esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 6 Standardization of Inspection for AM  
As briefly aforementioned, complex AM geometries may not always be appropriate for conventional inspection instruments (CMMs, calipers). As a result, non-contact measuring techniques for AM component inspection will be more frequently included in GD&T standards [33].  
These methods include the following: (1) using laser or structured light scanners to provide accurate digital representations of the surface geometry of an item, allowing for thorough surface examinations; (2) optical 3D scanning, which projects fringe patterns onto a component's surface and uses cameras to record the reflected data to perform precise shape and dimension analyses without physical contact; (3) Computed Tomography (CT) scanning, which uses X-rays to capture internal and external geometries and enables both the identification of subsurface defects and the dimensional assessment of internal features that are otherwise unmeasurable with traditional methods. Therefore, standardization and benchmarking in GD&T for AM will increasingly be involved together with the mature development of Reverse Engineering techniques, allowing for the development of uniform methods for defining, measuring, and verifying tolerances specific to AM processes. These efforts will ensure consistency, interoperability, and reliability across industries using AM technologies. In general, there is a lack of standardization in terms of tolerancing dimensional stability for AM parts. This is mainly due to the rising of AM process-dependent deviations on manufactured parts, due to thermal distortions, warping, and shrinkage, depending on the different AM technologies and on the wide range of materials used to produce the parts. To confront these issues, some efforts are currently in development, based on the standard ISO/ASTM 52910:2018 [34], providing a set of guidelines and recommendations for using AM in product design. However, more studies are expected in the future regarding this important topic.
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 3 GLS Encryption Using Physical Networked Dynamic
### GLS Secrecy Rate  
The secrecy rate of the GLS encryption is the channel capacity subtraction between legitimate (Tx,Rx) pairs and the wiretap channels. Here, we consider a simplified passive eavesdropper for secrecy rate computation, where the eavesdropping can only happen by intercepting the transmitted/received signal at Tx or Rx. Other sophisticated attackers (e.g., an active attacker) are examined in Section 3.3 and in the simulations.  
Figure 1: Illustration of graph layer security, where the wireless communications between (Tx,Rx) node pair is secured by the wireless channel-irrelevant physical networked dynamics. The encryption and decoding are leveraging the linear dependency of networked dynamics. (**a**) Shows the GLS schematic flow; (**b**) presents the illustration of the physical networked dynamic and the linear dependency among (Tx,Relays,Rx).  
Given Equations (3)-(5), the legitimate channel capacity of the (Tx,Rx) communication node pair can be expressed as:  
\[C_{\text{Tx,Rx}}=\log_{2}\left(1+\frac{\mathbb{E}(\mathbf{s})^{2}}{\|\mathbf{X}^{T} \cdot\mathbf{\omega}^{(\text{Tx,Rx})}\|_{F}^{2}+\|\mathbf{\alpha}^{(\text{Tx,Rx})}\|_{2 }^{2}\cdot\sigma^{2}}\right), \tag{6}\]  
In Equation (6), \(\mathbb{E}(\mathbf{s})\) accounts for the expectation of the information. \(\mathbf{\alpha}^{(\text{Tx,Rx})}=[\mathbf{\alpha}_{1}^{(\text{Tx,Rx})},\cdots,\mathbf{ \alpha}_{N}^{(\text{Tx,Rx})}]^{T}\) is the stacked weight vector of Tx node, selected relays, and Rx node, with zeros for unexpected nodes. \(\|\mathbf{X}^{T}\cdot\mathbf{\omega}^{(\text{Tx,Rx})}\|_{F}^{2}\) (with the Frobenius norm \(\|\cdot\|_{F}\)) therefore represents the residual energy after the process of Tx, selected relays, and Rx. \(\|\mathbf{\alpha}^{(\text{Tx,Rx})}\|_{2}^{2}\cdot\sigma^{2}\) gives the weighted variance of the physical measuring noise induced by the dynamic extraction sensors, whose sampling noise is assumed to follow the Gaussian distribution with zero mean and variance as \(\sigma^{2}\).  
Similarly, the channel capacity from Tx to an eavesdropper that is proximate at Tx and Rx can be formulated as follows:  
\[C_{\text{eav}}^{j}=\log_{2}\left(1+\frac{\mathbb{E}(\mathbf{s})^{2}}{\|\mathbf{ \alpha}_{j}^{(Tx,Rx)}\cdot\mathbf{X}_{j,\cdot}\|_{2}^{2}}\right),\ \ j\in\{Tx,Rx\}. \tag{7}\]  
In Equation (7), we remove the noise component to achieve an upper-bound eavesdropping channel capacity, which simplifies the following secrecy rate analysis for relay node selection and their weight computation.  
