esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Register Value Branch Predictor Based on CNN
## 4 Reasons for Introducing Register Values  
Paper [6] points out that simply increasing the size of the predictor does not improve the H2P branch prediction very well. Two reasons exist for this: first, H2P branches have high history variation and have no stable behavioral mode, which leads to much of the overhead used being wasted. The second is that some rare branches are not stable enough for the predictor to grasp their behavior and give correct predictions, which also leads to the uselessness of increasing the size. New prediction mechanisms are needed to address these H2P branches.  
The data-dependent branch which showed in Figure 3 is the main variety of H2P branching, and there have been some previous studies in dealing with this problem. In the paper [22], the data-dependent branch is proposed to be solved by recording the dependency chain of branch instructions, and it is pointed out in the paper that if a branch has the same register value as the previous branch in the dependency chain, the jump result of the previous branch can also be used in the current branch. Paper [23] designed a runahead mechanism based on the dependency chain to solve the data-dependent branch jump by running instructions in advance.  
All the above methods use some additional structure to handle data-dependent branches, but for CNN-based branch predictors, adding register value information in offline training mode is very easy and does not require additional mechanisms or struc  
Figure 3: Simple data dependency branch.  
tures. For BranchNet, an additional data path just needs to be added to handle register values and then extract the register values saved in the trace file during the training process.  
The question of whether there is a regularity in the statistical distribution of register values is addressed in the paper [6]. The authors studied the relationship between register values and the number of instructions in the SPECint2017 benchmarks, where the authors recorded the lower 32 bits of the register values for the 18 registers. By observing the results, the authors have two conclusions: (1) The distribution of register values varies between different benchmarks. (2) The distribution of register values is complex, but a recognizable structure can be found. So, we can say that the CNN has the ability to recognize the underlying patterns.  
In summary, the current predictor works in a way that makes it difficult to predict H2P branches represented by data-dependent branches, whereas neural network branch predictors have an excellent ability to uncover potential pattern matches and it is easy to incorporate register information into the offline training process. Therefore, we believe that it is feasible and effective to introduce register values to the neural network branch predictor training process.
## 5 Possibilities of Reducing the Acrylamide Content in French Fries and Potato Chips
### Technological Factors
#### 5.2.4 Frying of Potato Pieces  
In the production of potato snacks, the main stage of thermal processing is frying, where the applied parameters, i.e., time and temperature, have the greatest influence on the amount of acrylamide in ready snacks [7; 8; 9; 10; 49; 52; 58; 64; 68; 75]. The higher the temperature of this process and the longer its duration, the more acrylamide can be formed in the ready product [10; 49; 52; 131]. There are many different factors associated with the frying process that shape the amount of the toxic compound in potato snacks, among which the type of oil used in the process is particularly important [32; 68; 79]. French fries fried in different oils had varying amounts of acrylamide. It was most abundant in samples fried in palm oil compared to rapeseed and sunflower oils [44; 132]. Diversified content of the toxic compound was also characteristic for the potato chips obtained after frying potato slices in the following oils: palm olein, coconut oil, canola oil, and soybean oil. The highest amount of AA was found after using palm olein--2019 \(\mu\)g\(\cdot\)kg\({}^{-1}\)[133]. Heating different types of oil at 180 \({}^{\circ}\)C, resulted in a clear variation in the amount of acrylamide, the highest amount of this compound was in soybean oil (2447 \(\mu\)g\(\cdot\)kg\({}^{-1}\)), followed by palm olein > sunflower > sesame > olive > corn (430 \(\mu\)g\(\cdot\)kg\({}^{-1}\)) [134].  
Other factors related to the frying process also include the type of fryer used (traditional or vacuum) [32; 135; 136], degree of oil degradation [3; 137], the percentage ratio of oil used to sample [49; 138], and the size of the French fries and the thickness of the potato chips [68; 80; 131].
