esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### THz Communication  
As the first wireless communication system with ultra-high-speed communication, the use of frequency bands of more than 0.1 THz is required to support features that demand data transfer rates in the unit of Tbps. The capacity of the RF band is inadequate to cater to the rapidly increasing global demand for wireless communication technology, which  
Figure 5: 6G key enabling technologies.  
has propelled the growing interest in THz communication [59] for the 6G network. The massive bandwidth resources in the THz region (which ranges from 0.1 THz to 10 THz) address the limitations of certain technologies in coping with high speed. Through the use of the THz band, the use of 6G can be optimized with significantly improved bandwidth, capacity, speed, and network security. Moreover, the THz band can keep up with the capacity of cells at nanoscale up to micrometer over 10 m without compromising the speed in communication [60] and supporting the Internet of nano-things (IoNT) [61; 62].
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 3 Proposed Method
### Determining the Model Parameters  
To describe the QoE with mathematical formulas, function fitting methods, such as the least square method and nonuniform rational basis spline (NURBS) fitting method, are usually used to determine the hyperparameter in the formulas. These function fitting methods work well on continuous-value data sets. However, it should be noted that MOS-based QoE scores are discrete integer values in the range of 1 to 5, directly applying these conventional fitting methods on QoE task may cause high computation error. To solve this issue, in this paper, we propose an adaptive hyperparameter calculation algorithm inspired by artificial neural network training method. Let \(\Omega\) be the dataset with \(N\) samples consists of the network QoS metrics \(<DL_{i},LR_{i},TH_{i}>\) and its labeled QoE score \(QoE_{i}\), \(i=1,2,...,N\). According to (7), we define continuous-value variable  
\[S=\alpha\cdot exp(-\beta\cdot QoS)+\gamma \tag{8}\]  
where \(\bar{S},\bar{S}\) denotes the upper rounding and down rounding function of \(S\). With \(<DL_{i},LR_{i},TH_{i}>\), we can calculate the corresponding values of \(S_{i}\), \(\bar{S}_{i}\) and \(\bar{S}_{i}\). Then, define the predicted output vector \(O_{i}=[o_{i,1},o_{i,2},o_{i,3},o_{i,4},o_{i,5}]\), where \(o_{S_{i}}=\bar{S}_{i}-S\), \(o_{\underline{S}_{i}}=S-\underline{S}\), and the rest of the elements of \(O\) are set as 0. Further, define the target output vector \(T_{i}=[t_{i,1},t_{i,2},t_{i,3},t_{i,4},t_{i,5}]\) where \(t_{i,QoE_{i}}=1\) and the other elements of \(T\) are also set as 0. To set an example, for predicted output \(S=2.4\), and its target output \(QoE_{i}=3\), we have \(\bar{S}=3,\underline{S}=2\), then the predicted output vector \(O=[0,0.4,0.6,0,0]\) and the target output vector \(T=[0,0,1,0,0]\). Define the output error using cross entropy and L2 regularization error:  
\[loss_{i} = -\sum_{k=1}^{5}\big{(}t_{i,k}\log o_{i,k}+(1-t_{i,k})\log{(1-o_{ i,k})}\big{)}+l_{2}\cdot\sum_{k}\Theta_{k}^{2} \tag{9}\]  
where \(l_{2}\) is the regularization factor and \(\Theta\) denotes the set of all the hyperparameters.
# Path Optimization of Two-Posture Manipulator of Apple Packing Robots
## 5 Conclusions  
To solve the problem of the path optimization of manipulators moving from multiple fixed points to multiple varying points and with dual postures, an improved genetic  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**S**} & \multicolumn{3}{c}{**9-Groove**} & \multicolumn{3}{c}{**12-Groove**} & \multicolumn{3}{c}{**14-Groove**} & \multicolumn{3}{c}{**16-Groove**} \\ \cline{2-13}  & **T-F** & **T-T** & **O-A** & **T-F** & **T-T** & **O-A** & **T-F** & **T-T** & **O-A** \\
**No.** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** & **/s** \\ \hline
2 & 13.23 & 14.59 & 9.32 & 18.54 & 20.29 & 8.62 & 22.90 & 26.48 & 13.52 & 26.96 & 31.44 & 14.25 \\
3 & 13.77 & 15.84 & 13.07 & 19.08 & 22.30 & 14.44 & 22.32 & 26.96 & 17.21 & 28.18 & 32.46 & 13.19 \\
4 & 13.60 & 16.77 & 18.90 & 18.84 & 23.79 & 20.81 & 23.86 & 28.51 & 16.31 & 28.91 & 34.37 & 15.89 \\
5 & 13.22 & 16.07 & 17.73 & 19.32 & 23.23 & 16.83 & 23.52 & 28.15 & 16.45 & 28.22 & 31.60 & 10.70 \\
6 & 14.02 & 16.33 & 14.15 & 19.71 & 23.58 & 16.41 & 24.02 & 29.19 & 17.71 & 28.54 & 33.62 & 15.11 \\ ave. & 13.57 & 15.92 & 14.63 & 19.10 & 22.64 & 15.42 & 23.32 & 27.86 & 16.24 & 28.16 & 32.70 & 13.82 \\ \hline \hline \end{tabular} Note: “S” denotes the “Specifications of apple trays”, “No.” denotes the “Grasping point number”, “T-F” indicates the “Time of the improved genetic algorithm fused with optimal parameter selection algorithm”, “T-T” indicates the “Time of the traditional genetic algorithm”, and “O-A” indicates the “Optimization amplitude”.  