With the help of the legitimate and wiretap channel capacities in Equations (6) and (7), the secrecy rate is specified as their difference:  
\[R_{\text{Tx,Rx}}=\left[C_{\text{Tx,Rx}}-\max_{j\in\{T_{x},Rx\}}C_{\text{eav} }^{j}\right]^{+}, \tag{8}\]  
where \([x]^{+}=\max(x,0)\). Then, we will elaborate how to select the relay nodes and their weights by maximising the secrecy rate \(R_{\text{Tx,Rx}}\), in the absence of the networked dynamics \(\mathbf{X}\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADCP Acoustic Doppler Current Profiler  
AIS Automatic Identification System  
ASCII American Standard Code for Information Interchange  
ASM Application Specific Messages  
COG Course over Ground  
CPU Central Processing Unit  
CSV Comma Separated Values  
DP Dynamic Positioning  
DSC Digital Selective Calling  
ECDIS Electronic Chart Display and Information System  
EGC Enhanced Group Call  
EM Electromagnetic  
EPIRB Emergency Position Indicating Radio Beacon  
ETA Estimated Time of Arrival  
GMDSS Global Maritime Distress and Safety System  
GMSK Gaussian Minimum Shift Keying  
GPS Global Positioning System  
GT Gross Tonage  
HCS Heading Control System  
HF High Frequency  
IEC International Electrotechnical Commission
## 2 Materials and Methods
### SWAT Model Statistical Evaluation  
We used four metrics for statistical model evaluation to compare the measured and predicted CO\({}_{2}\) emissions. The R\({}^{2}\), NSE, RMSE, and the PBIAS were used to determine SWAT model performance [45].  
\[\mathrm{R}^{2}=\left[\frac{\sum_{i=1}^{n}\left(\mathrm{O}_{i}-\mathrm{\tilde{O}} \right)\left(\mathrm{E}_{i}-\mathrm{\tilde{E}}\right)}{\sqrt{\sum_{i=1}^{n} \left(\mathrm{O}_{i}-\mathrm{\tilde{O}}\right)^{2}}\sqrt{\sum_{i=1}^{n}\left( \mathrm{E}_{i}-\mathrm{\tilde{E}}\right)^{2}}}\right]^{2} \tag{11}\]  
where n is the number of observations, O\({}_{i}\) is observed value, E\({}_{i}\) is estimated value, \(\mathrm{\tilde{O}}\) is the mean of observed values, \(\mathrm{\tilde{E}}\) is the mean of estimated values, and i is the counter for individual observed and predicted values. R\({}^{2}\) values range between 0 and 1, where 1 indicates that the predicted value is equal to the observed value and 0 means that there is no correlation between the predicted and observed values.  
\[\mathrm{NSE}=1-\frac{\sum_{i=1}^{n}\left(\mathrm{E}_{i}-\mathrm{O}_{i}\right) ^{2}}{\sum_{i=1}^{n}\left(\mathrm{O}_{i}-\mathrm{\tilde{O}}\right)^{2}} \tag{12}\]  
The range of NSE lies between \(-\infty\) and 1.0 with NSE = 1 describing a perfect fit. Values 0 to 1.0 are generally viewed as acceptable levels of performance (the closer to 1.0, the better the model), whereas values <0 indicate that the mean observed value is a better predictor than the model. For biophysical (hydrological) models, NSE > 0.5 is generally considered good [46].  
\[\mathrm{RMSE}=\sqrt{\sum_{i=1}^{n}\frac{\left(\mathrm{O}_{i}-\mathrm{E}_{i} \right)^{2}}{n}} \tag{13}\]  
where RMSE is the root mean square error, \(\mathrm{O}_{i}\) is the measured datum, \(\mathrm{E}_{i}\) is the estimated value, \(\mathrm{\tilde{O}}\) is the mean of the observed values, \(\mathrm{\tilde{E}}\) is the mean estimated values, and n is the number of observations.  
\[\mathrm{PBIAS}=\frac{\sum_{i=1}^{n}\mathrm{O}_{i}-\mathrm{E}_{i}}{\sum_{i=1}^ {n}\mathrm{O}_{i}}\mathrm{x}\ 100 \tag{14}\]  
where PBIAS is the percent bias, \(\mathrm{O}_{i}\) is the measured datum, \(\mathrm{E}_{i}\) is the estimated value, \(\mathrm{\tilde{O}}\) is the mean of the observed values, \(\mathrm{\tilde{E}}\) is the mean of estimated values, and n is the number of observations. The optimal value of PBIAS is 0, with low-magnitude values indicating accurate model simulation [46].