## 4 GNN-Based Digital Twins for Next-Generation Networks
### Major Benefits of Using GNN-Based Digital Twins in Next-Generation Networks
#### 4.1.1 Network Optimization  
In the context of a Software-defined network (SDN), the integration of an NDT within the control plane enables dynamic optimization of network operations based on insights gained from the digital twin [34]. The synchronization between network systems and their digital replicas allows for real-time updates and accurate representation of the network state. Moreover, the centralized control plane enables the NDTs to capture the entire network topology, providing the collected data with the semantic relationship between network elements, and allowing the control plane the making of optimal decisions based on a holistic  
Figure 7: Illustrations of three major use cases of a Graph neural network-based network digital twin (GraphNDT) including (from left to right): network optimization (yellow), low-cost trials (green), and predictive maintenance (blue).  
view of the network. To effectively leverage the graph structure of the network, NDTs can leverage GNNs. GNNs are well-suited for capturing the dependencies and relationships between network elements, as they can learn from the graph structure and features associated with each element. On the one hand, GraphNDTs equipped with optimization algorithms can function as orchestration components within the control plane [34; 122]. They utilize network states collected from the synchronization link and leverage GNN models to propose optimal actions based on the gathered information. By analyzing the real-time data from the network using GNN techniques, GraphNDTs can optimize various aspects such as resource allocation, routing decisions, or traffic management to improve network performance (see Section 3). On the other hand, GraphNDTs can also serve as components for validating optimization policies generated by other algorithms [1; 109]. Once an optimization policy is generated by an external algorithm, the NDT can simulate its implementation and evaluate its effectiveness within the digital twin environment. By comparing the outcomes of the generated policy with the expected results, the NDT can provide feedback on the efficacy of the optimization policy. This dual functionality of GraphNDTs--acting as optimizers and validators--makes them versatile tools for network optimization.
## 3 Findings
### Feedback on App Usability and Workflow Modifications  
The field workers learned quickly how to use the smartphones and swiftly became comfortable with the application's workflow: most of them were already using smartphones in their personal lives. Nevertheless, the implementation of ODK Scan was guided by the principles of minimal disturbance in current workflow and generating value for users to improve the chances of adoption. Face-to-face interviews were conducted independently with each of the field workers at the end of the intervention period. These interviews were supported by a semi-structured questionnaire, which included hints to guide the interviewer to ensure the completeness of the feedback. The interviews were conducted by a research team member in the language of the enrolled field workers (Urdu). All interviews were digitally recorded, transcribed in Urdu and then translated into English. The interview recordings and the corresponding translations were then checked by another team member for consistency and completeness. Data is segregated based on the various steps of the app workflow for the ease of analysis and presentation.
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 3 Methods
### Movement Prediction
This has the effect of increasing the network depth, so it can efficiently predict images or video where sudden changes occur. The green-marked \(H_{t}^{k}\), blue marked \(H_{t}^{k}\) and red \(H_{t}^{k}\) are desired output of convolutional LSTM, stacked LSTM, and cascade LSTM respectively. The final output of cascade LSTM, \(H_{t}^{k}\), is more suitable for non-linearity data processing and prediction than convolutional LSTM and stacked LSTM outputs. Since most animal movements have non-linearity, the structure of the cascade LSTM is useful for predicting animal movements. Figure 6: Structural differences of (**a**) convolutional stacking LSTM; (**b**) PredRNN; (**c**) PredRNN++. Figure 7: Structural differences of convolutional LSTM, stacked LSTM, and cascade LSTM. \[\left( \begin{array}{c}\mathit{g}_{t}\\ \mathit{i}_{t}\\ \mathit{f}_{t}\end{array} \right)=\left( \begin{array}{c}\mathit{tanh}\\ \sigma\\ \sigma\end{array} \right)W_{1}*\left[\mathit{X}_{t},\;H_{t-1}^{k},\;C_{t-1}^{k}\right] \tag{2}\]  
\[C_{t}^{k}=f_{t}\;\odot\;C_{t-1}^{k}+\;i_{t}\;\odot\;g_{t} \tag{3}\]  