\end{table}
Table 2: Results under different specifications of trays and different numbers of grasping points.  
algorithm based on two-level chromosome coding and optimal parameter selection was proposed. Based on the test results and analysis, the following conclusions can be drawn:  
1. The improved genetic algorithm fused with the optimal parameter selection algorithm can effectively avoid the premature convergence of the traditional genetic algorithm.
2. The improved genetic algorithm fused with the optimal parameter selection algorithm provides an effective automatic packing method in the apple packing industry by reducing the packing time and improving the efficiency.
3. The two-level chromosome coding method used in the improved genetic algorithm can solve the packing path optimization problem of the two-posture manipulator.
4. The optimal parameter-fitting exhaustive algorithm can automatically determine the optimal parameter value of the proved genetic algorithm.  
Moreover, the design of the gripper device, and the calculation and analysis of the gripping force, will be carried out in further work. The mechanism of the influence of the size and weight of apples on the motion of the manipulator also needs further study.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background
### FedAvg Algorithm
```
1:Server executes:
2:Initialize \(w_{0}\)
3:for each round \(t=1,2,\dots\)do
4:\(m\leftarrow\max(C\cdot K,1)\)
5:\(S_{t}\leftarrow\) (random set of \(m\) clients)
6:for each client \(k\in S_{t}\)in paralleldo
7:\(w_{t+1}^{k}\leftarrow\textsc{ClientUpdate}(k,w_{t})\)
8:endfor
9:\(m_{t}\leftarrow\sum_{k\in S_{t}}n_{k}\)
10:\(w_{t+1}\leftarrow\sum_{k\in S_{t}}\frac{n_{k}}{m_{t}}w_{t+1}^{k}\)
11:endfor
12:ClientUpdate(\(k,w\)): {Run on client \(k\)}
13:\(\mathcal{B}\leftarrow\) (split \(\mathcal{P}_{k}\) into batches of size \(B\))
14:for each local epoch \(i\) from \(1\) to \(E\)do
15:for each batch \(b\in\mathcal{B}\)do
16:\(w\gets w-\eta\
abla\ell(w;b)\)
17:endfor
18:endfor
19:return\(w\) to server
```  
**Algorithm 1** Federated Averaging (FedAvg)  
The FedAvg algorithm enhances model performance and communication efficiency compared to FedSGD by allowing clients to perform multiple updates before aggregation. If \(B=\infty\), the local dataset is treated as a single mini-batch, and if \(B=\infty\) and \(E=1\), FedAvg corresponds exactly to FedSGD. Additionally, for a client with \(n_{k}\) local examples, the number of local updates per round is given by \(u_{k}=\frac{n_{k}}{B}\)[2]. Over time, variations in FedAvg have been developed to address specific cases and optimize performance under various conditions. Personalized variations in FedAvg, such as Personalized Federated Averaging (Personalized FedAvg), have been developed to tailor models to the specific requirements of each user. This method leverages meta-learning techniques to adapt the models to the local data characteristics of each client, thereby offering improved performance in personalized use cases [29]. Another significant variation is FedProx, which introduces an additional regularization term in the local cost functions to limit the deviation of local models from the global model, thus addressing the issue of model divergence due to the heterogeneous distribution of data among clients [30].
# A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks
## 4 Modeling Network Attack Using Attack Graph
### Node Minimal Effort  
Attack cost is the expense for an attacker to achieve an attack goal. We use attack cost in attack graphs to quantify the security level of the network. Namely, when the attack cost is higher, the network is more secure and _vice versa_. Normally, there are several actions in one attack behavior. We define action cost to designate the expense for an attacker to perform an action. To evaluate the attack cost of an attack path, we define NME for the nodes in the attack graph:  
_Definition 2: In an attack graph, NME is the minimal attack cost to reach a node._  
There are two types of nodes in our attack graph definition, namely state node and action node. In general, the minimal effort of a state node is the attack cost of the attack path that leads to the state node. If there are several attack paths that leads to the state node, the minimal effort of the state is minimal attack cost in the attack paths. Because of the AND relation, the minimal effort of an action node equal all the NMEs of its prerequisite nodes plus the action cost of itself.  