## 4 Performance Evaluation
### Performance of the Proposed DPC for Different Channel Environments in the Absence of Cellular Users
Figures 15 and 16 show the SE results of the proposed scheme for \(\alpha=5\) in Rayleigh fading channels and \(\alpha=3.8\) in Nakagami-\(m\) fading channels with \(m=3\), respectively, when the cellular users are out of the D2D communication area, and \(P_{c}=23\) dBm, where the distance between the center of the D2D communication area and all the cellular users is equally 1.5D. In these figures, the SE performance becomes worse as \(U\) increases, and the performance loss is larger for Nakagami fading channels than Rayleigh fading channels, as seen in Figures 13 and 14. However, in Figure 15, the performance loss is relatively small when \(D=40\), because the distance between D2D communication area and the cellular users is longer than the other simulation cases. Figure 12: Index of fairness of the conventional and proposed schemes for \(\alpha=3.8\) in Nakagami-\(m\) fading channels when \(m=3\). It is noted that the perfect IF is achieved for all of the simulation cases and, thus, the IF results are not shown in this section. Figure 14: The average spectral efficiency of the proposed scheme for \(\alpha=3.8\) in Nakagami-\(m\) fading channels with \(m=3\) when the cellular users are in the D2D communication area. Figure 13: Average spectral efficiency of the proposed scheme for \(\alpha=5\) in Rayleigh fading channels when the cellular users are in the device-to-device (D2D) communication area.
## 4 Results and Discussion
### Challenges
#### 4.2.1 Social Challenges  
Social challenges of digital payment technologies are associated with social influence, which is connected to a person's feelings that their friends and acquaintances think that they must use certain technologies. The behavior of individuals is influenced by their social circle, as this may preserve interrelationships and social harmony [91]. Out of 58 studies, 20 studies mentioned social challenges associated with digital payment technologies, such as trust, impersonalization (no human touch), risks, adoption, demands, culture, satisfaction, loyalty, social media impact, user perception and acceptance by age group (see Table 6). For example, [PERSON] et al. [59] investigated the joy of cheques in the UK. Their qualitative study that explored the banking experiences of 23 people over eighty years old found that the case for retaining and enhancing physical cheques is stronger. Thus, there was a challenge of \"acceptance by age group\" and rather than replacing cheques with e-cheques, designing ways of making them less costly to process was the only option [59].  
The challenges of \"trust\" for the emerging digital payment technologies were highlighted by several studies. Trust refers to the accumulation of customer beliefs of integrity, benevolence and ability that could increase customer willingness to use the technology easily. [PERSON] et al. [12] investigated \"trust\" as one of the main barriers to emerging digital payment technologies. They found that security concerns related to e-banking and mobile banking, i.e., monetary loss due to transaction error or bank account misuse, led to a \"lack of trust\". People see more risks in mobile devices, as they can be misplaced or stolen and may result in the exposure of confidential information [12]. Similarly, it was also confirmed that overcoming the issue of lack of trust is the most challenging task for the digital wallet service provider. A study by [PERSON] and [PERSON] [62] investigated customer satisfaction with digital wallets and found that high risk and uncertainty are involved in digital payment systems. The study further highlighted that in e-transactions, trust is affected by design aesthetics, usefulness, ease of use and customization [62]. Thus, as pointed out by [PERSON] et al. [37], despite being increasingly popular and ubiquitous for transactions and having relatively high security, consumers still perceive digital payments as risky, unsecure and untrustworthy [37].