\[\left( \begin{array}{c}\mathit{g^{\prime}}_{t}\\ \mathit{i^{\prime}}_{t}\\ \mathit{f^{\prime}}_{t}\end{array} \right)=\left( \begin{array}{c}\mathit{tanh} \\ \sigma\\ \sigma\end{array} \right)W_{2}*\left[\mathit{X}_{t},\;C_{t}^{k},\;M_{t}^{k-1}\right] \tag{4}\]  
\[M_{t}^{k}=f^{\prime}_{t}\;\odot\;\tanh\left(W_{3}*M_{t}^{k-1}\right)+\;i^{ \prime}_{t}\;\odot\;g^{\prime}_{t} \tag{5}\]  
\[o_{t}=\left(W_{4}*\left[\mathit{X}_{t},\;C_{t}^{k},\;M_{t}^{k}\right]\right) \tag{6}\]  
\[H_{t}^{k}=o_{t}\;\odot\;\tanh\left(W_{5}*\left[C_{t}^{k},\;M_{t}^{k}\right]\right) \tag{7}\]  
Equations (2)-(7) present the operating of cascade LSTM, where \(*\) and \(\odot\) indicates the operator of the convolution and the Hadamard product, \(\sigma\) is the Sigmoid function. The square brackets present concatenation of each tensor. The PredRNN++ used convolutional filters \(W_{1-5}\), where \(W_{3}\) and \(W_{5}\) has one by on convolution filer. The basic equations derived from standard LSTM [43] and the difference of standard LSTM and PredRNN++ is number of hyperbolic tangent layers and existing spatiotemporal memory cells. Equations (2) and (3) present standard LSTM gates operating equations, and Equation (4) is spatiotemporal gates update equation regulated by previous spatiotemporal memory, \(M_{t-1}^{k}\), and current state, \(C_{t}^{k}\). Equations (5) and (6) shows current spatiotemporal memory, \(M_{t}^{k}\), update operation. Final output, \(H_{t}^{k}\), is computed based on dual memory states \(C_{t}^{k}\) and \(M_{t}^{k}\).
# The Online Reservation Problem
## 1 Introduction
### Our Approach  
We first find a lower bound on the CR of any algorithm in \(\mathcal{A}\) in Section 2, proving that no algorithm can have a CR better than \(\mathcal{O}(\ln\Delta)\). Subsequently, in Section 3.1, we analyze the CRs of all greedy algorithms (including those that are randomized), which are those that do not decline a request whenever a server is available. We study greedy algorithms because they are intuitive and have been studied in the context of other problems, such as the bin-packing problem [11]. We prove that any greedy algorithm is \(\Theta(\Delta)\)-competitive. The gap between the CRs of the greedy algorithms and the best possible CR motivates the need for better algorithms. The linear dependence of the CRs of greedy algorithms on \(\Delta\) arises because, in some instances, they end up accepting jobs with duration  
Figure 1: Examples of problem instances with four jobs each under the online reservation problem (ORP) and the special case of the ORP (SORP) settings.  
\(D_{min}\) and declining those with duration \(D_{max}\). Therefore, we design two new algorithms while using the intuition that reserving capacity for higher-value future jobs may help to improve the CR and that this may be operationalized while using thresholds. We first propose a deterministic algorithm, Algorithm \(D\), in Section 3.2, which uses deterministic server-specific admission thresholds. We find the upper bound on the CR of Algorithm \(D\) and prove that the algorithm is \(\mathcal{O}(\ln\Delta)\)-competitive in the limit \(n\to\infty\). We also propose a randomized algorithm, Algorithm \(R\), in Section 3.3, which uses a randomized threshold. We prove that Algorithm \(R\) is \(\mathcal{O}(\ln\Delta)\)-competitive for any \(n\) and \(\Delta\), and it is therefore optimal in the sense that no algorithm can be better than \(\mathcal{O}(\ln\Delta)\)-competitive. For the SORP, we follow the same approach as we do for the ORP.  
Although we summarized our approach and results in terms of the big \(\mathcal{O}\) notation in the previous paragraph, we actually derive the exact values of all the CR bounds as functions of \(n\) and \(\Delta\). Being able to design algorithms that are \(\mathcal{O}(\ln\Delta)\)-competitive is important from a theoretical point of view, but our focus while designing Algorithms \(D\) and \(R\) is to go a step further and achieve CRs that have small numerical values. We do so because this paper is motivated by practical applications and an owner in a real-life setting will choose to use an algorithm for ORP/SORP only if the algorithm's CR is small for his/her particular \(n\) and \(\Delta\), regardless of the limiting behavior of the CR.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Procedure  
The experiment was divided into four blocks according to SNR (quiet plus three masked conditions). Within each block, listeners were presented with five trials for each of the four features being modified, for a total of 20 trials. The presentation order of the 20 trials was randomised, meaning that listeners would typically adjust different features on consecutive trials (i.e., there was no blocking by feature type).  