Let _min_effort_ denotes NME, _min_effort_ calculated as follows:  
* for state node \(s\in\ S\), \[\begin{array}{l}\text{min\_effort}(s)=0,\text{ if is the initial state;}\\ \text{min\_effort}(s)=\text{min\_k}(\text{min\_effort}(a_{k}))\end{array}\] (1)  
where \(a_{k}\) denotes a instance of the prerequisite nodes of \(s\).  
for action node \(a\in A\),  
\[\text{min\_effort}(a)=\sum_{k}\text{min\_effort}(s_{k})+\text{effort}(a) \tag{2}\]  
where \(s_{k}\) denoted a instance of the prerequisite nodes of \(a\), and effort\((a)\) denotes the attack cost of \(a\).
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 2 Materials and Methods
### Transcriptome Sequencing and Analysis  
WT EDL933 was exposed to pH 2.5 LB supplemented with or without 100 \(\upmu\)M 3-oxo-C6-AHL for 90 min at 37 \({}^{\circ}\)C (three biological replicates per condition). Cells were immediately stabilized in RNA-protect Bacterial Reagent (Qiagen, #76506, Hilden, Germany) and total RNA was extracted using TRIzol1R Reagent (Invitrogen1M, Thermo Fisher Scientific, Carlsbad, CA, USA) with DNase I treatment (Takara, #2270A, Dalian, China). Ribosomal RNA was depleted using the Ribo-Zero1M Gold rRNA Removal Kit (Illumina, #MRZG12324, San Diego, CA, USA). Strand-specific RNA-seq libraries were prepared with the NEBNext(r) Ultra1M II Directional RNA Library Prep Kit (NEB, #E7760L, Ipswich, MA, USA) and sequenced (150 bp paired-end) on an Illumina HiSeq 2000 platform (Novogene, Beijing, China), as previously described [(25)]. HISAT2 (v2.2.1) aligned reads to the EHEC EDL933 reference genome (NCBI RefSeq NC_002655.2). Differential gene expression analysis was performed using DESeq2 (v1.30.1) with thresholds of \(\mid\log_{2}(\)fold change\()\mid>1\) and a Benjamini-Hochberg adjusted \(p\)-value (FDR) \(<0.01\).
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Evaluation
#### 6.4.1 Test Procedure  
First of all, users must determine what portion of data they will use for training and testing. In stream classification, it is assumed that the instances appear in fixed sequential intervals and that the corresponding ground truth is directly available. However, standard techniques of the offline classification setting, e.g., cross-validation, do not always meet the requirements of the online setting since they would destroy the temporal order of data [4]. Thus, problem-tailored methods for the streaming scenario evolved.  
In many works, e.g., by [4; 7; 36; 84; 88; 169] three different validation methods are discussed: holdout, prequential evaluation (also named test-then-train), and controlled permutations. Within the holdout strategy, one data subset is selected for training, the rest for testing. In the stream setting, the latter subset should represent the exact distribution currently present in the data. Thus, it can only be applied in artificial settings when the user can define (a) the occurrence of concept drifts and (b) the exact training and testing subsets in advance [88].  
Prequential schemata apply an interleaved test-then-train approach: each data record is used to test model performance and afterward to (re-)train the algorithm. Thus, this method maximizes the data's usability. In contrast to holdout, the concept of drifts does not have to be known in advance. However, it may produce biased results, since the data stream is first used for testing and then for training. Controlled permutations overcome this issue. This approach runs multiple test runs with randomized portions of the data stream. Then, the portions are permuted in a sophisticated controlled way to preserve the local data distribution. More precisely, observations initially close concerning time remain close after a permutation. Accordingly, users who expect sudden concept drifts are recommended to use this method [7].  
Refs. [83; 170], criticize that, in all of these approaches, immediate availability of the labels is assumed. However, in real-world settings, a verification latency may occur. Ref. [170] discusses the example of the prediction of flight delays. A plane's delay is calculated after its departure, i.e., the actual delay is available hours after the model predicted the possible delay. This time can be divided into bins until the ground truth is available. For each bin, for every observation, a prediction is made. Then, for each sub-period of time, a performance metric is calculated. Thus, the change in the performance metric over time can be analyzed. Finally, when the current data instance's accurate label arrives, each bin's performance metric can be compared to the final label, and an overall model evaluation takes place.