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
###### Abstract  
Arsenic and heavy-metal-contaminated environments are a major concern due to their negative impacts on exposed people and ecosystems. In this study, sludge from an iron-ore processing area was used as an adsorbent to remove As, Mn, Zn, Cd, and Pb from aqueous solutions. The adsorption capacity of target adsorbates was investigated in batch experiments of both single- and mixed-metal solutions. The batch studies show that the maximum Langmuir adsorption capacities of the heavy metals onto the adsorbent occurred in the order Pb > As > Cd > Zn > Mn, and ranged from 0.710 mg/g to 1.113 mg/g in the single-metal solutions and from 0.370 mg/g to 1.059 mg/g in the mixed-metal solutions. The results of the kinetic experiments are consistent with pseudo-first-order and pseudo-second-order models, with a slightly better fit to the latter. Adsorption performances indicate that iron-ore sludge can simultaneously adsorb multiple metal ions and is a promising adsorbent for the removal of toxic pollutants from water.  
adsorption; arsenic; heavy metal; removal; unmodified iron ore sludge Article
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Cut-Point Identification Failure  
Cut-point identification is of great importance in chunking. The determination of appropriate points to divide a data stream into manageable segments increases the efficiency of the scheme. If an appropriate breakpoint is not achieved, this results in the imprecise evaluation of patients and also increases the processing time and computational cost. To check the performance of the DICA, a simulation was performed regarding this metric. Figure 5 shows the likelihood of failure in finding the most appropriate breakpoint within chunking. Sometimes, the optimum breakpoint is missed due to incorrect identification measures. A multiplier (\(\psi\)) is employed to determine the computational cost. The DICA maintains the minimum cut-point identification failures as it splits the data string into tokens, evaluates the sizes of chunks, and determines the breakpoint by dynamically adjusting the chunk size based on the window size criteria. By refining the breakpoint accuracy, the overall data reduction and transmission efficiency are enhanced in the proposed technique. In a situation in which the chunk size is larger than the defined threshold, the scheme navigates through the data stream to ensure appropriate cut-point identification.  
Figure 4: Performances of all schemes under IC and fixed-sized windows.  
The RAM scheme has fewer cut-point identification failures; it employs a window of fixed size situated at the start of the data chunk. The RAM focuses on finding greater data values within the window. It iteratively finds a byte greater than the existing maximum value, which is determined as the breakpoint for a specific chunk. There is a high probability that the preceding byte will be smaller. The CWCA and WCA-BF + ASE schemes achieve better cut-point identification. In the DSW approach, if a breakpoint based on a delimiter is not found, the algorithm might cut an arbitrary position. This could result in splitting a data reading into two parts, potentially rendering the value incomplete and unreadable. In the healthcare sector, when data values are split, the resulting chunks might contain incomplete or corrupted information, making it unreadable or unusable. For instance, blood pressure measurement data could be divided in such a way that neither chunk contains the full value, making the data meaningless. The x-axis shows the multiplier (\(\psi\)), whereas the vertical axis illustrates the likelihood of finding no breakpoints in the string. When \(\psi\)=4, the DICA has the lowest probability of 0.38, while the RAM, CWCA, WCA-BF + ASE, and DSW attain probabilities of 0.55, 0.42, 0.45, and 0.54, respectively.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 5 Conclusions  
In this paper, a new model based on LVMD-DBFCM load curve clustering and a CNN-IVIA-BLSTM hybrid model for power load forecasting is proposed. This comprehensive technique takes historical load data and influencing factors (meteorology, economy, and data type) into account, where historical load data, meteorological factors, and economic factors are normalized, and the data types are uniquely heat coded.  
The novel LVMD-DBFCM algorithm improves the continuity and stability of the data, and the values of the CHI and DBI quality assessment indicators are 1142.509 and 1.083, respectively, both of which reflect the good validity of the clustering method used in this paper. In the new CNN-IVIA-BLSTM model, a CNN is used for feature extraction, BLSTM is used for load forecasting, and the IVIA is used to optimize the relevant parameters in the BLSTM network. The results of the three electric load forecasting evaluation metrics of the hybrid forecasting model show that the RMSE is 31.9942, the MAE is 23.3691, and the MAPE is 1.6421%. The prediction effect of the electric load fits well with the target, and the prediction error is minimized.  