Each trial consisted of an adjustment phase followed by a test phase. In the adjustment phase, sentences were presented in a random order, with a 0.5 s gap between sentences. In order to prevent listeners from simply selecting the same adjustment value as on the previous trial, the modification value applied to the initial sentence in the adjustment phase was randomly selected from the available steps. Participants could listen to as much speech as desired during the adjustment phase but they were forced to listen to at least 5 s of speech before proceeding to the test phase. Listeners made their adjustments using on-screen buttons labelled with up and down arrows. They were instructed to \"use the buttons to alter the speech in order to recognise as many words as possible\". In the test phase of each trial, intelligibility was evaluated by a speech perception task using the value chosen at the end of the adjustment phase. Participants listened to two sentences and typed what they heard into an on-screen text box immediately after presentation of each of the two sentences. Prior to the experiment, participants underwent a task-familiarisation phase consisting of five trials (two in quiet and three in noise), using sentences from the adjustment set i.e., different from those used in any test phase.  
Block ordering was counterbalanced across listeners using a balanced Latin square design. Stimuli were presented through Sennheiser (Wedemark, Germany) HD380 headphones at a fixed presentation level, different for each condition (approximately 84, 79, 77 and 75 dB SPL for the \(-6\), \(-3\), 0 dB SNR and quiet conditions, respectively) as measured by a Bruel and Kjaer (Naerum, Denmark) type 4153 artificial ear and a Bruel and Kjaer type 2260 sound level meter. Listeners were seated in a sound-attenuating booth in a purpose-built speech perception laboratory at the University of the Basque Country (Vitoria Campus).
## 4 Experiments and Evaluation  
### Experiment Setting  
Table 2 summarizes our study on DPFL to train ML models cooperatively while maintaining user privacy using two real-world data sets:  
* Opinion Data: A survey of the Afghan People Opinion (2018) [52] was carried out by the Asia Foundation, which is an international development organization that has worked extensively in Afghanistan and focuses on issues such as leadership, justice administration, security, and economic growth. The survey aimed to determine how people felt about various aspects of the country's progress and governance.
* Payroll Data: The data set is provided by the Afghan Ministry of Education and provides the individual information of each employee in the educational institution payroll system of the provinces [53]. It includes unique sensitive identification, such as names, district and school, specific fields of study, and more. In particular, attributes also include personal and professional details such as gender, marital status, contract type, position, grade, and step, as well as financial information such as bank account numbers and salaries. The most important attribute in our analysis is _Attribution_, which indicates whether an employee has left the institution.  
A separate FFNN with two dense layers as the attack model is also used to evaluate the resistance of our models against potential MIAs to validate the approach. The incorporation of a noise factor as a regularization parameter is used to improve the resistance against the mentioned attacks. Although the shadow data set was divided into shadow train and shadow test sets, the normal data set was used for both model training and defense mechanism evaluation. The latter set was then used to build shadow models, which were used in the attack simulation. The effectiveness of the attack and defense mechanisms was assessed using accuracy and F1 score as metrics.  
Section 3.2 presents the key concepts of our methods and experiments to evaluate the trade-off between privacy and performance in DPFL. A Gaussian mechanism was used to inform noise calibration and assess the influence on accuracy and privacy. RDP was used for privacy quantification. This direct conformity with established principles ensures that our conclusions are methodologically solid and relevant.
# Evaluation of Radio Communication Links of 4G Systems
## 4 Results and Discussion
### Reference Signal Received Power (RSRP)  
The RSRP is the received average power of the downlink signals. It is given by the following equation [21; 22]:  
\[\text{RSRP}=(1/\text{K})\text{$\sum$}\text{P}_{\text{rs,k}} \tag{11}\]  
where: (P\({}_{\text{rs,k}}\)) [Watts] is the power of the received signal.  