# A New Road Damage Detection Baseline with Attention Learning
## 1 Introduction
The size of the collected image is large, but the proportion of damage is small in the image. The apparent distribution of the damage is sparse, and the boundary is not obvious. If we directly use any series of object detection algorithms, such as Faster RCNN [11; 12] or YOLO [13; 14; 15; 16; 17], it is very easy to have false detection and missed detection [18]. To solve this problem, this paper proposes novel road damage detection with attention learning based on existing detection algorithms. It consists of an attention fusion module, a salient feature learning module and fine-tuned training. The attention fusion module introduces simple and fast candy edge detection to provide salient cues and then guides damage detection by attention learning, which can effectively alleviate the sparse distribution and unclear boundaries of road damage. The salient feature learning module explicitly leverages attention normalization to alleviate the interference of approximate appearance on damage detection. Fine-tuning adopts a strategy from coarse to fine. That is, it first uses coarse labels without severity for training to obtain a pre-training model. Then, we fine-tune the proposed framework with the relatively strict categories of labels. This fine-tuning can enhance the network's perception of different damage and thus reduce the impact of some types of damage with similar appearance on classification. We evaluate the proposed baseline on the popular RDD2020 dataset [3] and the self-collected CNRDD dataset. It was found that the CNRDD dataset is more challenging than the RDD2020 dataset, and cracks are easier to distinguish than other damage. Further, the proposed baseline has a significantly improved F1-Score and mAP. Although its accuracy does not reach the level of physical object detection, this model at least provides a feasible solution for supervised ADRD. In summary, this paper's contributions are threefold:  
1. Release the first China road damage detection dataset, which contains eight types of damage with three degrees of severity. It is named CNRDD and comprises 21,552 annotations from 4319 images, which can effectively promote the development of ADRD. 2. Design an attention-learning-based road damage detection framework, which can explicitly utilize attention fusion and attention normalization to enhance the learning of salient damage features. 3. On RDD2020 and CNRDD datasets, the proposed baseline achieved state-of-the-art performance against existing methods without embedded learning. Based on the experiments, this work provides meaningful recommendations for future ADRD research.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.2 Federated and Decentralized Learning [240; 241; 242]  
Implement federated RL or distributed ML frameworks that preserve data privacy while enabling scalable IMI without centralized coordination.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Technique** & **Complexity** & **Scalability** & **Feasibility** & **Key Challenges** \\ \hline \hline POC & Low & High & High & Static thresholds, limited adaptability \\ \hline eICIC & Low–Medium & Medium & High & ABS inefficiency in non-macro–pico setups \\ \hline CoMP & High & Low–Medium & Medium & High backhaul, CO \\ \hline BF & Medium–High & Medium & Medium & CSI dependency, computation cost \\ \hline AI-based (RL/FL) & High & High (with edge AI deployment) & Medium & Training time, inference latency, model trust \\ \hline \hline \end{tabular}
\end{table}
Table 11: Trade-off evaluation of IMI techniques in B5G networks.
# Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning
## 2 Problem Formulation
### DRL-Based Algorithm Online Learning Application  
In Section 3.2, the DRL-based algorithm is proposed, however, it is an offline learning algorithm which can only be applied in the simulation environment. More importantly, the training process can only be applied in limited driving cycles, therefore, the trained DQN only performs well under the learned driving conditions, which may not provide satisfactory results under other driving cycles. This is unacceptable in HEV real-time applications. As a result, online learning is necessary for DRL-based algorithms in HEV EMS applications.  
The DRL-based online learning architecture is presented in Figure 4. Action execution and network training should be separated. There is a controller which contains a \(Q\) neural network and selects an action for the HEV while storing the state action transitions. When the HEV needs to learn a new driving cycle, the method of action selection will be the \(\varepsilon-greedy\) method. Otherwise, the HEV can always select the maximum Q-value action. There is another on-board computer or remote computing center which is responsible for \(Q\) neural network training. The on-board computer or remote computing center obtains state action transitions from the action controller and trains the neural network based on the DRL algorithm. The \(Q\) neural network is periodically updated by copying parameters from the on-board computer or remote computing center.  
The communication mode between the action controller and the on-board computer can be via CAN bus or Ethernet. If the \(Q\) neural network training is completed by a remote computing center, a vehicle terminal named Telematics-box (T-box) should be installed in the HEV in order to communicate with the remote computing center through the 3G communication network. A remote computing center can obtain state action transitions from other connected HEVs, as is shown in Figure 4. This is useful to train a large \(Q\) neural network which can deal with different driving conditions.  
The main differences between online learning and offline learning are as follows: (1) online learning can adapt to varying driving conditions, while offline can only learn from the given driving cycles; (2) action execution and network training should be separated in online learning because of the limited on-board controller computing ability; and (3) online training efficiency should be higher than offline training since the vehicle must learn the optimal EMS with the shortest time. Thus, it is necessary to cluster the representative state action transitions and use the recent data in the experience pool.  
Interestingly, offline learning and online learning can be combined to realize a good effect of EMS. For instance, we can train the DQN offline under the Urban Dynamometer Driving Schedule (UDDS), and then apply the online learning under the New European Driving Cycle (NEDC).