Conceptualization, L.H., J.W., Z.G. and T.Z.; Data curation, L.H., J.W., Z.G. and T.Z.; Formal analysis, L.H., J.W. and T.Z.; Investigation, L.H; Methodology, L.H. and J.W; Software, J.W. and Z.G.; Project administration, L.H.; Resources, L.H.; Supervision, L.H.; Validation, L.H.; Visualization, L.H.; Writing--original draft, [PERSON], J.W., Z.G. and T.Z.; Writing--review & editing, [PERSON] and Z.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions and Outlooks  
We established a novel problem setting for continually updating FMs using model merging methods and analyzed the effectiveness of various merging approaches. Our findings demonstrate that although model merging achieves higher accuracy than simple averaging, explicit mechanisms to mitigate knowledge forgetting are essential for preserving previously learned information. Furthermore, we identified hyperparameter dependence as a critical bottleneck that limits the scalability of model merging methods. In our experiments, each client fully fine-tuned the image encoder of the CLIP model to enable a fundamental analysis of the proposed framework. However, given the numerous parameters in large-scale models, full fine-tuning on resource-constrained clients is impractical. As an alternative, parameter-efficient fine-tuning (PEFT) methods [57], such as LoRA, are typically employed to reduce computational costs. In future work, we plan to incorporate PEFT into our framework and investigate model merging strategies that remain effective under resource-constrained settings while maintaining computational efficiency. In addition, we plan to extend our experimental environment to more realistic scenarios in order to further investigate the limitations of conventional model merging methods. A key future direction is the development of new merging techniques that can dynamically assign aggregation weights to fine-tuned models without relying on explicitly defined evaluation data, thereby enhancing adaptability and robustness in distributed learning environments. We also aim to evaluate these techniques under heterogeneous client conditions, such as varying data distributions and resource constraints, to better validate their practical effectiveness. Conceptualization, K.K., R.T., K.M. and T.O. ; methodology, K.K., R.T., K.M. and T.O. ; software, K.K. ; validation, K.K. ; data curation, K.K. ; writing--original draft preparation, K.K. ; writing--review and editing, K.K., R.T., K.M. and T.O. ; visualization, K.K. ; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript. This research was supported in part by JSPS KAKENHI Grant Numbers JP24K02942, JP23K21676, JP23K11211, and JP23K11141. **Infrared Consent Statement:** Not applicable. **Data Availability Statement:** A publicly available datasets were used in this work.
## 1 Introduction  
Since its first development, flow cytometry (FCM) has become a reliable tool to study virus-cell interactions and, among its applications, the quantification of cellular antigens gained popularity in the early 80s [1; 2]. This methodology, known as quantitative flow cytometry (qFCM), is defined as \"the calibrated measurement of fluorescence intensity from labelled particles to determine the actual number of fluorescent ligands labelling each particle\" [3]. Different clinical studies using qFCM have been published [4], including applications in viral research, like monitoring the multiplicity of infection of a virus during vaccine production, the evaluation of protein heterogeneity in the Herpes simplex virus, or the evaluation of cell antiviral response to _Rhinovirus_[5; 6; 7].  
FCM has also been extensively used in fish virology, for the screening of antibody libraries [8], for the detection of different viruses such as IPNV [9; 10; 11; 12; 13], the _Lymphocystis virus_[14], the _Infectious haematopoietic necrosis virus_ (IHNV) [15] or the _Iridovirus_[16], and in viral replication studies [17; 18; 19; 20; 21]. However, to our knowledge, its use for real quantitative purposes has never been reported.  
Infectious pancreatic necrosis is an important disease affecting salmonid aquaculture. The causative agent, the _Infectious pancreatic necrosis virus_ (IPNV), is an _Aquabirnavirus_, belonging to the family _Birnaviridae_. Members of this family have a genome composed of two segments of double-stranded RNA (named A and B), and a naked icosahedral single-shelled capsid. Segment A encodes a 105 KDapVP2-NS-VP3 polyprotein, which is cotranslationally processed and cleaved into the structural VP2 and VP3 viral proteins, and the NS viral protease (also known as VP4) [22]. This segment also encodes a 17 KDa non-structural protein called VP5 whose biological function still needs to be determined [23]. Segment B encodes VP1, the viral RNA-dependent RNA-polymerase (RdRp) [24]. Since VP2 elicits neutralizing antibodies and carries serotype-specific and group-specific antigenic determinants, this protein is important for the development of subunit vaccines, for diagnosis and for serological typing.  
Different applications of FCM have been employed for this virus, both for the detection of the virus in different cells and for in vitro and in vivo viral replication studies; although in some of those studies FCM was used to determine the level of production of certain viral proteins, a quantitative approach of this technique has yet to be validated [17; 20; 25; 26].  
The objective of the present study has been the design and evaluation of a qFCM procedure for the analysis and quantification of VP2 proteins expressed in in vitro infected bluegill fry (BF-2) cells. We have also adapted the method for analysing RNA following intracellular sorting (MARIS) reported by [PERSON] et al. [27] for the simultaneous quantification of the viral RNA expression in infected cells.