The RSRP provides important information about the strength of the received signal to determine optimal power settings for network operation. The most studied area problems are: quality of area coverage due to the geographic profile. In addition, we have inadequate coverage in this area due to the absence of a dominant cell. The obtained results of RSRP with the drive test before dealing with the problems are presented in Figure 7.  
Figure 6: Studied area.  
Under the normal state, when the received signal is not affected by the noise and interference, RSRP varies between [\(-100\), \(-80\)] dBm. In good condition, RSRP is higher than \(-80\) dBm. For these two states, no action will be done. If the value of RSRP is less than \(-100\) dBm, an activity of optimization is necessary. Several actions are possible: boost the power transmission, increase the height of the transmitter, and the implementation of new eNB if necessary. The obtained values of RSRP before and after treatment are given in Table 1:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Range** & \multicolumn{2}{c}{**RSRP (dBm)**} \\ \hline  & **Before Treatment** & **After Treatment** \\ \hline
[min, \(-120\)] & 28,24 (1.25\%) & 11 (0.18\%) \\
[\(-120\), \(-110\)] & 16,545 (7.31\%) & 308 (5.15\%) \\
[\(-110\), \(-100\)] & 59,294 (26.21\%) & 1,358 (22.7\%) \\
[\(-100\), \(-80\)] & 124,094 (54.85\%) & 3,476 (58.11\%) \\
[\(-80\), Max] & 23,473 (10.38\%) & 829 (13.86\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: RSRP values before and after treatment.  
Figure 8: Histogram’s distribution of RSRP (before and after treatment).  
Figure 7: RSRP distribution: (**a**) before treatment; (**b**) after treatment.  
The aim of this section is the amelioration (ensuring maximum possible coverage) of the received RSRP in the area where the power is low (three first cases in Table 1). According to Figure 8 and Table 1, there is an amelioration of 6.74% in RSRP distribution. These ameliorations are distributed as follows: amelioration of 1.07% in the range of [min, \(-120\)] dBm; amelioration of 2.16% in the range of [\(-120\), \(-110\)] dBm and amelioration of 3.51% in the range of [\(-110\), \(-100\)] dBm.
### The Present Study
#### 1.2.2 Definition of Low vs. High Mental States  
Three distinct psychological tasks were used which involve emotional and cognitive processes and have been designed and validated to elicit varying levels of arousal, valence, and mental load: Affective Norms for English Words (ANEW), International Affective Picture System (IAPS), and the \(n\)-Back task. These tasks were selected because they demonstrated reasonable validity in distinguishing a relative difference in low as opposed to high psychological states as defined by the subjective self-ratings of the participants and as reflected by the state-dependent changes in EEG correlated with those low vs high levels.  
ANEW is a set of 1034 English-language words associated with emotional values [48]. This dataset was made to complement the IAPS database described below [49; 50] to assess attentional and emotional processing in the auditory and verbal/linguistic domains. The words are assessed by self-ratings based on arousal, which goes from calm to excited, and valence, which ranges from unpleasant to pleasant. For example, \"war\" would evoke high arousal and low valence since it is stressful and displeasing, while \"rollercoaster\" would evoke high arousal and high valence due to its mostly pleasant and exciting nature [49]. Low level as opposed to high level of word arousal and valence has been previously demonstrated to alter cortical activity in ways measurable by EEG [51].  
The IAPS is a repository of 956 photos chosen to be standardized visual stimuli in experiments of emotion and attention. Just like the ANEW system, the photos are chosen based on their ability to cause emotions and are primarily classified using valence and arousal [52]. Photos can also be rated based on dominance, but the latter variable was not considered in this study. Similarly to the ANEW, this task can create noticeable differences in EEG in relation to changes from low as compared to high levels of arousal and valence, as previous experiments have been able to detect such differences in brain activity during image processing, depending on pleasantness and unpleasantness [53].  
The \(n\)-Back task was initially designed to include up to three items [54], and then upgraded to six items [55]. In this task, the participant is exposed to a stream of stimuli and must indicate whether the current stimuli matches the one \(n\) trials before [56]. For example, in a 1-back task, participants must indicate whether or not each letter matches the one right before; in a 3-back task, participants must indicate whether or not each letter matches the one 3 letters before; etc. The change in attention from low as opposed to high cognitive or mental load required for this task can be detected by EEG [57].