users felt bored. However, the users felt anxious when challenges exceeded their skills. Widely used studies in the context of information system and electronic commerce have shown that the online behavior of users is significantly affected by the flow experience. For instance, [PERSON] and [PERSON] (2004) found that people will likely play online games when they feel flow [31]. [PERSON] and [PERSON] (2005) explored the factors that affect the flow and how it affects the transaction intentions in the travel community [33]. [PERSON] (2013) indicated that the flow can facilitate the consumers' continuance usage of mobile payment services [34].  
In the online social media context, when users plan to participate in a brand community, they must have the skills to use the social media service and brand community, namely, the ability to read posts, as well as social skills, etc. They may also face challenges, such as operation difficulty and low response rate from others. Customers in a social network-based brand community must match both skills and challenges to obtain an enjoyable experience when they participate in a brand community.  
With the flow, customers experience extreme enjoyment when they participate in a brand community, and they may expect to obtain this enjoyable experience again. As such, they will continue to participate in a brand community. Thus, we propose that:  
**H1.**_Flow has a positive effect on customer's participation intention._